Cognitive Cardiac Rehabilitation Using IoT and AI Tools [Team-IRA] 1668475618, 9781668475614

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Cognitive Cardiac Rehabilitation Using IoT and AI Tools Parijat Bhowmick Indian Institute of Technology, Guwahati, India Sima Das Camellia Institute of Technology and Management, India Kaushik Mazumdar Indian Institute of Technology, Dhanbad, India

A volume in the Advances in Medical Diagnosis, Treatment, and Care (AMDTC) Book Series

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Library of Congress Cataloging-in-Publication Data Names: Bhowmick, Parijat, 1986- editor. | Das, Sima, 1992- editor. | Mazumdar, Kaushik, 1983- editor. Title: Cognitive cardiac rehabilitation using IoT and AI tools / edited by Parijat Bhowmick, Sima Das, Kaushik Mazumdar. Description: Hershey, PA : Medical Information Science Reference, [2023] | Includes bibliographical references and index. | Summary: “Cognitive & Cardiac Rehabilitation is a field of study that focuses on the impact on the human mind, heart, behavior, conscious and unconscious motivation, feelings, thoughts, attitudes, emotions, and more. The contribution of the proposed approach can be summarized as follows i. Application of artificial intelligence technique used to extract features from brain and heart signal data and better efficiency compared to the existing methods. ii. Application of artificial intelligence tools to classify disorders and better performance compared to the existing benchmarks. iii. The aim of towards to describe the motivational factors affecting the patients’ participation and compliance to cardiac rehabilitation by recognizing and understanding the nature of patients’ experiences. iv. Cognitive impairment may influence patient self-management by reducing medication adherence, rendering patients unable to make lifestyle modifications and causing missed healthcare visits”-- Provided by publisher. Identifiers: LCCN 2023008828 (print) | LCCN 2023008829 (ebook) | ISBN 9781668475614 (hardcover) | ISBN 9781668475621 (ebook) Subjects: MESH: Cardiac Rehabilitation | Cognitive Training | Patient Acceptance of Health Care | Medical Informatics Applications Classification: LCC RC682 (print) | LCC RC682 (ebook) | NLM WG 180 | DDC 616.1/203--dc23/eng/20230412 LC record available at https://lccn.loc.gov/2023008828 LC ebook record available at https://lccn.loc.gov/2023008829 This book is published in the IGI Global book series Advances in Medical Diagnosis, Treatment, and Care (AMDTC) (ISSN: 2475-6628; eISSN: 2475-6636) British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: [email protected].

Advances in Medical Diagnosis, Treatment, and Care (AMDTC) Book Series ISSN:2475-6628 EISSN:2475-6636

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Cases on Multimorbidity and Its Impact on Elderly Patients Adil Hamad Alharthi (King Fahad Armed Forces Hospital, Saudi Arabia) Medical Information Science Reference • ©2023 • 285pp • H/C (ISBN: 9781668423547) • US $325.00 Global Perspectives on Health Assessments for an Aging Population Luis Vieira Ribeiro (School of Health, University of Algarve, Portugal) and Anabela Magalhães Ribeiro (School of Health, University of Algarve, Portugal) Medical Information Science Reference • ©2023 • 370pp • H/C (ISBN: 9781668476307) • US $360.00 Cannabis sativa Cultivation, Production, and Applications in Pharmaceuticals and Cosmetics Rafiq Lone (Central University of Kashmir, India) Aabid Hussain Mir (University of Kashmir, India) and Javid Manzoor (Jiwaji University, India) Engineering Science Reference • ©2023 • 227pp • H/C (ISBN: 9781668457184) • US $250.00 Medical Cannabis and the Effects of Cannabinoids on Fighting Cancer, Multiple Sclerosis, Epilepsy, Parkinson’s, and Other Neurodegenerative Diseases Rana R. Zeine (Kean University, USA) and Brian W. Teasdale (Kean University, USA) Medical Information Science Reference • ©2023 • 405pp • H/C (ISBN: 9781668456521) • US $345.00 Perspectives on Coping Strategies for Menstrual and Premenstrual Distress B.S. Parimal (The Maharaja Sayajirao University of Baroda, India) and Kavita Gupta (The Maharaja Sayajirao University of Baroda, India)

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Table of Contents

Foreword ............................................................................................................. xv Preface ............................................................................................................... xvii Acknowledgment ........................................................................................... xxxvi Chapter 1 Cognitive Cardiac Rehabilitation Using IoT and AI Tools ....................................1 Parijat Bhowmick, Indian Institute of Technology, Guwahati, India Sima Das, Camellia Institute of Technology and Management, India Chapter 2 Survey on Cognitive Rehabilitation .......................................................................5 Sima Das, Camellia Institute of Technology and Management, India Chapter 3 A Comprehensive Survey on Rehabilitative Applications of Electroencephalogram in Healthcare ...................................................................13 Pritam Chatterjee, Maulana Abul Kalam Azad University of Technology, West Bengal, India Ahona Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Sriparna Saha, Maulana Abul Kalam Azad University of Technology, West Bengal, India Chapter 4 Mental Health Predictions Through Online Social Media Analytics ..................44 Moumita Chatterjee, Aliah University, India Subrata Modak, Maryland Institute of Technology and Management, Jamshedpur, India Dhrubasish Sarkar, Supreme Institute of Management and Technology, India

Chapter 5 Emotion Detection Using EEG-Based Brain-Computer Interaction ...................67 Sima Das, Camellia Institute of Technology and Management, India Kitmo, National Advanced School of Engineering of Marou, Cameroon Nimay Chandra Giri, Centurion University of Technology and Management, India Chapter 6 Machine Learning-Based Approaches in the Detection of Depression: A Comparative Study ...............................................................................................77 Dipanwita Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Mihir Sing, Maulana Abul Kalam Azad University of Technology, West Bengal, India Arpan Adhikary, Haldia Institute of Technology, India Chapter 7 Machine Learning-Based Approaches in the Detection of Suicide From Social Media Comments ......................................................................................91 Dipanwita Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Mihir Sing, Maulana Abul Kalam Azad University of Technology, West Bengal, India Arpan Adhikary, Haldia Institute of Technology, India Asit Kumar Nayek, Haldia Institute of Technology, India Chapter 8 Significant Approaches and Applications of Virtual Reality in the Treatment of Depression .....................................................................................................105 Arpan Adhikary, Haldia Institute of Technology, India Dipanwita Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Asit Kumar Nayek, Haldia Institute of Technology, India Chapter 9 Survey on Cardiac Rehabilitation ......................................................................113 Rabindranath Sahu, Supreme Knowledge Foundation Group of Institutions, India

Chapter 10 Clinical and Diagnostic Presentation on Myocardial Biology ...........................122 Susmita Chakrabarty, Centurion University of Technology and Management, India Monali Priyadarsini Mishra, Centurion University of Technology and Management, India Nimay Chandra Giri, Centurion University of Technology and Management, India Chapter 11 Predictive Analysis of Heart Disease Using Machine Learning Algorithms ....151 Subrata Modak, Maryland Institute of Technology and Management, Jamshedpur, India Rajesh Kumar Ghosh, Siksha O Anusandhan University, India Dhrubasish Sarkar, Supreme Knowledge Foundation, India Chapter 12 Application of Machine Learning Algorithms for Automatic Detection of Risk in Heart Disease .........................................................................................166 Anudeepa Gon, Assistant Professor, Computational Sciences, Brainware University (UGC approved), Barasat, India Sudipta Hazra, Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India Siddhartha Chatterjee, Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India Anup Kumar Ghosh, Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India Chapter 13 Cognitive Cardiac Rehabilitation Using IoT and AI Tools: Conclusion ...........189 Kaushik Mazumdar, Indian Institute of Technology (ISM) Dhanbad, India Sima Das, Camellia Institute of Technology and Management, India

Compilation of References .............................................................................. 192 Recommended Readings.................................................................................. 216 About the Contributors ................................................................................... 249 Index .................................................................................................................. 253

Detailed Table of Contents

Foreword ............................................................................................................. xv Preface ............................................................................................................... xvii Acknowledgment ........................................................................................... xxxvi Chapter 1 Cognitive Cardiac Rehabilitation Using IoT and AI Tools ....................................1 Parijat Bhowmick, Indian Institute of Technology, Guwahati, India Sima Das, Camellia Institute of Technology and Management, India Cognitive cardiac rehabilitation is an essential component of cardiac care, which aims to improve the mental and emotional well-being of patients with cardiovascular disease. Traditional rehabilitation methods involve in-person sessions with healthcare professionals, which may be limited by geographic distance, scheduling conflicts, and costs. However, with recent advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) technologies, it is now possible to provide cardiac rehabilitation Chapter 2 Survey on Cognitive Rehabilitation .......................................................................5 Sima Das, Camellia Institute of Technology and Management, India Cognitive rehabilitation refers to a range of interventions designed to improve or restore cognitive functioning in individuals who have suffered from neurological injury or disease. This type of rehabilitation typically involves a combination of cognitive and behavioral strategies aimed at enhancing specific cognitive abilities such as attention, memory, language, and problem-solving skills. The process of cognitive rehabilitation begins with a comprehensive assessment of the individual’s cognitive abilities, followed by the development of an individualized treatment plan.

Chapter 3 A Comprehensive Survey on Rehabilitative Applications of Electroencephalogram in Healthcare ...................................................................13 Pritam Chatterjee, Maulana Abul Kalam Azad University of Technology, West Bengal, India Ahona Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Sriparna Saha, Maulana Abul Kalam Azad University of Technology, West Bengal, India A set of therapeutic control required for persons suffering from or expected to suffer from limitations in daily living activities is called rehabilitation which can restore or improve the functional ability in a stipulated time. These abilities can be from the physical aspect, can be from mental aspect and can be from cognitive perspective also. During human interaction, sensation or emotion plays a major role. To collect this sensation, the most important work is to implement an emotion collection system recording the signal accurately. The present chapter discusses all the implemented methodologies and the corresponding applications. The first one is concerned with a set of recommendations to overcome the shortcomings of the existing works and the second one is about a detailed analysis of the steps to be performed for achieving proper rehabilitative aid for future research in this concerned area. Chapter 4 Mental Health Predictions Through Online Social Media Analytics ..................44 Moumita Chatterjee, Aliah University, India Subrata Modak, Maryland Institute of Technology and Management, Jamshedpur, India Dhrubasish Sarkar, Supreme Institute of Management and Technology, India According to WHO data, mental health is a major source of concern throughout the world, amounting to suicide in the majority of instances if left untreated. Presently, social media is a great way for people to express themselves through text, emoticons, images, or videos that depict their sentiments and emotions. This has opened up the possibility of investigating social networks in order to better comprehend their users’ mental states. Social media is currently being used by researchers to predict the prevalence of mental illnesses such as depression, suicide ideation, anxiety, and stress. This area of study has considerable potential for the monitoring, diagnosis, and prevention of mental health issues. In this research we aim to give an overview of the most recent works for predicting mental health status on social media. We focused on data collection and annotation approaches, preprocessing and feature selection, model selection, and validation. We addressed research on depression and suicidal thoughts, which are the most common among mental health studies on social media.

Chapter 5 Emotion Detection Using EEG-Based Brain-Computer Interaction ...................67 Sima Das, Camellia Institute of Technology and Management, India Kitmo, National Advanced School of Engineering of Marou, Cameroon Nimay Chandra Giri, Centurion University of Technology and Management, India The study of emotional behavior has always been a challenging area of research. While traditional methods have relied on self-reported measures, recent advancements in technology have enabled researchers to investigate emotional behavior through the analysis of brain activity, specifically using electroencephalography (EEG). In this study, we aim to investigate the relationship between EEG signals and emotional behavior. The experiment involved 20 participants who were presented with a series of emotionally charged images while their EEG signals were recorded. Chapter 6 Machine Learning-Based Approaches in the Detection of Depression: A Comparative Study ...............................................................................................77 Dipanwita Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Mihir Sing, Maulana Abul Kalam Azad University of Technology, West Bengal, India Arpan Adhikary, Haldia Institute of Technology, India In the last few decades, psychological health issues, i.e., anxiety, stress, and depression, have become common problems worldwide. Most of those with these issues feel down, sad, and tragic from time to time. Some people experience this problem very strongly for a long period of time without any evident reason. Generally, depression is not a low mood—it is the condition that affects the physical and mental health of a person. Some work has been done by researchers in the detection of depression using machine learning (ML) classifiers. In this chapter, a detailed survey and review of the different works in this area has been carried out. The authors also identified certain drawbacks of these works. Based on which, the authors tried to explore the future scope of works, which will enhance the performance of the existing models in the detection of depression.

Chapter 7 Machine Learning-Based Approaches in the Detection of Suicide From Social Media Comments ......................................................................................91 Dipanwita Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Mihir Sing, Maulana Abul Kalam Azad University of Technology, West Bengal, India Arpan Adhikary, Haldia Institute of Technology, India Asit Kumar Nayek, Haldia Institute of Technology, India Suicidal tendencies have increased today due to nuclear organization of families and rapid urbanization around the world. Loneliness, aggression, and fast-moving daily lives make the youths and the aged persons depressed. Most of the time, they are involved in mutual relationships on social media. Social media posts and chats, thus, become an important resource from where we can find one’s mental illness level and suicidal tendances. The most-used keywords are taken from an open database and are analyzed. ML algorithms like random forest, support vector classifier, and KNN are used to train and predict a person’s suicide attempt. Out of these algorithms, SVC produces greater accuracy. To generate more accuracy, word sets shall be robust. Chapter 8 Significant Approaches and Applications of Virtual Reality in the Treatment of Depression .....................................................................................................105 Arpan Adhikary, Haldia Institute of Technology, India Dipanwita Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India Asit Kumar Nayek, Haldia Institute of Technology, India Virtual reality (VR) is one of the versatile technologies which extends reality into the virtual domain. It offers an enormous virtual environment that is partially or completely independent from the real world. The objective of VR is to create an environment that will simulate or build the digital forms of the real world. For this feature of simulation, VR technology is being rapidly adopted in the healthcare industry for different purposes, i.e., training, treatment, disease awareness, etc., where many of its applications are in the testing phase. Depression is one of the psychological health issues that has become a very common problem worldwide. Different methods like antidepressants and psychotherapy can be used to treat this problem. Some VR technologies have already been adopted for the treatment of depression. In this chapter, the authors are going to discuss the significant applications and approaches of VR towards depression.

Chapter 9 Survey on Cardiac Rehabilitation ......................................................................113 Rabindranath Sahu, Supreme Knowledge Foundation Group of Institutions, India Cardiac rehabilitation increases quality of life, cardiovascular fitness, and improves cardiac event risk. Despite cardiac rehabilitation’s advantages, many eligible people don’t enroll. Increased cardiac rehabilitation involvement demands greater access. Thorough cardiac rehabilitation helps cardiovascular disease patients. Cardiovascular disease counselling, exercise training, and healthy lifestyle instruction are typical. Personalize aerobic, resistance, and flexibility activities. Healthy living instruction includes food, smoking cessation, and stress management. Counseling may help with anxiety, depression, and other cardiovascular disease-related feelings. Chapter 10 Clinical and Diagnostic Presentation on Myocardial Biology ...........................122 Susmita Chakrabarty, Centurion University of Technology and Management, India Monali Priyadarsini Mishra, Centurion University of Technology and Management, India Nimay Chandra Giri, Centurion University of Technology and Management, India Myocardial infarction (MI) is known as a critical medical emergency that necessitates urgent medical intervention due to its life-threatening nature. Traditional treatment methods, such as medication, surgery, and cardiac rehabilitation, have significantly improved patient outcomes. Understanding the intricate biology of the myocardium is crucial for accurately diagnosing and effectively managing various cardiac conditions. Here the abstract highlights the key aspects of clinical and diagnostic presentation pertaining to myocardial biology. However, emerging technologies like the Internet of Things (IoT) present novel opportunities to enhance diagnosis and treatment approaches for MI. This research proposal aims to investigate the potential benefits of IoT-based diagnosis & treatment for myocardial infection and its impact on patient outcomes. Additionally, it explores the clinical manifestations and diagnostic approaches employed to assess myocardial function and identify pathological conditions.

Chapter 11 Predictive Analysis of Heart Disease Using Machine Learning Algorithms ....151 Subrata Modak, Maryland Institute of Technology and Management, Jamshedpur, India Rajesh Kumar Ghosh, Siksha O Anusandhan University, India Dhrubasish Sarkar, Supreme Knowledge Foundation, India Early prediction of cardiac arrest is one of the most challenging jobs in medical science. It takes a long time to figure out the exact causes behind it. Approximately one human dies per minute because of heart disease. Data analysis plays a vital part in handling huge amounts of healthcare data. In order to decrease the number of deaths from heart diseases, efficient detection techniques have to be used. This chapter describes the chances of heart disease and classifies a patient’s risk level by implementing different classification algorithms such as K-Nearest neighbour, support vector machine, Naïve Bayes, random forest and a multilayer perception. Artificial neural network optimized by particle swarm optimization (PSO) are combined with ant colony optimization (ACO) approaches. Thus, this chapter presents a comparative study by analysing the performance of different machine learning algorithms. The trial results verify that random forest algorithm has achieved the highest accuracy of 90.16% compared to other ML algorithms implemented. Chapter 12 Application of Machine Learning Algorithms for Automatic Detection of Risk in Heart Disease .........................................................................................166 Anudeepa Gon, Assistant Professor, Computational Sciences, Brainware University (UGC approved), Barasat, India Sudipta Hazra, Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India Siddhartha Chatterjee, Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India Anup Kumar Ghosh, Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India The main cause of death worldwide is heart disease, emphasizing the need for accurate risk prediction models to recognise those who are at high risk. In this study, we propose an automated approach using artificial intelligence to forecast the likelihood of developing heart disease. The dataset consists of various clinical and demographic features, including blood pressure, cholesterol levels, age, gender, and exercise habits. We evaluate the performance of numerous machine learning algorithms, such as neural networks, logistic regression, support vector machines, and random forests, in predicting the likelihood of heart disease. Our results demonstrate that automated learning techniques can effectively determine the likelihood of cardiac disease with high accuracy, precision, and recall. Furthermore, we conduct

analysis feature importance to the risk prediction model can determine which factors have the greatest impact. The automated risk prediction system can provide early detection and intervention strategies for individuals at high risk, enabling proactive healthcare management and reducing the burden of heart disease. This research showcases the potential of machine learning algorithms in improving heart disease risk assessment and guiding personalized preventive measures. Machine learning which is employed in worldwide in different industries. In the healthcare industry, there are no exceptions. Determining whether or not there will be heart problems, abnormalities and other disorders can be highly dependent on machine learning. For the purpose of predicting probable heart conditions in humans, we are creating machine learning algorithms. In our work, We contrast the effectiveness of several classifiers, which includs Naive Bayes, Decision Tree, Logistic Regression, Random Forest, SVM. Finally, we evaluate the effectiveness of the suggested classifiers, including the more accurate Ada-boost and XG-boost. Early detection of heart disease in high-risk persons is essential for assisting them in deciding whether to alter their lifestyle, which reduces consequences. Medical data’s hidden patterns may be exploited to diagnose health issues. Chapter 13 Cognitive Cardiac Rehabilitation Using IoT and AI Tools: Conclusion ...........189 Kaushik Mazumdar, Indian Institute of Technology (ISM) Dhanbad, India Sima Das, Camellia Institute of Technology and Management, India Cognitive cardiac rehabilitation using IoT and AI tools is a promising approach for improving the effectiveness and efficiency of cardiac rehabilitation programs. The use of IoT devices allows for the remote monitoring of patients’ vital signs and physical activity, while AI tools can analyze this data to provide personalized feedback and support. This approach has the potential to enhance patient engagement and adherence to rehabilitation protocols, leading to better outcomes for patients. While the benefits of cognitive cardiac rehabilitation using IoT and AI tools are clear, there are also some challenges that need to be addressed.

Compilation of References .............................................................................. 192 Recommended Readings.................................................................................. 216 About the Contributors ................................................................................... 249 Index .................................................................................................................. 253

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Foreword

In the rapidly advancing field of healthcare, the integration of human-focused robot technologies has become a subject of great interest and importance. With a particular focus on social-emotional intelligence, researchers strive to create connections between these technologies and human lifestyles. Among the various sectors that stand to benefit from these developments, the healthcare sector holds a crucial position, offering numerous possibilities for therapeutic intervention. One area of significant concern within healthcare is patient adherence to cardiac rehabilitation. To achieve successful outcomes, it is essential to understand the motivational factors that influence patient participation and compliance with rehabilitation programs. By recognizing and comprehending the nature of patient experiences, we can devise more effective strategies to enhance patient engagement and overall well-being. In this context, the book Cognitive Cardiac Rehabilitation Using IoT and AI Tools presents an invaluable resource that explores the application of AI techniques in the field of cardiac rehabilitation. The book delves into the extraction of features from brain and heart signal data using AI tools, surpassing the efficacy of existing methods. Furthermore, it discusses the implementation of AI algorithms to classify disorders, showcasing superior performance compared to existing benchmarks. The book also covers topics such as heart disease prediction, online social media analytics, and the intersection of mental health predictions with myocardial biology. The multidisciplinary approach embraced by this publication is commendable, bringing together healthcare professionals, medical engineers, hospital administrators, students, educators, researchers, and academicians. By doing so, it fosters a collaborative environment that encourages the exchange of ideas, expertise, and innovative approaches. This collective effort is crucial in propelling the healthcare industry forward and ensuring the delivery of the highest quality care to patients. The integration of artificial intelligence, the Internet of Things (IoT), and machine learning algorithms offers immense potential to revolutionize the healthcare landscape. By harnessing the power of these technologies, healthcare professionals can gain deeper insights into patient behavior, improve diagnostics and treatment strategies, and enhance overall healthcare outcomes. The book at hand provides a

Foreword

comprehensive exploration of these technologies and their applications, offering a valuable guide for those seeking to leverage AI tools and IoT advancements in the context of cardiac rehabilitation and related healthcare domains. Kitmo National Advanced School of Engineering of Marou, Cameroon

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Preface

OVERVIEW OF THE SUBJECT MATTER Advancements in technology have revolutionized various aspects of our lives, and the healthcare sector is no exception. The integration of artificial intelligence (AI) and the Internet of Things (IoT) has paved the way for significant developments in human-focused robot technologies, particularly in the field of social-emotional intelligence. This book, Cognitive Cardiac Rehabilitation Using IoT and AI Tools, explores the potential of these technologies to improve patient adherence and compliance with cardiac rehabilitation, a crucial aspect of healthcare. Cardiac rehabilitation plays a vital role in the recovery and overall well-being of individuals with heart conditions. However, ensuring patient participation and adherence to rehabilitation programs has been a persistent challenge. By understanding the motivational factors that influence patient engagement and compliance, we can design interventions that cater to the unique experiences and needs of each individual. This book presents a comprehensive overview of how AI techniques can be applied to extract valuable insights from brain and heart signal data. By leveraging machine learning algorithms, researchers and healthcare professionals can improve the efficiency of cardiac rehabilitation programs. Moreover, the book explores the use of AI tools in classifying cardiac disorders, surpassing existing benchmarks and enhancing diagnostic accuracy. The scope of this publication extends beyond cardiac rehabilitation, encompassing a wide range of topics relevant to healthcare professionals, medical engineers, hospital administrators, students, educators, researchers, and academicians. The chapters delve into areas such as heart disease prediction, online social media analytics, and mental health predictions. By integrating AI and IoT technologies, these areas can benefit from enhanced predictive capabilities, real-time monitoring, and personalized interventions. We believe that this book serves as a valuable resource for individuals interested in the intersection of healthcare, AI, and IoT. It offers insights into cutting-edge research, practical applications, and future directions in the field of cognitive

Preface

cardiac rehabilitation. By harnessing the power of technology, we can transform the healthcare landscape and improve patient outcomes. We would like to express our gratitude to the contributors of this publication for their dedication and expertise. Their contributions have made this book a comprehensive reference source, providing a wealth of knowledge and inspiration for further exploration. We hope that readers will find this book informative and inspiring, spurring further advancements in the field of human-focused robot technologies and the healthcare sector as a whole. This book delves into the intersection of healthcare, technology, and human cognition, exploring how these advancements revolutionize our approach to cardiac rehabilitation. Cardiac rehabilitation holds immense significance in the recovery journey of individuals who have experienced cardiac events or undergone cardiac procedures. It encompasses a holistic program of exercise, education, and lifestyle modification, aiming to enhance cardiovascular health, mitigate the risk of future cardiac events, and improve overall well-being. Traditionally, cardiac rehabilitation programs have centered around in-person sessions, involving healthcare professionals and patients engaging within clinical or hospital settings. However, we stand at an exhilarating juncture where rapid technological advancements empower us to leverage IoT and AI to augment and elevate cardiac rehabilitation. The amalgamation of these technologies facilitates the creation of intelligent algorithms and smart, interconnected devices capable of monitoring, analyzing, and tailoring rehabilitation programs to individual needs. This comprehensive book serves as a guide to comprehending the potential of cognitive cardiac rehabilitation through the utilization of IoT and AI tools. We delve into various facets of this emerging field, elucidating the fundamentals of IoT and AI, their application in cardiac rehabilitation, and the associated benefits and challenges. Furthermore, we explore compelling case studies and real-world examples that highlight the transformative impact of these technologies on patient outcomes and healthcare delivery. Whether you are a healthcare professional striving to stay abreast of the latest advancements in cardiac rehabilitation, a researcher fascinated by the application of IoT and AI in healthcare, or an individual who has experienced a cardiac event and seeks to explore the possibilities of cognitive cardiac rehabilitation, this book aims to provide invaluable insights and knowledge.

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Preface

A DESCRIPTION OF WHERE YOUR TOPIC FITS IN THE WORLD TODAY Cognitive Cardiac Rehabilitation (CCR) using IoT (Internet of Things) and AI (Artificial Intelligence) tools is a cutting-edge approach that integrates technology and healthcare to improve the rehabilitation process for individuals with cardiovascular conditions. In the world today, CCR using IoT and AI tools holds great potential and fits into several significant areas: 1.

2.

3.

4.

5.

Cardiac Rehabilitation Enhancement: Traditional cardiac rehabilitation programs focus primarily on physical exercise and lifestyle modifications. CCR using IoT and AI tools extends beyond these aspects by incorporating cognitive training, mental health support, and personalized care. By leveraging IoT devices and AI algorithms, patients can engage in interactive exercises, receive realtime feedback, and monitor their progress remotely. This integration improves patient engagement, adherence to treatment plans, and overall outcomes. Remote Patient Monitoring: IoT devices such as wearable sensors, connected scales, blood pressure monitors, and ECG monitors can collect real-time data on various vital signs and activity levels. These devices transmit the data to healthcare providers, allowing for remote monitoring and timely interventions. AI algorithms can analyze the data to detect anomalies or patterns indicative of potential cardiac issues. CCR using IoT and AI tools enables continuous monitoring and facilitates early detection of complications, leading to proactive and personalized interventions. Personalized Treatment Plans: AI algorithms can analyze a patient’s medical history, physiological data, and behavioral patterns to generate personalized treatment plans. These plans can adapt over time based on the patient’s progress and response to interventions. By tailoring rehabilitation programs to individual needs, CCR using IoT and AI tools optimizes treatment outcomes and enhances patient satisfaction. Behavioral Modification and Education: CCR using IoT and AI tools can provide educational resources, behavior change support, and cognitive training exercises to patients. AI algorithms can analyze patient responses and interactions, identify areas of improvement, and deliver personalized educational content. This approach promotes self-management, empowers patients to make informed decisions, and facilitates long-term behavior change, ultimately reducing the risk of future cardiac events. Data-Driven Research and Insights: The integration of IoT and AI in CCR generates vast amounts of data, which can be anonymized and aggregated for research purposes. Researchers can use this data to gain insights into xix

Preface

patient populations, treatment effectiveness, and risk prediction models. These findings can further contribute to the development of evidence-based practices, guidelines, and the improvement of healthcare policies related to cardiac rehabilitation. CCR using IoT and AI tools represents a significant advancement in cardiac rehabilitation, enabling a more holistic and patient-centric approach. It has the potential to improve patient outcomes, reduce healthcare costs, enhance accessibility to care, and advance our understanding of cardiovascular health. As technology continues to evolve, the application of CCR using IoT and AI tools will likely expand, creating new opportunities for personalized and efficient cardiac rehabilitation worldwide.

TARGET AUDIENCE Our aspiration is for this book to inspire healthcare providers, researchers, technologists, and policymakers to embrace the potential of IoT and AI in transforming cardiac rehabilitation, ultimately enhancing the lives of individuals affected by cardiovascular diseases. Let us embark on this enlightening journey of cognitive cardiac rehabilitation together, forging a path towards a healthier and more interconnected future. The target audience for cognitive cardiac rehabilitation using IoT (Internet of Things) and AI (Artificial Intelligence) tools can include: Cardiac Patients: Individuals who have undergone cardiac procedures such as heart surgery, heart attack, or angioplasty, and need rehabilitation to regain their physical and cognitive abilities. Cognitive rehabilitation can help improve memory, attention, problem-solving, and decision-making skills, which are often affected after cardiac events. Healthcare Professionals: Physicians, cardiologists, nurses, and other healthcare providers who are involved in the care and management of cardiac patients. Cognitive cardiac rehabilitation tools can assist healthcare professionals in monitoring patient progress, providing personalized care plans, and making informed treatment decisions based on real-time data collected through IoT devices. Caregivers and Family Members: The family members and caregivers of cardiac patients play a crucial role in supporting their recovery. Cognitive cardiac rehabilitation tools can provide them with insights into the patient’s progress, educate them about post-cardiac care strategies, and empower them to assist the patient in their rehabilitation journey. Researchers and Scientists: Researchers and scientists in the field of cardiovascular health and rehabilitation can benefit from cognitive cardiac xx

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rehabilitation tools. These tools can provide valuable data for research purposes, allowing them to study the effectiveness of different interventions, develop new therapies, and improve existing rehabilitation protocols. Healthcare Organizations and Administrators: Healthcare organizations, hospitals, and rehabilitation centers can adopt cognitive cardiac rehabilitation tools to enhance their services and improve patient outcomes. These tools can assist in optimizing resource allocation, streamlining workflows, and ensuring efficient and effective delivery of cardiac rehabilitation programs. It is important to note that the specific target audience may vary based on the nature and scope of the cognitive cardiac rehabilitation program, the availability of IoT and AI tools, and the intended use case.

DESCRIPTION OF THE IMPORTANCE OF EACH OF THE CHAPTERS Chapter 1 This chapter is relevant to the topic of cognitive cardiac rehabilitation using IoT and AI tools because it provides an introduction to the subject matter. It highlights the potential benefits and challenges associated with integrating IoT and AI technologies into cardiac rehabilitation programs. The chapter sets the stage for further exploration and discussion of the topic by establishing the context, explaining the concept of cognitive cardiac rehabilitation, and outlining how IoT and AI tools can enhance the delivery of rehabilitation services. By introducing the concept of cognitive cardiac rehabilitation and its traditional methods, the chapter establishes the need for advancements in remote and precise rehabilitation techniques. It highlights the limitations of in-person sessions, such as geographic distance, scheduling conflicts, and costs, which can be addressed through the use of IoT and AI tools. The chapter explains how these technologies can enable continuous care and monitoring, personalized rehabilitation plans, and motivational support for patients, even outside of the hospital setting. Furthermore, the chapter outlines the potential benefits of cognitive cardiac rehabilitation using IoT and AI tools, such as better health outcomes, reduced risk of readmission, improved data collection for healthcare providers, and increased accessibility and convenience for patients. It also addresses the challenges associated with data privacy and security, as well as technological barriers that need to be overcome for effective implementation. Overall, this chapter provides a foundation for understanding the importance and potential impact of integrating IoT and AI tools into cognitive cardiac rehabilitation. It sets the stage for further exploration of the topic by presenting the key aspects, xxi

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benefits, and challenges involved, thereby motivating the need for deeper analysis and discussion in subsequent chapters.

Chapter 2 The topic of cognitive cardiac rehabilitation using IoT and AI tools is relevant to cognitive rehabilitation as it expands the application of cognitive rehabilitation techniques to individuals with cardiac conditions. Cognitive cardiac rehabilitation aims to improve cognitive function in individuals who have experienced cardiac events or have cardiac conditions. Integrating IoT (Internet of Things) and AI (Artificial Intelligence) tools into cognitive cardiac rehabilitation can enhance the effectiveness and efficiency of the rehabilitation process. Here’s how the topic is relevant: Remote Monitoring: IoT devices can be used to remotely monitor cardiac patients’ vital signs, physical activity levels, and sleep patterns. This data can provide valuable insights into the patient’s overall health and help identify potential cognitive deficits or changes in cognitive functioning. Personalized Interventions: AI algorithms can analyze the data collected from IoT devices to personalize cognitive rehabilitation interventions. By considering the individual’s specific cardiac condition, cognitive impairments, and daily activities, AI can generate personalized cognitive training exercises or activities tailored to the individual’s needs and goals. Adaptive Training: AI can dynamically adjust the difficulty level of cognitive training exercises based on real-time performance data. This adaptive training approach ensures that the exercises challenge the individual appropriately, promoting cognitive improvement while avoiding frustration or excessive difficulty. Behavioral Monitoring: IoT devices and AI algorithms can track behavioral patterns and provide feedback to individuals during their cognitive cardiac rehabilitation. This feedback can help individuals become more aware of their cognitive performance, make behavioral adjustments, and reinforce positive changes. Telemedicine and Virtual Reality: IoT and AI technologies can enable telemedicine consultations and virtual reality-based cognitive rehabilitation sessions. This allows individuals to access cognitive rehabilitation programs remotely, reducing the need for in-person visits and increasing accessibility, particularly for individuals who have difficulty traveling or accessing rehabilitation centers. Data Analysis and Research: The use of IoT and AI tools in cognitive cardiac rehabilitation can generate large amounts of data. This data can be analyzed to gain insights into the effectiveness of specific interventions, identify patterns, and refine rehabilitation approaches. It can also contribute to research efforts aimed at further improving cognitive rehabilitation techniques for cardiac patients.

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By incorporating IoT and AI tools into cognitive cardiac rehabilitation, healthcare providers can enhance the assessment, intervention, and monitoring processes. This integrated approach has the potential to improve cognitive functioning, quality of life, and overall outcomes for individuals with cardiac conditions. However, it’s important to note that the implementation of IoT and AI tools in healthcare settings must consider privacy, security, and ethical considerations to protect patient data and ensure responsible use of technology.

Chapter 3 The topic of “Rehabilitative Applications of Electroencephalogram (EEG) in Healthcare” is highly relevant to the book. Cognitive cardiac rehabilitation aims to enhance the cognitive functioning and overall well-being of individuals who have experienced cardiac events or undergone cardiac procedures. EEG is a noninvasive technique that measures the electrical activity of the brain. It provides valuable information about the brain’s functional status, cognitive processes, and emotional responses. In the context of cognitive cardiac rehabilitation, EEG can be utilized to assess and monitor cognitive function, identify cognitive deficits, and track the progress of rehabilitation interventions. Integrating AI tools and IoT into cognitive cardiac rehabilitation can further enhance its effectiveness. AI algorithms can analyze the EEG data and extract meaningful insights, such as identifying patterns associated with cognitive impairment or predicting individual response to rehabilitation interventions. These insights can help personalize and optimize the rehabilitation program for each patient, leading to better outcomes. Furthermore, IoT devices can be employed to facilitate remote monitoring and real-time feedback during cognitive cardiac rehabilitation. Wearable EEG devices can provide continuous monitoring of brain activity, enabling healthcare professionals to assess cognitive performance in real-time and make prompt adjustments to the rehabilitation program if necessary. IoT connectivity can also enable seamless data transfer between the patient, healthcare providers, and AI systems, facilitating personalized feedback and intervention delivery. A comprehensive survey on the rehabilitative applications of EEG in healthcare can explore various aspects, such as: EEG-Based Assessment of Cognitive Function in Cardiac Patients: This includes evaluating cognitive domains such as attention, memory, executive functions, and information processing speed using EEG metrics. EEG Biomarkers for Predicting Cognitive Outcomes: Identifying EEG patterns or biomarkers that can predict the risk of cognitive decline or recovery potential in cardiac patients undergoing rehabilitation.

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EEG-Guided Cognitive Training Interventions: Exploring the use of EEGbased neurofeedback or brain-computer interfaces (BCIs) to deliver personalized cognitive training programs for cardiac patients. Integration of AI Tools for EEG Data Analysis: Examining how AI algorithms, such as machine learning and deep learning, can be applied to EEG data to extract valuable information and generate predictive models for cognitive cardiac rehabilitation. IoT-Enabled Remote Monitoring and Intervention Delivery: Investigating the use of IoT devices and connectivity to enable remote monitoring of cognitive performance, deliver real-time feedback, and facilitate personalized intervention delivery. By conducting a comprehensive survey on these topics, researchers can gain insights into the current state of EEG-based rehabilitative applications in healthcare and identify opportunities for integrating AI tools and IoT into cognitive cardiac rehabilitation. This knowledge can ultimately contribute to the development of more effective and personalized rehabilitation approaches that improve cognitive outcomes for cardiac patients.

Chapter 4 The topic of mental health predictions through online social media analytics is relevant to cognitive cardiac rehabilitation using IoT and AI tools in several ways: Identification of Psychological Distress: Online social media platforms provide a vast amount of user-generated content that can be analyzed to detect signs of psychological distress. By applying natural language processing and machine learning techniques to social media data, researchers and healthcare professionals can identify individuals who may be at risk of mental health issues, such as depression, anxiety, or stress. This information can be valuable in the context of cognitive cardiac rehabilitation, as patients recovering from cardiac events often experience psychological challenges. Personalized Interventions: By leveraging the insights gained from social media analytics, cognitive cardiac rehabilitation programs can be tailored to the specific needs of individual patients. AI tools can analyze the online behavior and sentiment expressed by patients, helping healthcare providers understand their emotional states and cognitive patterns. This information can guide the development of personalized interventions, such as targeted cognitive behavioral therapy techniques or mindfulness exercises, to support patients in managing their mental health during cardiac rehabilitation. Early Intervention and Prevention: Online social media analytics can also enable early intervention and prevention strategies. By detecting early warning signs xxiv

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of mental health issues through social media data, healthcare providers can reach out to patients at risk and offer timely support. For example, if a patient’s social media posts indicate a decline in emotional well-being, an AI-powered system can trigger an alert to notify healthcare professionals, who can then provide appropriate interventions to prevent the escalation of mental health problems. Long-Term Monitoring and Support: Cognitive cardiac rehabilitation is a continuous process that extends beyond the initial recovery phase. By integrating IoT devices into the rehabilitation program, physiological data such as heart rate, blood pressure, and activity levels can be collected and monitored. This data, combined with social media analytics, can provide a comprehensive picture of a patient’s mental and physical well-being over time. AI algorithms can analyze this integrated data to detect patterns, trends, and potential correlations between mental health and cardiac recovery, enabling healthcare providers to offer ongoing support and adjust treatment plans as needed. In summary, leveraging online social media analytics for mental health predictions aligns with cognitive cardiac rehabilitation using IoT and AI tools by enabling personalized interventions, early intervention and prevention, and long-term monitoring and support. These combined approaches have the potential to enhance patient outcomes and well-being during the rehabilitation process.

Chapter 5 The topic of “Emotion Detection Using EEG-Based Brain-Computer Interaction (BCI)” is relevant to Cognitive Cardiac Rehabilitation Using IoT and AI Tools in several ways: Emotional States Play a Crucial Role in Cardiac Rehabilitation: Emotional factors such as stress, anxiety, and depression can have a significant impact on the recovery and overall well-being of cardiac patients. By using EEG-based BCI, it becomes possible to detect and monitor the emotional states of patients during their rehabilitation process. This information can help healthcare providers tailor rehabilitation programs to better address emotional needs and enhance patient outcomes. BCI Provides a Non-Invasive Method for Emotional Assessment: EEGbased BCI technology allows the measurement and analysis of brain activity, which can be correlated with emotional states. By using this technology, healthcare providers can assess patients’ emotional responses during different phases of cardiac rehabilitation without invasive procedures or relying solely on self-reporting, which can be subjective. IoT and AI Tools Enable Real-Time Monitoring and Analysis: Cognitive Cardiac Rehabilitation can be enhanced through the integration of IoT devices and AI xxv

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tools. IoT devices can collect real-time data from various sources such as wearables, sensors, and monitoring devices. AI algorithms can then process and analyze this data to provide meaningful insights. In the context of emotion detection using BCI, IoT devices can be used to capture EEG signals, while AI algorithms can analyze these signals to detect specific emotional states. Personalized Rehabilitation Programs: The combination of emotion detection using BCI, IoT, and AI tools can enable the development of personalized rehabilitation programs. By continuously monitoring a patient’s emotional state, healthcare providers can adjust the intensity and content of rehabilitation exercises based on the detected emotions. This personalization can lead to improved engagement, motivation, and adherence to the rehabilitation program, ultimately benefiting the patient’s recovery. Overall, the integration of emotion detection using EEG-based BCI with Cognitive Cardiac Rehabilitation Using IoT and AI Tools holds potential to optimize cardiac rehabilitation by incorporating emotional factors into the treatment process, providing real-time monitoring, and enabling personalized interventions.

Chapter 6 The topic of “Machine Learning-Based Approaches in the Detection of Depression: A Comparative Study” is relevant to Cognitive Cardiac Rehabilitation Using IoT and AI Tools in the context of improving patient outcomes and personalized care. Let’s explore the connection further: Detection of Depression: Depression is a common comorbidity among patients with cardiovascular diseases, including those undergoing cardiac rehabilitation. Identifying depression early can significantly impact patient well-being and treatment outcomes. Machine learning techniques can be applied to analyze various data sources, such as patient self-reports, electronic health records, wearable devices, and social media data, to detect patterns indicative of depression symptoms. Comparative Study: A comparative study involves evaluating and comparing different machine learning approaches for depression detection. These approaches may include traditional algorithms (e.g., logistic regression, support vector machines) as well as more advanced techniques like deep learning or ensemble methods. By conducting a comparative study, researchers can determine which approach is most effective in accurately identifying depression in the context of cardiac rehabilitation. Cognitive Cardiac Rehabilitation: Cognitive cardiac rehabilitation focuses on improving patients’ cognitive functions and mental health in addition to their physical recovery. Integrating cognitive therapy and mental health interventions into cardiac rehabilitation programs can have a positive impact on patients’ psychological wellbeing, adherence to treatment plans, and overall cardiovascular health outcomes.

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IoT and AI Tools: Internet of Things (IoT) devices, such as wearable sensors and mobile applications, can collect a wealth of data regarding patients’ physiological and behavioral patterns. AI tools, including machine learning algorithms, can process and analyze this data to gain insights into patients’ mental health status. By combining IoT and AI tools, healthcare providers can monitor patients remotely, provide timely interventions, and personalize cognitive cardiac rehabilitation programs based on individual needs. The comparative study on machine learning approaches for depression detection contributes to the broader goal of enhancing cognitive cardiac rehabilitation using IoT and AI tools. By accurately identifying patients at risk of depression and tailoring interventions accordingly, healthcare professionals can provide more effective support during the rehabilitation process, improving patient outcomes, and overall quality of care.

Chapter 7 The topic of “Machine Learning-Based Approaches in the Detection of Suicide From Social Media Comments” is relevant to Cognitive Cardiac Rehabilitation Using IoT and AI Tools due to several reasons: Mental Health and Cardiac Health: Suicide is a serious public health issue, and individuals with cardiac conditions may be at an increased risk. There is a recognized link between mental health and cardiac health, as psychological factors can significantly impact the recovery and management of cardiovascular diseases. By detecting signs of suicide in social media comments, healthcare professionals can intervene early and provide appropriate support to individuals at risk, including those undergoing cardiac rehabilitation. Monitoring Patient Well-Being: Cognitive cardiac rehabilitation programs aim to improve patients’ mental and emotional well-being along with their physical health. By integrating machine learning approaches to analyze social media comments, healthcare providers can gain insights into patients’ mental states outside of clinical settings. This information can be used to personalize and optimize cognitive rehabilitation programs, ensuring that patients receive the necessary support to overcome mental health challenges that may arise during their recovery. Early Detection and Intervention: Machine learning algorithms can be trained to identify patterns and indicators of suicidal ideation in social media comments. This can help identify individuals who are at risk of self-harm or suicide. By integrating such algorithms into AI-powered tools used in cognitive cardiac rehabilitation, healthcare providers can receive real-time alerts or notifications when a patient’s social media activity raises concerns. This enables timely intervention and the provision of necessary mental health support, potentially saving lives. xxvii

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Holistic Approach to Patient Care: The combination of IoT and AI tools allows for a holistic approach to patient care, considering both physical and mental health aspects. By incorporating machine learning-based suicide detection from social media comments, healthcare providers can obtain a comprehensive understanding of patients’ well-being and tailor their rehabilitation programs accordingly. This integration promotes a more personalized and patient-centric approach, focusing on the overall health and recovery of individuals. Overall, integrating machine learning-based approaches for suicide detection from social media comments into cognitive cardiac rehabilitation using IoT and AI tools enhances the potential for early detection, intervention, and personalized care, ultimately improving patient outcomes and well-being.

Chapter 8 The topic of “Significant Approaches and Applications of Virtual Reality in the Treatment of Depression” is relevant to Cognitive Cardiac Rehabilitation Using IoT and AI Tools because it highlights the potential of virtual reality (VR) as a therapeutic tool for mental health conditions such as depression. By incorporating VR into cognitive cardiac rehabilitation programs, which focus on the psychological and cognitive aspects of recovery after a cardiac event, patients may benefit from a more comprehensive and holistic approach to their rehabilitation. Here’s why this topic is significant and relevant: Mental Health and Cardiovascular Health Connection: There is a wellestablished bidirectional relationship between mental health and cardiovascular health. Depression is a common mental health condition that often coexists with cardiovascular diseases, and it can negatively impact the recovery process and overall outcomes for patients undergoing cardiac rehabilitation. By addressing depression through innovative approaches like VR, the overall effectiveness of cardiac rehabilitation programs may improve. Enhanced Engagement and Adherence: Traditional cardiac rehabilitation programs can sometimes be monotonous and may not fully engage patients, leading to reduced adherence and limited outcomes. Integrating VR into cognitive cardiac rehabilitation can provide an immersive and interactive experience, making therapy sessions more engaging and enjoyable for patients. This increased engagement can potentially improve adherence to the rehabilitation program, leading to better long-term outcomes. Cognitive Rehabilitation Benefits: Cognitive cardiac rehabilitation aims to address cognitive impairments and emotional well-being following a cardiac event. Depression often affects cognitive function, and VR-based interventions have shown promise in targeting cognitive deficits. By combining VR with cognitive xxviii

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rehabilitation exercises, patients may experience enhanced cognitive improvement, emotional regulation, and overall psychological well-being. IoT and AI Integration: The use of IoT and AI tools in cognitive cardiac rehabilitation can provide personalized and data-driven interventions. IoT devices can monitor patients’ physiological parameters, physical activity levels, and medication adherence, providing real-time feedback and alerts. AI algorithms can analyze this data and provide personalized recommendations and interventions based on individual patient needs. By integrating VR with IoT and AI, a comprehensive ecosystem can be created to support patients’ cognitive, emotional, and physical well-being. Advancements in VR Technology: Virtual reality technology has significantly advanced in recent years, becoming more accessible, immersive, and user-friendly. These advancements allow for the development of tailored and realistic virtual environments that can simulate various situations, experiences, and challenges relevant to the cognitive cardiac rehabilitation process. By leveraging these technological advancements, VR can provide a more effective and targeted intervention for patients with depression during their rehabilitation journey. In conclusion, exploring the significant approaches and applications of virtual reality in the treatment of depression is relevant to cognitive cardiac rehabilitation using IoT and AI tools. By integrating VR into cognitive cardiac rehabilitation, healthcare providers can offer a more comprehensive and personalized approach to address depression, enhance cognitive function, and improve overall outcomes for patients recovering from cardiovascular events.

Chapter 9 The topic of a survey on cardiac rehabilitation relevant to cognitive cardiac rehabilitation using IoT (Internet of Things) and AI (Artificial Intelligence) tools is significant for several reasons: Rising Cardiovascular Diseases: Cardiovascular diseases, including heart attacks and strokes, are a leading cause of death globally. Cardiac rehabilitation plays a crucial role in improving the health outcomes and quality of life for individuals recovering from cardiovascular events. Therefore, exploring innovative approaches to enhance cardiac rehabilitation is essential. Cognitive Rehabilitation: Cardiac rehabilitation traditionally focuses on physical aspects, such as exercise training and medication management. However, cognitive impairments, such as memory problems, attention deficits, and depression, are common among cardiac patients. Cognitive cardiac rehabilitation aims to address these issues and optimize cognitive functioning alongside physical recovery. IoT and AI in Healthcare: IoT and AI technologies have the potential to revolutionize healthcare by enabling real-time data collection, remote monitoring, xxix

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and personalized interventions. In the context of cardiac rehabilitation, IoT devices like wearables can track vital signs, activity levels, and sleep patterns, providing valuable data for healthcare professionals. AI algorithms can analyze this data and provide personalized feedback, reminders, and interventions to enhance cognitive rehabilitation. Remote and Personalized Care: IoT and AI tools can facilitate remote monitoring and care delivery, particularly relevant in situations where patients may have limited access to healthcare facilities or face mobility challenges. By leveraging these technologies, healthcare professionals can remotely monitor patients’ progress, provide timely interventions, and deliver personalized cognitive rehabilitation programs tailored to individual needs. Evidence-Based Practice: Conducting a survey on cardiac rehabilitation using IoT and AI tools can help gather valuable insights from both healthcare providers and patients. The survey can collect data on the current usage, challenges, and potential benefits of incorporating IoT and AI in cardiac rehabilitation programs. This data can inform the development of evidence-based guidelines and best practices for cognitive cardiac rehabilitation using these innovative technologies. By exploring the intersection of cardiac rehabilitation, cognitive impairments, and IoT/AI tools, the survey can contribute to advancements in healthcare delivery, patient outcomes, and the overall well-being of individuals recovering from cardiovascular events.

Chapter 10 The topic of “Clinical and Diagnostic Presentation on Myocardial Biology” is relevant to Cognitive Cardiac Rehabilitation Using IoT and AI Tools because it provides a foundational understanding of the physiological and pathological aspects of the heart, specifically focusing on the myocardium. The myocardium is the muscle tissue of the heart responsible for its contraction and pumping action. Understanding the clinical and diagnostic presentation of myocardial biology is crucial for developing effective rehabilitation strategies for individuals with cardiac conditions. Cognitive cardiac rehabilitation refers to a comprehensive approach that integrates physical exercise, education, and psychological interventions to optimize recovery and improve the overall well-being of individuals with cardiac issues. It aims to enhance cognitive function, emotional well-being, and overall quality of life. By incorporating IoT (Internet of Things) and AI (Artificial Intelligence) tools into cardiac rehabilitation, healthcare professionals can gather real-time data on patients’ cardiac health, monitor their progress, and personalize their treatment plans. IoT devices such as wearables, remote monitoring systems, and connected medical devices can provide continuous data on various cardiac parameters, including heart rate, blood pressure, xxx

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and electrocardiogram (ECG) signals. AI algorithms can then analyze this data, identify patterns, and provide valuable insights to healthcare providers for better decision-making. To design effective cognitive cardiac rehabilitation programs using IoT and AI tools, a thorough understanding of myocardial biology is essential. It helps healthcare professionals in accurately diagnosing cardiac conditions, understanding the impact of these conditions on cognitive function and overall well-being, and tailoring rehabilitation interventions to address specific needs. By considering the clinical and diagnostic presentation of myocardial biology, healthcare providers can develop personalized treatment plans that target both the physical and cognitive aspects of cardiac rehabilitation. In summary, the knowledge of clinical and diagnostic presentation on myocardial biology is relevant to cognitive cardiac rehabilitation using IoT and AI tools because it forms the basis for understanding cardiac conditions, designing effective rehabilitation interventions, and leveraging technology to monitor and improve patient outcomes.

Chapter 11 Predictive analysis of heart disease using machine learning algorithms can be a valuable tool in the field of Cognitive Cardiac Rehabilitation (CCR) when combined with IoT and AI tools. Let’s break down the various components and their relevance: Machine Learning Algorithms: Machine learning algorithms can be trained on large datasets of patient information, including medical history, vital signs, laboratory results, and lifestyle factors. These algorithms can then analyze the data and identify patterns and relationships that may indicate a higher risk of heart disease. Some commonly used machine learning algorithms for predictive analysis in this context include decision trees, random forests, logistic regression, and support vector machines. Cognitive Cardiac Rehabilitation (CCR): CCR is a therapeutic approach that focuses on helping individuals with heart disease improve their cognitive and psychological well-being, in addition to physical rehabilitation. It involves various strategies to enhance cognitive function, emotional well-being, and behavioral modification. Predictive analysis using machine learning algorithms can aid in the identification of individuals who may benefit from CCR, as well as personalize treatment plans based on their specific risk factors and needs. IoT (Internet of Things): IoT refers to the network of interconnected devices and sensors that collect and transmit data over the internet. In the context of heart disease and CCR, IoT devices can be used to monitor patients’ vital signs, physical activity levels, medication adherence, and other relevant parameters. This continuous stream of data can provide valuable insights into patients’ progress and help identify potential issues or risks. xxxi

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AI Tools: Artificial intelligence tools, including natural language processing and computer vision, can play a significant role in CCR. Natural language processing techniques can be used to analyze patients’ textual data, such as electronic health records and patient-reported outcomes, to extract meaningful information. Computer vision can assist in analyzing images and videos for facial expressions, body language, and other visual cues that may provide insights into patients’ emotional states. The combination of predictive analysis, CCR, IoT, and AI tools can enable healthcare professionals to develop personalized treatment plans, monitor patient progress remotely, identify early warning signs, and intervene proactively. This approach has the potential to improve patient outcomes, enhance patient engagement and adherence, and optimize resource allocation in healthcare settings. However, it is crucial to consider ethical and privacy considerations while implementing these technologies and ensure that patient data is handled securely and in compliance with relevant regulations.

Chapter 12 The topic of “Heart Disease Prediction Using Machine Learning Algorithms” is highly relevant to Cognitive Cardiac Rehabilitation Using IoT and AI Tools. Let’s explore the connection between the two: Early Prediction and Intervention: Heart disease prediction using machine learning algorithms can help identify individuals who are at a higher risk of developing cardiovascular conditions. By analyzing various factors such as medical history, lifestyle choices, and physiological data, ML algorithms can provide insights into potential risks. This information can be used to design personalized cognitive cardiac rehabilitation programs for individuals. Targeted Rehabilitation Programs: Cognitive cardiac rehabilitation aims to improve the cognitive and emotional well-being of patients with heart disease. By incorporating machine learning algorithms and IoT tools, rehabilitation programs can be tailored to each patient’s specific needs. The predictive models can help identify the most effective cognitive interventions for different individuals based on their risk factors and psychological profiles. Remote Monitoring and Support: IoT devices can collect real-time data on a patient’s vital signs, physical activity levels, and sleep patterns. Machine learning algorithms can process this data to detect patterns and provide insights into a patient’s progress during cardiac rehabilitation. This information can be used to remotely monitor patients and offer personalized support and guidance when needed, improving the effectiveness of the rehabilitation program. Adaptive Rehabilitation Programs: Machine learning algorithms can continuously analyze patient data to adapt and optimize the cognitive cardiac xxxii

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rehabilitation programs. By considering a patient’s evolving risk factors, physiological data, and responses to interventions, AI tools can dynamically adjust the rehabilitation program to ensure it remains relevant and effective for the individual. Enhanced Decision-Making: Machine learning algorithms can analyze vast amounts of data to extract valuable patterns and insights. This information can aid healthcare professionals in making informed decisions regarding a patient’s cognitive cardiac rehabilitation plan. By leveraging AI tools, healthcare providers can deliver personalized care and interventions, leading to better patient outcomes. In summary, heart disease prediction using machine learning algorithms is relevant to cognitive cardiac rehabilitation using IoT and AI tools as it enables early prediction, targeted interventions, remote monitoring, adaptive rehabilitation programs, and enhanced decision-making for optimizing the rehabilitation process and improving patient outcomes.

Chapter 13 Cognitive Cardiac Rehabilitation is a crucial aspect of the overall treatment and recovery process for individuals with cardiac conditions. The integration of Internet of Things (IoT) and Artificial Intelligence (AI) tools has the potential to revolutionize and enhance the effectiveness of cognitive cardiac rehabilitation programs. By combining these technologies, healthcare providers can offer personalized, remote monitoring and support, leading to improved patient outcomes and quality of life. IoT devices such as wearable sensors, smartwatches, and mobile health apps can collect real-time data on various physiological parameters, including heart rate, blood pressure, and activity levels. These devices can continuously monitor patients during their rehabilitation journey, providing valuable insights into their progress and alerting healthcare providers in case of any irregularities or emergencies. This real-time data collection allows for early detection of potential issues, enabling prompt intervention and reducing the risk of complications. Additionally, AI algorithms can analyze the collected data, providing intelligent and personalized feedback to patients. Machine learning algorithms can recognize patterns and trends in the data, helping to identify areas of improvement and tailoring rehabilitation programs to individual needs. AI-powered virtual assistants can also deliver cognitive interventions and educational content, helping patients understand their condition, adhere to their rehabilitation plan, and make necessary lifestyle modifications. The combination of IoT and AI tools not only enhances the monitoring and support capabilities but also enables seamless communication and collaboration between healthcare providers and patients. Remote consultations can be conducted through telemedicine platforms, where healthcare professionals can remotely assess xxxiii

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patients’ progress, adjust treatment plans, and provide guidance. This approach reduces the need for frequent in-person visits, particularly for patients who may face geographical barriers or have limited mobility. Overall, the integration of IoT and AI tools in cognitive cardiac rehabilitation offers numerous benefits. It provides continuous monitoring, personalized feedback, and remote support, empowering patients to actively participate in their recovery journey. This technology-driven approach has the potential to improve adherence to rehabilitation programs, enhance patient engagement, and ultimately lead to better outcomes in terms of cardiac health and overall well-being. In conclusion, cognitive cardiac rehabilitation using IoT and AI tools is a promising area of research and application in the field of cardiac care. By harnessing the power of these technologies, healthcare providers can offer personalized, remote monitoring, and support to individuals with cardiac conditions. The integration of IoT devices and AI algorithms enables continuous data collection, analysis, and feedback, facilitating early detection of issues and personalized interventions. This technology-driven approach has the potential to revolutionize cardiac rehabilitation, improving patient outcomes, and enhancing the overall quality of care.

CONCLUSION Cognitive cardiac rehabilitation using IoT (Internet of Things) and AI (Artificial Intelligence) tools holds great promise in improving the effectiveness and accessibility of cardiac rehabilitation programs. By integrating IoT devices and AI algorithms, this approach can enhance patient monitoring, personalization of care, and datadriven decision-making, leading to better outcomes for individuals recovering from cardiac events. One of the key benefits of cognitive cardiac rehabilitation using IoT and AI tools is the ability to monitor patients in real-time. IoT devices such as wearable sensors can collect physiological data, such as heart rate, blood pressure, and activity levels, continuously and transmit it to healthcare providers. This enables early detection of abnormalities or warning signs, facilitating timely interventions and reducing the risk of complications. Furthermore, AI algorithms can analyze the collected data, identify patterns, and provide insights to clinicians, supporting personalized treatment plans and targeted interventions. The integration of AI tools in cognitive cardiac rehabilitation also enables the development of virtual coaching and self-management systems. AI-powered applications can provide personalized exercise programs, diet recommendations, medication reminders, and educational resources to patients. These systems can adapt and learn from individual patient data, tailoring interventions to their specific needs and preferences. By empowering patients with self-monitoring and self-management tools, cognitive cardiac xxxiv

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rehabilitation becomes more engaging, convenient, and effective. Moreover, the use of IoT and AI in cognitive cardiac rehabilitation promotes data-driven decisionmaking in healthcare. The large amounts of data collected through IoT devices can be analyzed using AI algorithms to derive meaningful insights. Clinicians can use this information to identify trends, predict outcomes, and adjust treatment plans accordingly. AI can also support risk stratification, helping healthcare providers identify high-risk patients who require additional attention or interventions. While cognitive cardiac rehabilitation using IoT and AI tools offers significant benefits, it is important to address some challenges. Ensuring data security and privacy is crucial, as the sensitive nature of patient health data requires robust safeguards. Additionally, integrating these technologies into existing healthcare systems and workflows may require infrastructure upgrades, training of healthcare professionals, and addressing regulatory and ethical considerations. In conclusion, cognitive cardiac rehabilitation using IoT and AI tools has the potential to revolutionize the field of cardiac rehabilitation. By leveraging real-time patient monitoring, personalization of care, and data-driven decision-making, this approach can improve outcomes, enhance patient engagement, and increase the accessibility of rehabilitation programs. However, careful implementation, addressing challenges, and ensuring ethical use of these technologies are essential for their successful integration into clinical practice. Parijat Bhowmick Indian Institute of Technology, Guwahati, India Sima Das Camellia Institute of Technology and Management, India Kaushik Mazumdar Indian Institute of Technology, Dhanbad, India

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Acknowledgment

We would like to express our sincere gratitude and appreciation to IGI Global for the opportunity to serve as an editor for the book Cognitive Cardiac Rehabilitation Using IoT and AI Tools. It has been a rewarding experience working with the dedicated team at IGI Global in bringing this publication to fruition. would like to extend our heartfelt thanks to all the contributing authors for their valuable insights and expertise, which have greatly enriched the content of this book. Their commitment and dedication to their respective fields have made this project a success. Additionally, we are grateful to our colleagues and co-editors who have provided guidance and support throughout the editorial process. Their expertise and input have been invaluable in shaping the content and structure of this book. We applaud the authors for their dedication, expertise, and commitment to advancing the field of cognitive cardiac rehabilitation. Their contributions pave the way for a future in which technology and human experiences harmoniously coexist, leading to improved patient outcomes and greater overall well-being. We are confident that this book will serve as an exceptional reference source for professionals and students alike, sparking further research, and progress in the field. Thank you once again to everyone involved in this book, your contributions have made this book a valuable resource for scholars, researchers, and practitioners alike.

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Cognitive Cardiac Rehabilitation Using IoT and AI Tools Parijat Bhowmick Indian Institute of Technology, Guwahati, India Sima Das Camellia Institute of Technology and Management, India

ABSTRACT Cognitive cardiac rehabilitation is an essential component of cardiac care, which aims to improve the mental and emotional well-being of patients with cardiovascular disease. Traditional rehabilitation methods involve in-person sessions with healthcare professionals, which may be limited by geographic distance, scheduling conflicts, and costs. However, with recent advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) technologies, it is now possible to provide cardiac rehabilitation

INTRODUCTION Cognitive cardiac rehabilitation is an essential component of cardiac care, which aims to improve the mental and emotional well-being of patients with cardiovascular disease. Traditional rehabilitation methods involve in-person sessions with healthcare professionals, which may be limited by geographic distance, scheduling conflicts, and costs. However, with recent advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) technologies, it is now possible to provide cardiac rehabilitation remotely and with higher precision. IoT and AI tools can be integrated into cardiac DOI: 10.4018/978-1-6684-7561-4.ch001 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Cognitive Cardiac Rehabilitation Using IoT and AI Tools

rehabilitation programs to monitor patients’ vital signs, physical activities, and mental health remotely. These tools can collect data from wearable devices, such as smartwatches, fitness trackers, and heart rate monitors, and use machine learning algorithms to analyze the data and provide insights into patients’ health status. Furthermore, IoT and AI tools can be used to deliver personalized rehabilitation plans and offer motivational support to patients based on their progress. The benefits of cognitive cardiac rehabilitation using IoT and AI tools are numerous. Patients can receive continuous care and monitoring, even after leaving the hospital, which can lead to better health outcomes and reduce the risk of readmission. Additionally, healthcare providers can gather valuable data on patients’ progress and make more informed decisions about their care. Finally, patients can receive rehabilitation services remotely, making it more accessible and convenient. Cognitive cardiac rehabilitation is a form of rehabilitation that aims to improve the mental and emotional well-being of individuals who have undergone cardiac events such as heart attack or heart surgery. It involves a combination of therapy, counselling, and lifestyle changes that can help individuals to better manage their condition and prevent future cardiac events. Recent advancements in technology have paved the way for the use of IoT and AI tools in cognitive cardiac rehabilitation. IoT devices such as wearable sensors and mobile apps can be used to monitor physical activity, sleep, and other important health metrics. AI algorithms can analyze this data to provide personalized feedback and recommendations to patients and healthcare providers. The integration of IoT and AI tools into cognitive cardiac rehabilitation has the potential to revolutionize the way cardiac rehabilitation is delivered. By providing patients with real-time feedback and support, these tools can help to improve adherence to rehabilitation programs, increase patient engagement, and ultimately improve patient outcomes. In this context, the objective of this essay is to explore the potential benefits and challenges associated with the use of IoT and AI tools in cognitive cardiac rehabilitation. We will examine the current state of the art in this field, identify the key stakeholders involved, and discuss the ethical and regulatory considerations that need to be taken into account. Cognitive Cardiac Rehabilitation (CCR) is a structured program designed to help individuals recover from a cardiac event, such as a heart attack or heart surgery. The program typically involves a combination of physical exercise, education, and counselling to help patients manage their symptoms and improve their overall health. In recent years, there has been a growing interest in using technology to enhance the effectiveness of CCR programs. The Internet of Things (IoT) and Artificial Intelligence (AI) tools have shown potential to improve patient outcomes and provide more personalized care.

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Cognitive Cardiac Rehabilitation Using IoT and AI Tools

IoT devices such as wearables, sensors, and mobile applications can monitor patient activity levels, heart rate, and other vital signs. This data can be used to track patient progress and provide real-time feedback to patients and healthcare providers. AI tools, on the other hand, can help analyze large amounts of data collected by IoT devices, identify patterns, and provide insights that can be used to tailor the CCR program to each patient’s specific needs. This combination of IoT and AI technologies can enable healthcare providers to provide more personalized and effective CCR programs, resulting in better outcomes for patients. This article will explore the potential benefits of using IoT and AI tools in CCR programs and how they can be integrated into clinical practice. In summary, cognitive cardiac rehabilitation using IoT and AI tools is a promising approach to improving cardiac care. By leveraging these technologies, patients can receive personalized rehabilitation plans, continuous monitoring, and motivational support, leading to better health outcomes and improved quality of life. Cognitive cardiac rehabilitation using IoT and AI tools is a promising approach to improving the outcomes of patients with cardiovascular diseases. The integration of IoT devices and AI algorithms can provide real-time monitoring and personalized rehabilitation plans, leading to better adherence and engagement in the rehabilitation program. In this essay, we will discuss the potential benefits and challenges of cognitive cardiac rehabilitation using IoT and AI tools and draw a conclusion on its effectiveness in improving the outcomes of patients with cardiovascular diseases.

BENEFITS OF COGNITIVE CARDIAC REHABILITATION USING IOT AND AI TOOLS Real-time Monitoring One of the significant benefits of cognitive cardiac rehabilitation using IoT and AI tools is the ability to provide real-time monitoring of patients’ physiological parameters such as heart rate, blood pressure, and oxygen saturation. IoT devices such as wearables, sensors, and mobile applications can capture these parameters continuously and transmit them to the cloud for analysis using AI algorithms. This real-time monitoring can help identify any deviations from the normal range and alert the healthcare provider or patient for early intervention. Realtime monitoring can also help patients track their progress and provide feedback on their performance, leading to better engagement and adherence to the rehabilitation program. Personalized Rehabilitation Plans Another significant benefit of cognitive cardiac rehabilitation using IoT and AI tools is the ability to provide personalized rehabilitation plans tailored to each patient’s needs. AI algorithms can analyze the patients’ physiological and behavioral data and create a personalized rehabilitation 3

Cognitive Cardiac Rehabilitation Using IoT and AI Tools

plan based on their goals, preferences, and abilities. This personalized plan can help patients adhere to the program and achieve better outcomes. Enhanced Patient Engagement Cognitive cardiac rehabilitation using IoT and AI tools can enhance patient engagement and motivation by providing real-time feedback and personalized rehabilitation plans. The use of gamification techniques such as challenges, rewards, and social support can also increase patients’ motivation and adherence to the rehabilitation program. These techniques can make the rehabilitation program more enjoyable and less monotonous, leading to better engagement and adherence.

CHALLENGES OF COGNITIVE CARDIAC REHABILITATION USING IOT AND AI TOOLS Data Privacy and Security One of the significant challenges of cognitive cardiac rehabilitation using IoT and AI tools is data privacy and security. IoT devices and cloud services collect and store a large amount of sensitive patient data, including their physiological parameters, medical history, and personal information. This data can be vulnerable to cyber-attacks, data breaches, and unauthorized access, leading to privacy violations and identity theft. Healthcare providers and technology companies need to ensure that appropriate security measures are in place to protect patient data from such risks. Technological Barriers Another significant challenge of cognitive cardiac rehabilitation using IoT and AI tools is technological barriers such as the cost of IoT devices, network connectivity, and data interoperability. Some patients may not have access to IoT devices or reliable network connectivity, limiting their participation in the rehabilitation program. Additionally, data interoperability can be a challenge as IoT devices and AI algorithms from different vendors may use different data formats and standards, making it difficult to integrate data from multiple sources.

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Survey on Cognitive Rehabilitation Sima Das Camellia Institute of Technology and Management, India

ABSTRACT Cognitive rehabilitation refers to a range of interventions designed to improve or restore cognitive functioning in individuals who have suffered from neurological injury or disease. This type of rehabilitation typically involves a combination of cognitive and behavioral strategies aimed at enhancing specific cognitive abilities such as attention, memory, language, and problem-solving skills. The process of cognitive rehabilitation begins with a comprehensive assessment of the individual’s cognitive abilities, followed by the development of an individualized treatment plan.

INTRODUCTION Cognitive rehabilitation is a form of therapy that has the objective of enhancing cognitive function and aiding individuals in recovering from brain injuries, illnesses, or other cognitive impairments. Its focus is on employing diverse techniques and exercises to address cognitive deficits like memory loss, attention, language, problemsolving, and decision-making skills. The primary aim of cognitive rehabilitation is to restore independence and enhance the quality of life for individuals undergoing the therapy. It is commonly utilized in the treatment of various conditions, including traumatic brain injuries, stroke, Parkinson’s disease, multiple sclerosis, and dementia. Cognitive rehabilitation can be conducted in different settings such as hospitals, rehabilitation centers, or even

DOI: 10.4018/978-1-6684-7561-4.ch002 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Survey on Cognitive Rehabilitation

in the individual’s own home. The therapy may involve either one-on-one or group sessions, and it can be customized to suit the specific needs of each person. Overall, cognitive rehabilitation has proven to be a highly effective form of therapy for individuals with cognitive deficits, aiding them in regaining their independence and improving their overall quality of life. Advantages are as follows: 1.

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Cognitive rehabilitation (Das et. al., 2023) is a type of therapy that aims to improve cognitive functioning and help individuals with cognitive impairments regain lost abilities. Some advantages of cognitive rehabilitation include: Improved quality of life: Cognitive rehabilitation (Das et. al., 2022) can help individuals regain lost abilities and improve their overall quality of life. This can include improvements in memory, attention, problem-solving, and communication skills. Increased independence: As cognitive functioning improves (Das et. al., 2023); individuals may become more independent in their daily lives. They may be better able to manage their own finances, take care of household tasks, and navigate their environment with greater ease. Better social functioning: Cognitive rehabilitation can help individuals with cognitive impairments (Das et. al., 2022) communicate more effectively, which can lead to better social functioning. This can include improved relationships with family members, friends, and caregivers. Improved job performance: Individuals who have encountered cognitive impairments due to a brain injury or other conditions can benefit from cognitive rehabilitation, as it can assist in enhancing their job performance and boosting their capacity to work. Cost-effective: Compared to long-term care or hospitalization, cognitive rehabilitation (Das et. al., 2020; Ghosh et. al., 2022; Das et. al., 2023) is often a more cost-effective treatment option. It can help individuals regain lost abilities and reduce the need for ongoing care. Overall, cognitive rehabilitation can be a valuable tool for individuals with cognitive impairments. It can help them improve their cognitive functioning, regain lost abilities, and lead more fulfilling lives. Limitations are as follows:

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Cognitive rehabilitation aims to improve cognitive functioning and skills that have been lost or impaired due to injury or illness. While it can be highly effective in many cases, there are also limitations to this type of therapy that should be considered.

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One limitation of cognitive rehabilitation is that it may not be suitable for everyone. Some individuals may not be able to tolerate the intensity or duration of the therapy, or may have cognitive deficits that are too severe for the therapy to be effective. Another limitation is that cognitive rehabilitation can be time-consuming and costly. It often requires multiple sessions with a therapist or a team of therapists, which can be difficult to fit into a busy schedule. Additionally, many insurance plans do not cover the full cost of cognitive rehabilitation, making it inaccessible to some individuals. Finally, cognitive rehabilitation may not always produce lasting results. While it can be effective in helping individuals regain some cognitive functioning, it may not be able to completely restore their previous level of functioning. Additionally, the effects of cognitive rehabilitation may not always be sustained over time, and individuals may need to continue therapy or engage in other interventions to maintain their cognitive gains. Despite these limitations, cognitive rehabilitation can be a valuable tool for individuals with cognitive deficits. By understanding the limitations and potential challenges of this therapy, individuals and their healthcare providers can work together to determine the best course of treatment and maximize its effectiveness.

LITERATURE SURVEY Cognitive rehabilitation is a treatment approach aimed at improving cognitive function in individuals with neurological or other conditions affecting their cognitive abilities. Here are some relevant studies and references related to cognitive rehabilitation: Cicerone, K. D., Dahlberg, C., Kalmar, K., Langenbahn, D. M., Malec, J. F., Bergquist, T. F., & Morse, P. A. (2000). Evidence-based cognitive rehabilitation: recommendations for clinical practice. Archives of physical medicine and rehabilitation, 81(12), 1596-1615.The following collection of studies and references offers valuable insights into the effectiveness and practicality of cognitive rehabilitation interventions for individuals with cognitive deficits resulting from neurological or other conditions. Rohling, M. L., Faust, M. E., Beverly, B., & Demakis, G. (2009) conducted a meta-analysis that re-examined the effectiveness of cognitive rehabilitation following acquired brain injury. This study built upon the systematic reviews by Cicerone et al. (2000, 2005). Das, N., & Das, R. (2021) conducted a systematic review and meta-analysis that specifically focused on the effectiveness of cognitive rehabilitation for individuals with traumatic brain injury. Green, R. E., Turner, G. R., Thompson, W. F., & Drew, A. (2008) provided an 7

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overview of rehabilitation interventions for cognitive deficits and discussed their practicality and effectiveness. Wilson, B. A., Emslie, H., Quirk, K., & Evans, J. J. (2001) investigated the effectiveness of a paging system in reducing everyday memory and planning problems in individuals with memory impairments. Ponsford, J., Bayley, M., Wiseman-Hakes, C., Togher, L., Velikonja, D., McIntyre, A., ... & Tate, R. (2012) offered recommendations for managing cognitive deficits following traumatic brain injury, with a focus on posttraumatic amnesia/delirium. Clare, L., & Woods, R. T. (2014) conducted a review that specifically focused on cognitive training and cognitive rehabilitation interventions for individuals with early-stage Alzheimer’s disease. Kessels, R. P. (2012) conducted a study investigating the effectiveness of executive functions training in individuals with dementia. These studies collectively contribute to our understanding of the usefulness and practicality of various cognitive rehabilitation interferences for individuals with cognitive deficits resulting from neurological or other conditions.

PROPOSED WORK Cognitive rehabilitation is a type of therapy aimed at improving cognitive function in individuals with cognitive deficits resulting from various conditions such as traumatic brain injury, stroke, or neurodegenerative disorders. Here’s a proposed plan for cognitive rehabilitation: Assessment: The first step would be to assess the individual’s cognitive abilities using standardized neuropsychological tests. This will help identify specific areas of cognitive impairment and tailor the rehabilitation plan accordingly. Goal Setting: Based on the assessment, specific goals should be established that target the areas of cognitive impairment. Intervention: A variety of interventions can be used, including computer-based training, cognitive training exercises, and functional activities. Cognitive training exercises can include attention, memory, executive function, and visuospatial skills. Functional activities can include activities of daily living, such as cooking, cleaning, and shopping. Feedback and Monitoring: It’s essential to provide feedback to the individual throughout the rehabilitation process to encourage progress and reinforce positive changes. Monitoring progress regularly and making adjustments to the intervention plan accordingly is also crucial. Family and Social Support: Family and social support can play a vital role in cognitive rehabilitation. Educating family members about the individual’s cognitive deficits and providing them with strategies to help can significantly improve the individual’s quality of life. 8

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Follow-up: Follow-up evaluations should be conducted periodically to assess progress and adjust the intervention plan as necessary. In summary, cognitive rehabilitation is a personalized approach to help individuals with cognitive deficits regain or improve cognitive function. The key components of cognitive rehabilitation include assessment, goal setting, intervention, feedback and monitoring, family and social support, and follow-up evaluations.

RESULTS Cognitive rehabilitation is a therapeutic approach that focuses on improving cognitive function in individuals with neurological disorders, including conditions like traumatic brain injury, stroke, or dementia. The primary objective of cognitive rehabilitation is to assist individuals in regaining or enhancing their ability to think, reason, and remember, thereby improving their overall quality of life. Numerous studies have demonstrated the effectiveness of cognitive rehabilitation in enhancing various cognitive functions, such as attention, memory, language, and executive function. However, it is important to recognize that the outcomes of cognitive rehabilitation can differ depending on several factors. The severity and type of the neurological disorder, the individual’s age, and the specific cognitive functions being targeted all play a role in determining the extent of improvement. While cognitive rehabilitation can lead to significant advancements in cognitive function, particularly when it is customized to address an individual’s specific needs and goals, it is crucial to understand that it does not provide a cure for neurological disorders. The degree of improvement achieved through cognitive rehabilitation can vary from person to person. Additionally, maintaining the gains achieved through therapy may require ongoing efforts. Cognitive rehabilitation often involves a combination of targeted exercises, strategies, and techniques that individuals can practice and apply in their daily lives. Consistency and continued practice of these skills are crucial to sustaining the improvements made during rehabilitation. In summary, cognitive rehabilitation is a therapeutic approach aimed at improving cognitive function in individuals with neurological disorders. It has shown promise in enhancing attention, memory, language, and executive function. However, the effectiveness of cognitive rehabilitation can be different dependent on features like severity disorder, the individual’s age, and specific cognitive functions being targeted. It is important to note that cognitive rehabilitation is not a cure and may require ongoing efforts to maintain the gains achieved through therapy.

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CONCLUSION Cognitive rehabilitation is an effective approach for improving cognitive functioning in individuals who have suffered from brain injuries, strokes, or other neurological disorders. Through the use of various techniques and strategies, cognitive rehabilitation aims to help individuals regain lost skills or develop new ones, improve their quality of life, and increase their independence. There are many different approaches to cognitive rehabilitation, including computer-based training, cognitive behavioral therapy, and structured learning techniques. The specific approach used will depend on the individual’s needs, goals, and abilities. Research has shown that cognitive rehabilitation can lead to important enhancements in cognitive functioning, including attention, memory, language, and executive functioning. It can also lead to improvements in other areas, such as mood, behavior, and overall quality of life. However, cognitive rehabilitation is not a one-size-fits-all solution, and success depends on several factors, including the severity and type of injury or disorder, the individual’s motivation and participation in the rehabilitation process, and the expertise of the rehabilitation team. Overall, cognitive rehabilitation offers a promising approach for improving cognitive functioning and quality of life in individuals with neurological disorders. With continued research and advancements in technology, it is likely that cognitive rehabilitation will become even more effective in the future.

REFERENCES Cicerone, K. D., Dahlberg, C., Kalmar, K., Langenbahn, D. M., Malec, J. F., Bergquist, T. F., & Morse, P. A. (2000). Evidence-based cognitive rehabilitation: Recommendations for clinical practice. Archives of Physical Medicine and Rehabilitation, 81(12), 1596–1615. doi:10.1053/apmr.2000.19240 PMID:11128897 Clare, L., & Woods, R. T. (2014). Cognitive training and cognitive rehabilitation for people with early-stage Alzheimer’s disease: A review. Neuropsychological Rehabilitation, 24(3-4), 419–450. PMID:23957379 Das, N., & Das, R. (2021). The Effectiveness of Cognitive Rehabilitation for Individuals With Traumatic Brain Injury: A Systematic Review and Meta-analysis. American Journal of Occupational Therapy, 75(2), 7502205010p1-7502205010p8.

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Das, Sima & Bhowmick, Parijat & Giri, Nimay & Minakova, Kseniia & Rubanenko, Olena & Danylchenko, Dmytro. (2022). Telemedical System for Monitoring the Psycho-Neurological State of Patients in the Process of Rehabilitation. 1-6. . doi:10.1109/KhPIWeek57572.2022.9916354 Das, Sima & Ghosh, Ahona. (2023). Emotion Detection Using Generative Adversarial Network. . doi:10.1201/9781003203964-11 Das, Sima & Ghosh, Ahona & Saha, Sriparna. (2022). A Review on Gaming Effects on Cognitive Load for Smart Healthcare and Its Security. . doi:10.4018/978-1-66845741-2.ch001 Das, Sima & Ghosh, Lidia & Saha, Sriparna. (2020). Analyzing Gaming Effects on Cognitive Load Using Artificial Intelligent Tools. . doi:10.1109/ CONECCT50063.2020.9198662 Das, Sima & Malick, Sayantan & Dey, Sovan & Sarkar, Amin & Hossain, Fardin & Samad, Abdus. (2023). Game-Stresso-Tracker: EEG Based Smart Advisor Bot for Stress Detection During Playing BGMI Game. . doi:10.1007/978-981-99-0969-8_40 Das, Sima & Saha, Sriparna. (2022). Home Automation System Combining Internetof-Things with Brain–Computer Interfacing. . doi:10.1007/978-981-19-1408-9_11 Ghosh, Ahona & Das, Sima & Saha, Sriparna. (2022). Stress detection for cognitive rehabilitation in COVID-19 scenario. . doi:10.1049/PBHE042E_ch12 Green, R. E., Turner, G. R., Thompson, W. F., & Drew, A. (2008). The practicality and effectiveness of rehabilitation interventions for cognitive deficits. Current Opinion in Neurology, 21(6), 683–688. PMID:18989113 Kessels, R. P. (2012). Executive functions training in patients with dementia. Journal of Alzheimer’s Disease, 30(s1), S187–S196. Ponsford, J., Bayley, M., Wiseman-Hakes, C., Togher, L., Velikonja, D., McIntyre, A., ... Tate, R. (2012). INCOG recommendations for management of cognition following traumatic brain injury, part I: Posttraumatic amnesia/delirium. The Journal of Head Trauma Rehabilitation, 27(2), 118–129. PMID:24984094 Rohling, M. L., Faust, M. E., Beverly, B., & Demakis, G. (2009). Effectiveness of cognitive rehabilitation following acquired brain injury: A meta-analytic reexamination of Cicerone et al.’s (2000, 2005) systematic reviews. Neuropsychology, 23(1), 20–39. doi:10.1037/a0013659 PMID:19210030

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Wilson, B. A., Emslie, H., Quirk, K., & Evans, J. J. (2001). Reducing everyday memory and planning problems by means of a paging system: A randomised control crossover study. Journal of Neurology, Neurosurgery, and Psychiatry, 70(4), 477–482. doi:10.1136/jnnp.70.4.477 PMID:11254770

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A Comprehensive Survey on Rehabilitative Applications of Electroencephalogram in Healthcare Pritam Chatterjee Maulana Abul Kalam Azad University of Technology, West Bengal, India Ahona Ghosh https://orcid.org/0000-0003-0498-285X Maulana Abul Kalam Azad University of Technology, West Bengal, India Sriparna Saha https://orcid.org/0000-0002-7312-2450 Maulana Abul Kalam Azad University of Technology, West Bengal, India

ABSTRACT A set of therapeutic control required for persons suffering from or expected to suffer from limitations in daily living activities is called rehabilitation which can restore or improve the functional ability in a stipulated time. These abilities can be from the physical aspect, can be from mental aspect and can be from cognitive perspective also. During human interaction, sensation or emotion plays a major role. To collect this sensation, the most important work is to implement an emotion collection system recording the signal accurately. The present chapter discusses all the implemented methodologies and the corresponding applications. The first one is concerned with a set of recommendations to overcome the shortcomings of the existing works and the second one is about a detailed analysis of the steps to be performed for achieving proper rehabilitative aid for future research in this concerned area. DOI: 10.4018/978-1-6684-7561-4.ch003 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Survey on Rehabilitative Applications of Healthcare Electroencephalogram

INTRODUCTION The brain can be interfaced with a computer to collect brain signals, examine those signals, and convert them into commands given to output devices to perform the expected tasks. The main objective is to restore the useful activity of people who are disabled or less abled by neuromuscular diseases like stroke, spinal cord injury, etc. Brain-computer interfacing (BCI) technology is in focus because it is a quickly growing research and development technology that greatly stimulates scientists, engineers, clinicians, and the public in general. BCI requires convenient, portable, and safe signal acquisition hardware which can work in all situations. Communication within the human nervous system is done by passing messages between cells using electrochemical signals, resulting in electrical impulses in our brain cells (Ghosh & Saha, 2022). If a subject suffering from prosthesis damage in their arms wants to bring a glass of water from a nearby table to drink, the subject cannot bring it using their arms. A similar problem is faced by subjects with disorders in the brain’s parietal lobe when they attempt to perform the same task (Ghosh & Saha, 2020). But in both situations, an electroencephalography (EEG) acquisition device can be used to monitor and record the occipital data of that subject while they are planning to do the activity by looking at the glass, and depending on the recorded EEG signal, a robotic arm (artificial limb) is active to grasp the glass. This simple scenario is controlling an artificial limb based on our brain signals. The work is vital and effective in rehabilitative applications where a subject suffers from parietal and/or motor cortex disorders and, in turn, suffers from prosthesis issues. Neuronal postsynaptic membrane polarity changes generate various electrical signals due to voltage-gated activation (Nakajima & Baker, 2018). Nowadays, BCI is implemented using non-invasive EEG signals, which are easy, safe, and inexpensive to acquire. These electrical signals are significantly decreased when passing through the dura, skull, and scalp, which is the main disadvantage (Scrivener & Reader, 2022). Because of this, the main information may be dropped. In recent years, the coordination control scheme of intelligent agents like robots has drawn the attention of many researchers as the work efficiency of robots outperforms the same of human beings in various fields (Corcione et al., 2005). In the real world, robots suffer from uncertainties like external disturbance, parameter perturbations, and unmodelled dynamics, which are being attempted to overcome by different machine learning (ML) tools. Communication with a human-machine interface may be multimodal since the mouse and keyboard are not accepted nowadays as the only input sensory system. Humans should be able to communicate with robots in ways that are as close to the concise, rich, and diverse forms of communication they use with one another as possible.

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Several researchers have targeted rehabilitation as the application area of ML. A nonlinear sliding mode controller has been attempted to be implemented for the upper limb exoskeleton in (Rahman et al., 2012) for stroke rehabilitation, where different exercises are to be undergone involving single and multi-joint movements. The experimental outcome has proved it as an effective one. Still, it dealt with only passive arm movements, whereas in the future, active movements are also to be considered to broaden the scope of the control strategy. Respiratory rehabilitation has been the area of research in Lee et al.’s robot assistance work (Lee et al., 2015), where the robot is mainly controlled by an attached joystick which creates pressure in the abdominal region of the human body and helps to exhale easily. Logarithmic profiles with some definite shape parameters have been useful in this case, and user safety has also been another concern. Three degrees of freedom wearable robotic assistive devices have been designed to help the subject in passive rehabilitation (Islam et al., 2017), where movement tracking error is 0.5 degrees. Based on user ratings, the gesture-controlled evidence-based robot has provided cost-effective rehabilitation treatment and improved mobility (Segal et al., 2017). However, motor learning and better functioning can be accomplished through a gesture-controlling robot or not; it is to be checked in the future by using game therapy or virtual reality-based therapy. These papers deal with bodily signals, which only apply to motor rehabilitation. Ortega et al. have achieved cognitive rehabilitation using a 2D vision system by facial feature detection, image processing, and hand movement tracking using a Kinect sensor during some exercise performance monitoring. A game-based rehabilitation system has been proposed by (GonzálezOrtega et al., 2014), where people were motivated more than (Roy et al., 2013) to participate actively because of the structure of the exercise in the form of a game. But the authors have not considered brain signals and responses in rehabilitation techniques. Signals from the brain help to indicate the subject’s mental state at that time. Based on that, possible regions where the disorder has occurred can be identified, and issues can be attempted to be resolved accordingly. The improvement in patients’ physical and mental health has not been measured after going through the systems, according to which some modifications in the complexity levels of the systems could be done further. In our daily life, many people are badly affected by stroke and stroke-like disorders. Different parts of the human body are badly affected by these. Using EEG based rehabilitation model, one can help them get back to their early life. After reading and analyzing the available research papers and works, we found that using EEG is a very simple and riskless process for patients recovering from stroke and strokelike disorders (Bag et al., 2023). So, we have decided to compare some existing works based on different parameters, try to find out the benefits and loopholes of those techniques, and take an idea about the possible best technique for improving 15

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these patients suffering from disorders. Accordingly, we have discussed different types of rehabilitation processes for different conditions of patients; in which some of these processes are more time-consuming, and some are less time-consuming. This work mainly focuses on comparing different rehabilitative aid prescribing techniques in the existing literature and determining their benefits and drawbacks to address the challenges and improve the performance for real-time clinical applications. After identifying the set of approaches, a comparison process is done between them. A case study has also been presented by considering stroke patient datasets. Apart from comparing different filtering, feature extraction, and classification techniques, a comparison analysis is also done between stroke-affected people and normal people’s brain maps generated using sLORETA (Ghosh et al., 2020).

INTRODUCTION TO MOTOR REHABILITATION Motor rehabilitation is an activity to assist a person suffering from any disability or illness by which he lost his skills or ability partially or completely to do some action or movement. Suppose one person cannot speak well after the stroke; using numerous techniques can help him shorten the problem (Saha & Das, 2021). It will go on till the full recovery of the patient. Motor stultification, such as paralysis, is the most common disorder after a stroke. Motor rehabilitation is a practice pivoted to recouping a particular function, such as moving body parts. It needs specific training by which the patients can learn again, and which helps them to rehabilitate. Figure 1 shows a common block diagram of the existing EEG-based rehabilitative applications working mechanism. The design of rehabilitative aid will require seamless interaction between the task-executing output device and the BCI operator without cognitive disruption. Based on the review, it has been observed that lower limb tasks involving hip joint Figure 1. General block diagram of rehabilitative applications using EEG

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and knee movements have never been applied in rehabilitative exercise (Deng et al., 2018) since only foot and upper limb kinesthetics have been deployed. EEG-based activity recognition frameworks only consist of orthosis and exoskeletons. No EEGbased prosthetic lower limb or ankle-foot device is available, to our knowledge. Most of the existing applications lack quantitative performance evaluators to test the applicability of the models in practical scenarios. Factors like walking endurance, energy consumption, and system cost have often been ignored. EEG-based BCI models have mostly applied non-invasive techniques instead of invasive ones and face different constraints due to limited feature sizes in complex movements. More efficient and robust techniques must be implemented to carry out further research.

INTRODUCTION TO ELECTROENCEPHALOGRAM The neurons in the human nervous system communicate by generating various electrical signals. There are mainly four lobes and one cortex in the brain that processes the signals. EEG sensor is the method that considers the electric signals initiated by the human brain. These are collected by electrodes which are positioned on the scalp. After collecting the data, it is sent to the amplifier for analysis. By processing EEG signals, brain injuries or such diseases can be detected.

Regions of Brain The different regions of the human brain are responsible for different daily living activities. This section describes the working of these regions (Ghosh et al., 2021).

Occipital Lobe The optical processing unit of our brain is the occipital lobe, which is situated in the backend of the human skull. It converts the data into images that are collected by optics. Different types of tasks, like verification of color, distance measuring, face recognition, etc., are done by it.

Parietal Lobe The parietal lobe tackles the crucial sensory area of the human body. It is situated in the upper rearmost region of the human brain. It is the most important judgment region of the human skull, which deals with various senses like touch, cold, hot, pain, pressure, etc.

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Temporal Lobe The temporal lobe manages the audiovisual data, which is encrypted with memory. It is presented near the ears. Its main function is storing long-term memory.

Frontal Lobe It is situated in the earlier part of the human skull. All the managerial functions of humans are done by it. It helps us with future planning and keeping the commands.

Prefrontal Cortex It is presented in the earlier part of the frontal lobe. It collects facts from outside and processes these. People determine and reach their aims with the help of this. It is central to consciousness, future planning, complex thinking, etc. Figure 2 has outlined the International 10-20 framework of electrode placement on the human scalp. The brain wave patterns are exclusive to every individual. The five widely recognized brain waves generated in different situations are as follows.

Brainwaves The brainwaves based on frequency can be categorized as given below.

Theta (4 – 7 Hz) It is associated with memory encoding. It is also generated during the time of sleep with dreams. The time when we are assigned a difficult task, the theta wave becomes more important.

Alpha (7 – 12 Hz) These waves are generated during the unbending time of the brain. It is generated when we are undisturbed.

Beta (12 – 30 Hz) These waves are produced when planning something or preparing to move some body parts. It is also produced when we are in a very conscious mind.

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Gamma (>30 Hz, typically 40 Hz) It is generated to communicate and exchange data between brain regions. It is the fastest wave.

Delta (1 – 4 Hz) These are produced during the time of dreamless sleep and during the time of meditation. It is also found during internal work of memory tasks with full concentration. These brain waves are very gentle.

OTHER DATA ACQUISITION DEVICES Besides the EEG, more devices are available to collect and analyze brain signals. The working of those data acquisition devices is described in this section.

Figure 2. 10-20 electrode placement framework of EEG

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Intracortical Electrode Array It is an array that consists of 100 microelectrodes made of Silicon. It collects the electrical signals from the cerebral cortex. It has been taking a long time for this kind of work in which it gives the best result. It also analyzes the central nervous system (Maynard et al., 1997).

Electrocorticography It is a kind of physiological study of electrical signals of the human body, mainly in the human brain. It is mainly used during surgery. The electrodes are placed on the scalp surface. It collects and stores the action from the cerebral cortex (Leuthardt et al., 2009).

Magnetoencephalography It is known to us that there are several electrical signals throughout the human brain. These electrical signals develop a magnetic field. Magnetoencephalography is the process of escalating this magnetic field. A very sensitive Magnetometer records it. Analysis of these helps research human brain activity (Baillet & Garnero, 1997).

Functional Magnetic Resonance Imaging It is another means of recording brain activity that recognizes blood circulation. Through this, we learned that blood circulation in that region increases when a particular brain area is activated. It is used to track down the human brain and spinal cord’s neural activity. But this process is disrupted by the noise, which can be removed in the later signal-processing stage (Bai et al., 2010).

Functional Near-Infrared Imaging It is an analysis of the human brain by utilizing near-infrared spectroscopy. Nearinfrared light is applied to detect the activity of the neuron. It has greater advantages because of its portability. It only measures the concentration of oxygen in the hemoglobin in the cortical region (Guyen et al., 2020).

Existing Methods Researchers have performed different studies and followed different approaches to achieve rehabilitative aid using EEG-based health monitoring applications. 20

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Schaechter has proposed brain plasticity associated with motor rehabilitation after stroke (Schaechter, 2004). For this, he has talked about some interventions like Neurofacilitation techniques (it is used to increase the participation of centrally paretic muscles). He has discussed some rehabilitation techniques, but some of his designs are quasi-experimental and otherwise non-experimental. By which the chances of results are very limited. Bashir has proposed a list of divergent processes engaged in repetitive motor activity (Bashir, 2014) and helps rehabilitate patients affected by stroke. The processes are functional electrical stimulation (used to minimize motor impairment of stroke patients with hemiparesis), paired-associative stimulation (to enhance the demonstrative of the cortico-spinal projection), and non-invasive brain stimulation. It has two different functions repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS). The process is more time-consuming. Dimyan et al. have proposed neuroplasticity for the rehabilitation of stroke patients (Dimyan & Cohen, 2014). Neuroplasticity is the capacity to develop the ability to acquire new skills and store memory in the human brain. In this process, task-specified skilling is given. There are mainly three stages; in the first stage, the molecular and cellular procedure of general movement is analyzed. In the second stage, the analysis is done to know the growth of biological, pharmacological, and electrophysiological approaches which help train neuroplasticity. And the last stage encourages systematic recovery within the brain by using biomedical and tissue engineering. Champaty et al. have proposed a motorized wheelchair (Champaty et al., 2014) based on electrooculography (EOG). It is developed through humanmachine interface. EOG is the process of calculating the deformity of the eye’s retina and evaluating the activity of pigment epithelium. Depending on this, the blink of the eyes is measured, and the data are collected, and later on, these data are classified. This collection is done through the signal-acquiring system, which also generates signals to check the motion of motorized wheelchairs. Cervera et al. have proposed motor rehabilitation after stroke (Cervera et al., 2018) using BCI. They have collected data or information from some review articles and the database of MEDLINE, CENTRAL, etc. They mainly used fact-finding and checked techniques, including electrical stimulation, motor imagery, robotic therapy, etc., for rehabilitation purposes. The samples which are taken are small in size. So, it is required that the larger samples enhance the accuracy of the study. Setiawan et al. have proposed recognizing EEG parameters to observe rehabilitation processes by utilizing individual analysis (Setiawan et al., 2019). They first collected EEG signals and divided them into two data sets: health hand movement and affected hand movement. After collecting this data, these are processed and analyzed. Then the feature extraction is done by which they have identified some statistical features. After examining the positive differences, they conclude that the power 21

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spectral density (PSD) is the best feature and alpha is the best frequency. In this work, only four features are extracted; there are requirements for further analysis in this section. There are also requirements for further research to demonstrate the PSD. Comment et al. have proposed stroke patient rehabilitation using EEG, virtual reality, and robotics (Comani et al., 2015). They came up with an approach of a passive robotic device that performs with the help of EEG along with virtual reality. The rehabilitation is mainly done through the applications of virtual reality training. First, the type of training is the simple sponge which is free movements. Then the hardness of the training increases gradually, like Twirl (2D coordination movements), Grab3D (double reaching in three dimensions), etc. Keng Ang et al. have examined the capacity of stroke patients to utilize motor imagery (MI) based on EEG along with BCI (Ang et al., 2011). They applied their research to different age groups and people who are disabled by different types of disorders. With the help of EEG, they collected the brain signals of the patients, and with the help of a common spatial pattern (CSP) algorithm, they divided the whole data set into two practical classes. The filter bank CSP approach develops a content-based MI detection model containing four gradual steps. There is a requirement to advance the EEG signal acquisition process. The whole system is dependent on this. So, the advancement of this will increase capacity and efficiency. Murugappan et al. have proposed examining emotion identification utilizing EEG, which will depend on visuals and audiovisual stimuli (Murugappan et al., 2009). The complete analysis is done by doing two types of experiments: one is audiovisual stimulus, and another is visual stimulus. They have utilized the “db4 ‘’ wavelet function, which works under the alpha band and can categorize five emotions (sad, happy, disgust, anger, and surprise). They have been given tasks like viewing inspirational videos and collecting data through EEG sensors. They use the average mean reference (AMR) method to remove noise and wavelet transform (WT) for feature extraction, which assists in applying pattern recognition. After the experiment, they concluded that the audiovisual stimulus shows more perfection than the visual stimulus. In this work, they have mentioned deficiencies like detecting a particular emotion, the impact of noise and artifacts, and utilizing many electrodes. For these kinds of problems, they must face some difficulties and limitations. Further research on sensation interchange has been conducted in recent years (Chakraborty et al., 2020). Hong et al. in (Hong et al., 2014) mainly discussed the system, which consists component-based face detector and expression classifier. A vector machine that uses scale-invariant techniques is used to classify the expression. In the future, it will focus on gestures and multimodal patterns of communication’s emotions. Rao in (Rao, 1999) worked on learning and recognizing static and dynamic events in the visual environment using the generative model and statistical theory of 22

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Kalman filtering. Vaid et al. in (Vaid et al., 2015) reviewed different EEG signals used in BCI systems where artifact detection and removal are major problems when conventional methods are applied, resulting in improper feature extraction from the signals. Features extraction from a signal with minimum computation and avoiding over-specification is still a major problem. Sreeshakthy et al. (Sreeshakthy et al., 2016) describe human emotions analyzed by EEG signals in various situations. Classification of emotions is determined by analysis of performance and classification of emotion groups. The main objectives behind the implication of visual perceptual and cognitive hurdles on daily activity are provided in (Toglia, 1989). Jutai et al. in (Jutai et al., 2003) detect written and printed words), apraxia (incapacity to carry out purposeful movement), ataxia (incapacity to guide the limbs visually, mis localization when focusing on a particular thing), depth perception (incapacity to decide depths and distances), spatial relations (incapacity to detect the position of two or more objects about self) and unilateral spatial neglect (incapacity to address to respond to meaningful sensory stimuli presented in the affected hemisphere). Patients received approximately 20 hours of examining (1 hour per day for four weeks in reading, writing, and calculation), tracking target practice, light searching, cancellation of stimulation, sensory awareness, spatial organization training, perceptual retraining, cognitive skill remediation, perceptual remediation, visual scanning, and mirror treatment, but some of these treatment processes are not functioning effectively or giving poor results. Due to sicknesses of the eyes, visual deficits appear, which require special attention (Trauzettel-Klosinski, 2011). After the sudden incident, complex brain injuries majorly caused several visual impairments. The visual deficit can harm the body part and restrict the person’s capability due to some organ failure. Some of the visual disorders are media opacity (results in a reduction of vision and contrast and enhanced susceptibility to glare with consequences for the ability to read and orientate oneself), retinopathies (it is any damage of the retina of the eyes that causes retinal diseases), macular disease (it causes mislaying of the center of the area of vision). Treatment was suggested in the form of optokinetic training with moving text, oculomotor training, optokinetic training with moving text, search task (single object), audiovisual training, search task (multiple objects) vs. visual field stimulation, oculomotor training vs. attention training, audiovisual vs. visual. The authors have discussed several visual deficits and their treatments. But these treatments are more time-consuming and very costly. Even in some cases, after the treatment is given, the expected result is not seen.

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TOOLS USED IN EEG-BASED REHABILITATIVE APPLICATIONS Further artifact removal, feature extraction, selection, and classification algorithms used in the existing EEG-based rehabilitative applications have been described along with their working mechanism in this section.

Artifact Removal Algorithms Artifacts are waves that are not initiated by a human brain. In EEG-based rehabilitative applications, artifacts are mainly of two types: physiologic and non-physiologic artifacts. Humans mainly generate physiologic artifacts. Non-physiologic artifacts are generated from the surrounding environment. Several techniques are available to eliminate physiologic artifacts generated by an eye blink, muscle movements, and breathing (Beniczky & Schoemr, 2011).

Blind Source Separation The Blind Source Separation technique includes various unsupervised learning algorithms without previous data and additional reference channels. In (Sawada et al., 2019), PCA was used to remove artifacts from the raw data of EEG.

Regression Analysis The procedure removes the signal noise, assuming that each channel is the cumulative sum of pure EEG data and a proportion of artifacts (Jiang et al., 2019). Regression analysis first defines the amplitude relation between reference and EEG channels by transmission factors, then subtracts the estimated artifacts from EEG. Thus, this algorithm requires exogenous reference channels (i.e., EOG, ECG) to omit different artifacts.

Filtering of EEG Signals It weeds out the unwanted components of signals. Different filters include linear, nonlinear, analog, digital, etc. These filters’ main function is to fully block some frequencies while letting others pass through unchanged. The frequencies that can pass through are referred to as the passband, whereas those that cannot pass through are found in the stop band (Rho et al., 2021). In this section, we have segregated some filtering processes used in EEG-based rehabilitative applications based on the impulse ratio.

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Infinite Impulse Response (IIR) is a recursive filter in that the filter’s output is computed using the current and previous inputs and outputs. Because the filter uses previous output values, there is feedback on the output in the filter structure. Some well-known IIR filters are the Chebyshev filter, the Butterworth filter, and the Elliptic filter. Chebyshev Type I Filter operates to differentiate one frequency from another. It is used in both types, such as analog filters and digital filters. It is the first type of Chebyshev filter which has a passband ripple. Chebyshev Type II Filter is the second type of Chebyshev filter. Instead of having a wave in the passband, it has a stopband wave. Like type I, it also uses analog and digital filters. In EEG signals analysis, Chebyshev type II is used in most cases. For illustration, this uses a type II filter for decomposing EEG signals into different bands (Alturki, 2021). Butterworth Filter is a type of filter that works for signal processing. It is used to solve very hard mathematics. The scope of this filter in EEG is limited. The fourth-order Butterworth filter filters EEG signals in Nagabushanam et al.’s work (Nagabushanam et al., 2020). Also known as Cauer or Zolotarev filters, the elliptical filter maximizes the transition rate between the frequency response’s passband and stopband at the expense of ripple and increased ringing in the step response (Newson & Thiagarajan, 2019). Sample outcomes of the IIR filter for stroke patient data are given in Figure 8. A non-recursive filter known as finite impulse response (FIR) computes its output utilizing both the current and prior inputs. There is no feedback in the filter structure because it does not consider prior output values. Simply put, an equiripple filter is one with ripples of the same height. Actual digital filters may have a rippled magnitude response. For instance, because the traditional filter construction uses continuous functions (such as with the Fourier transform) to approximate a discontinuous ideal magnitude response (Mahabub, 2019), the magnitude response of a FIR low pass filter may have ripples close to its cut-off frequency. With consecutive iterations of the forward and inverse discrete Fourier transforms, a FIR equiripple filter can be created. Also referred to as the Kaiser-Bessel, the Kaiser window is a family of one-parameter window functions used in spectral analysis and FIR design. Sample outcomes of the FIR filter for stroke patient data are given in Figure 9.

Feature Extraction Techniques for EEG Signals It is the technique of finding the meta-data from the EEG signals. This section discusses some tools applied to extract features from EEG-based rehabilitative applications.

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Time domain feature extraction EEG’s time domain or temporal features include statistical measures concerning time, i.e., mean, standard deviation, mode, skewness, kurtosis, Shannon entropy, log energy entropy, etc. (Liu & Fu, 2021).

Frequency Domain Feature Extraction The frequency domain features refer mostly to the relative powers of certain frequency bands. It includes PSD, spectral energy, etc. PSD is mainly used to calculate the strength of the frequency of the signals. It mainly differentiates periodic signals of broadband. It is widely used in the sector of EEG and BCI (Gómez Araújo et al., 2019) and sometimes also combined with time domain signals like the mean of power in the alpha band, mean of power in the beta band, the standard deviation of power in the alpha band, the standard deviation of power in the beta band, etc. (Wang & Wang, 2021).

Time-Frequency Domain Feature Extraction A significant method for signal decomposition into time and frequency is timefrequency analysis. The ability to track frequency changes across time is the most crucial aspect of time-frequency domain analysis. Short-Time Fourier Transform (STFT) is the simplest form of EEG signal’s time–frequency analysis (Li et al., 2020). Frequently, simply the spectrogram—or STFT’s squared magnitude—is taken into account. The signal of interest is evenly divided into a number of briefs, overlapping sections, and the data in each portion are then windowed and Fourier converted to compute the STFT. As a result, a collection of Fourier transforms at various points in time are produced, illustrating how these spectral qualities vary from one segment to the next—or, more specifically, how frequencies change with time. Wavelet Transform is a common substitute for the spectrogram that also gives coefficients as time-frequency domain features. A changeable window size that is based on spectrum frequencies gives wavelet transforms an edge over spectrograms. It serves as an example of the mathematical square-integrable function (Al-Qerem et al., 2020).

Spatial Features Spatial filtering is a technique in which the voltage values in two or more additional electrodes are weighted to alter the EEG amplitude in each electrode (Zhao et al., 2019). The spatial weights are determined by CSP using the observed signals in order 26

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to achieve the greatest feasible difference between the two classes in the variances of the signals retrieved by the linear combination of a multichannel signal and the spatial weights. It has been widely applied in the existing rehabilitation-based applications of EEG and performed well in different scenarios.

Feature Selection Methods for EEG Signals From the total feature space, sometimes the most relevant features must be selected for various reasons like avoiding overfitting, faster training, and easy interpretation. The outcomes of feature extraction and selection on filtered EEG data of stroke patients are presented in Figure 10. The following part of this survey describes several well-accepted feature selection methods.

Independent Component Analysis It is a tool that converts multivariate signals to independent signals. It is mainly used for noise removal in the process of signal collection. For example, when the collection of EEG signals is going on, there may be numerous noises that get later removed by this.

Principal Component Analysis It mainly groups the points in the coordinate space in real-time. It is mainly used to analyze data and create a predictive model. It is the procedure for the computation of principal components. It shifts data into a new coordinate system called an orthogonal linear transformation.

Commonly Used Classification Algorithms ML is a learning procedure that utilizes numerous algorithms to classify the data. It is a process of arranging things based on their characteristics, such as color, weight, speed, physical and chemical properties, etc. This type of grouping technique assembles the items based on common features. Below we have described some classification methods used in EEG-based rehabilitative applications in the existing literature. The corresponding results are presented in Figure 11 in terms of confusion matrices.

Support Vector Machine Support Vector Machine (SVM) is a training algorithm in ML that examines the data to classify. We can divide SVM into two parts, one is a linear SVM, and another 27

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one is a nonlinear SVM. Using this, we can classify different types of EEG signals, which helps us in further research and analysis work on rehabilitation. For example, Saputro et al. have identified 90 different seizure kinds of EEG signals using SVM to analyze Epilepsy (Saputro et al., 2019), where Independent Component Analysis, Hjorth Descriptor, and Mel Frequency Cepstral Coefficient are the feature extractor of the continuous monitoring EEG.

Decision Tree A Decision Tree is a tool that utilizes a tree-like model for deciding and analyzing the possible outcome. In ML, it is named decision tree learning. There are many decision tree types, but in the case of EEG, it is mainly used as a classification tree. The authors of (Trujillo et al., 2017) used it for statistical analysis and here mainly used it as Statistical Decision Tree for classification.

Random Forest It is an ensemble learning method for classification, regression, and other tasks by training many decision trees. For classification tasks, the random forest’s output is the class chosen by the majority of trees. The mean or average prediction of the individual trees is returned for regression tasks. It generally gives more accurate results than a Decision Tree and solves the overfitting problem of ML (Jochumsen et al., 2018).

k-Nearest Neighbors The k-Nearest Neighbors (k-NN) algorithm is an uncomplicated algorithm used to solve the problem of backsliding and classification. It is doing the classification based on the similarity. It does not speculate based on data, so it is non-parametric. Xu et al. (Xu & Plataniotis, 2012) used k-NN as a classifier to detect emotional states, which can be later extended to cognitive rehabilitation-based platforms for affective computing.

EEG-BASED REHABILITATIVE APPLICATION AREAS The rehabilitation-based healthcare application areas are discussed in this section, where EEG has been extensively applied as a data acquisition device. For absolute and relative power, a significant increase, reduction, or no significant change can be observed in power in each frequency band relative to control for each disorder. 28

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For schizophrenia and OCD (eyes closed), autism, and PTSD, significant declines in absolute power were dominating in the alpha band. And in the beta band, it was observed for internet addiction, autism, and attention deficit hyperactivity disorder. Significant increases were found in a few diseases, most often while individuals’ eyes were open, such as depression (beta, eyes closed and open), bipolar (beta and alpha, eyes open), schizophrenia (beta and alpha, eyes open), and alcohol habit (beta and alpha, eyes open) (beta, eyes closed) (Newson & Thiagarajan, 2019).

Bipolar Disorder Cerebral sickness leads to an innumerable low and high frame of mind and substitutes in many activities like sleep, thinking, feeling, etc. The main characteristic of bipolar disorder is a mood swing, such as extreme happiness to extreme anger. Some of the noticeable disorders are unrestrained excitement, happiness, restlessness, alcohol abuse and less amount of judgment capacity, reduction of concentration level, etc. EEG mainly quantifies the functions of the human brain. In the case of bipolar disorder, EEG mainly helps to detect and identify the disorder (Yasin et al., 2021). EEG assists in the early diagnosis of several disorders in the human brain.

Drug Rehabilitation It is a curing process for a person who is too addicted to alcoholic content, street drugs, medicine, etc. Various continuous processes like counseling and meditation recover them. Dependency on the drug is a serious problem in human life. It can affect the human body and brain (Volkow et al., 2004). EEG is a vital tool that shows the effects of addiction to the drug in the human brain. At first, the data is collected using EEG, and the rehabilitation process is started after the analysis. Quantitative EEG alterations in heroin addicts have only been studied in a few research. Low-voltage background activity with diminished alpha rhythm, a rise in beta activity, and a considerable number of low-amplitude delta and theta waves in central regions were observed in more than 70% of heroin users during the acute withdrawal stage (Egerton, 2006). Several lines of evidence imply that cannabis (marijuana, tetrahydrocannabinol) alters prefrontal brain functionality, impairing various sophisticated cognitive functions.

Gait Rehabilitation It mainly measures the movement of the human body, observation of eye movement, and working of muscles more accurately for learning the motion of humans. By this, we can detect the disorder of an individual. After that, we can give him medical 29

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treatment. Using EEG, we can know the activity of muscles, which plays a vital role in the cortical control of movements (van Iersel et al., 2004). It also helps to know the uniqueness of a person.

Vascular Hemiplegia Rehabilitation It is a recovery process for a patient with paralysis of one part of his body mainly caused due to stroke, meningitis, cerebral palsy, cerebral haemorrhage, etc. Numerous rehabilitation processes include carrying out exercise, training, relearning, exertion of muscle, etc. These processes vary from patient to patient (Marquez-Chin et al., 2016).

Dementia This is one kind of syndrome where there is degradation in human brain memory in terms of capacity to perform daily works, thinking, and analyzing capacity. It mainly affects aging people. It has mainly three stages such as early stage, middle stage, and last stage. EEG helps to detect mainly two types of dementia, vascular dementia, and Parkinson’s dementia, which are mainly cortical. EEG studies patients with continuous human brain disorders to detect this (Durongbhan et al., 2019).

Epilepsy This is a type of disorder in which nerve cells of the human brain are not working properly. There are mainly two types of Epilepsy one is acting on the entire brain, and another is acting on the portion of the brain. These originated because of withdrawal of alcohol. The symptoms after withdrawal can be too much fever and very low blood sugar etc. For this disorder, there are no particular treatment processes. It can be handled by medicinal treatment. Epilepsy can be identified using EEG clinically. There are also many identification processes, but EEG is the most nominal (Vidyaratne & Iftekharuddin, 2017). It records the signal in different conditions. After that, the analysis and rehabilitation started.

CASE STUDY FOR STROKE PATIENTS A stroke happens when blood circulation is interrupted in the human brain, disrupting the supply of oxygen and supplements. As a result, the brain cells are about to expire. It is one kind of medical crisis. Some signs of stroke are trouble speaking, paralysis, problems walking, problems with arm movement, etc. It can cause obstacles like 30

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pain in different body parts, memory loss, loss of arm movement, a problem during talking, etc. In this section a comparison between a stroke-affected person and a normal is discussed. The results of each step discussed previously are presented.

Dataset Description The EEG dataset of stroke patients has been collected from BNCI (Jeong et al., 2020), where EEG data has been considered from the Cz channel with 256Hz. Each neurofeedback session included trials in which cortical positivity had to be increased and trials in which cortical negativity had to be increased. Similarly, using the Brain Tech Traveler device, we have collected EEG data from healthy subjects in our Human-Computer Interaction Laboratory of the Maulana Abul Kalam Azad University of Technology, West Bengal.

Channel Selection It is mainly done using sLORETA, which figures out the cortical activity of the human brain. We have taken the data set from EEG readings of two-stroke patients. It is conducted eight times in one week between two conclaves. In EEG signals, two bipolar electrodes recorded the vertical movement of the eyes. Using sLORETA, we have shown that only the Occipital lobe of the brain of these patients has excessive activation during eye movement and emotion-analyzing processes, as depicted in Figure 3. Figure 4 shows the EEG recordings of stroke patients. On the other hand, after applying the eye movement and emotion analysis data set of a normal person, it has been shown that the Occipital and Parietal lobes have Figure 3. sLoreta visualization of stroke patients

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more activation, as shown in Figure 5. EEG signal recording of a normal person has been shown in Figure 6. So, it has clearly shown the difference in active brain lobes between a normal person and a stroke-affected person. There is also a difference between EEG signals recording graphs which can be clearly seen in the results.

Pre-Processing for Noise Removal The raw EEG signal must be pre-processed for essential noise removal. Figure 7 illustrates the PSD plot of the raw EEG signal obtained from a stroke patient during rehabilitative exercise. This section presents the outcomes of some impulse ratiobased filtering processes used in EEG-based rehabilitative applications. Since it

Figure 4. EEG signal recording of stroke patients

Figure 5. sLoreta visualization of a normal person

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Figure 6. EEG signal recording of a normal person

is not feasible to give the results for the combination of all the orders and all the frequency bands of Infinite Impulse Ratio filters, only order 2 and low-pass filter outcomes for the Butterworth, Chebyshev Type 1, Chebyshev Type II, and Elliptical filter have been shown in Figure 8. Here it is visible that they could remove the noise in the signals with the proper selection of filter parameters. Figure 9 shows the outcomes of the Equi-ripple and Keiserwin filters for low-pass and high-pass configurations. From the pictorial illustration, it can be concluded that it has weeded out the unwanted components of signals successfully. Figure 7. PSD plot of raw EEG signal

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Figure 8. Outcomes of applying IIR filters on raw EEG data of stroke patients

Figure 9. Outcomes of FIR filters on raw EEG data of stroke patients

Feature Extraction and Selection Outcomes Feature extraction techniques from EEG data of rehabilitative applications can be mainly categorized into four types. As it is not feasible to give the result of all the techniques applied on all the electrode channel data obtained from all the strokeaffected and normal subjects, thus Figure 10 presents sample outcomes of the time-domain feature extraction technique and PCA and ICA applied to the extracted features of stroke rehabilitation data.

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Figure 10. Outcomes of feature extraction and selection on filtered EEG data of stroke patients

Classification The classification results of four types of classifiers already discussed are presented in this section. The confusion matrices of classifying stroke-affected and normal persons using SVM, DT, RF, and kNN are presented in Figure 11 for this purpose.

CONCLUSION AND FUTURE WORK In this work, we have discussed several methods to implement a rehabilitative paradigm for patients who are disabled by some sudden injuries due to stroke or stroke-like disorders using EEG. But after reviewing and analyzing existing works, we have found that some rehabilitation procedures are working very effectively. Still, some are less effective and take longer to give the results. In the future, more attention is required to develop more effective, less time-consuming rehabilitation processes that give the best results. There is also scoped to use BCI in rehabilitation rather than stroke-like disorders. In the future, we will try to find out the best solutions for the recovery of these patients, which take less time and minimum cost. Multimodal data also will be considered for better performance of the system. 35

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Figure 11. Confusion matrices generated from SVM, DT, RF, and kNN classifiers

ACKNOWLEDGMENT The second author is grateful to the university for providing the AICTE Doctoral Fellowship, with appointment letter Ref. No.2.2.1/Regis./Appt.(AG)/Ph.D(ADF)/ 2021 dated 01.02.2021. The third author is grateful to the university for providing research seed money, File No.: 9.6/Regis./SD/Mn.(SS)/2019 dated 19.06.2019 and UGC Start-up Grant under the scheme of Basic Scientific Research, File No. F.30449/2018(BSR) dated 21.11.2019.

REFERENCES Al-Qerem, A., Kharbat, F., Nashwan, S., Ashraf, S., & Blaou, K. (2020). General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution. International Journal of Distributed Sensor Networks, 16(3), 1550147720911009. doi:10.1177/1550147720911009

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Alturki, F. A., Aljalal, M., Abdurraqeeb, A. M., Alsharabi, K., & Al-Shamma’a, A. A. (2021). Common spatial pattern technique with EEG signals for diagnosis of autism and epilepsy disorders. IEEE Access : Practical Innovations, Open Solutions, 9, 24334–24349. doi:10.1109/ACCESS.2021.3056619 Ang, K. K., Guan, C., Chua, K. S. G., Ang, B. T., Kuah, C. W. K., Wang, C., Phua, K. S., Chin, Z. Y., & Zhang, H. (2011). A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface. Clinical EEG and Neuroscience, 42(4), 253–258. doi:10.1177/155005941104200411 PMID:22208123 Bag, D., Ghosh, A., & Saha, S. (2023). An Automatic Approach to Control Wheelchair Movement for Rehabilitation Using Electroencephalogram. In Design and Control Advances in Robotics (pp. 105–126). IGI Global. Bai, X., Vestal, M., Berman, R., Negishi, M., Spann, M., Vega, C., Desalvo, M., Novotny, E. J., Constable, R. T., & Blumenfeld, H. (2010). Dynamic time course of typical childhood absence seizures: EEG, behavior, and functional magnetic resonance imaging. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 30(17), 5884–5893. doi:10.1523/JNEUROSCI.5101-09.2010 PMID:20427649 Baillet, S., & Garnero, L. (1997). A Bayesian approach to introducing anatomofunctional priors in the EEG/MEG inverse problem. IEEE Transactions on Biomedical Engineering, 44(5), 374–385. doi:10.1109/10.568913 PMID:9125822 Bashir, S. (2014). Novel Approaches of Non-Invasive Stimulation Techniques to Motor Rehabilitation Following Stroke: A Review. Brain & Neurorehabilitation, 7(2), 71–75. doi:10.12786/bn.2014.7.2.71 Beniczky, S., & Schomer, D. L. (2020). Electroencephalography: Basic biophysical and technological aspects important for clinical applications. Epileptic Disorders, 22(6), 697–715. doi:10.1684/epd.2020.1217 PMID:33270023 Cervera, M. A., Soekadar, S. R., Ushiba, J., Millán, J. D. R., Liu, M., Birbaumer, N., & Garipelli, G. (2018). Brain‐computer interfaces for post‐stroke motor rehabilitation: A meta‐analysis. Annals of Clinical and Translational Neurology, 5(5), 651–663. doi:10.1002/acn3.544 PMID:29761128 Chakraborty, D., Ghosh, A., & Saha, S. (2020). A survey on Internet-of-Thing applications using electroencephalogram. In Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach (pp. 21–47). Academic Press. doi:10.1016/ B978-0-12-819593-2.00002-9

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Champaty, B., Jose, J., Pal, K., & Thirugnanam, A. (2014, July). Development of EOG based human machine interface control system for motorized wheelchair. In 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD) (pp. 1-7). IEEE. 10.1109/AICERA.2014.6908256 Comani, S., Schinaia, L., Tamburro, G., Velluto, L., Sorbi, S., Conforto, S., & Guarnieri, B. (2015, August). Assessing neuro-motor recovery in a stroke survivor with high-resolution EEG, robotics and Virtual Reality. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3925-3928). IEEE. 10.1109/EMBC.2015.7319252 Corcione, F., Esposito, C., Cuccurullo, D., Settembre, A., Miranda, N., Amato, F., Pirozzi, F., & Caiazzo, P. (2005). Advantages and limits of robot-assisted laparoscopic surgery: Preliminary experience. Surgical Endoscopy, 19(1), 117–119. doi:10.100700464-004-9004-9 PMID:15549629 Deng, W., Papavasileiou, I., Qiao, Z., Zhang, W., Lam, K. Y., & Han, S. (2018). Advances in automation technologies for lower extremity neurorehabilitation: A review and future challenges. IEEE Reviews in Biomedical Engineering, 11, 289–305. doi:10.1109/RBME.2018.2830805 PMID:29994006 Dimyan, M. A., & Cohen, L. G. (2011). Neuroplasticity in the context of motor rehabilitation after stroke. Nature Reviews. Neurology, 7(2), 76–85. doi:10.1038/ nrneurol.2010.200 PMID:21243015 Durongbhan, P., Zhao, Y., Chen, L., Zis, P., De Marco, M., Unwin, Z. C., Venneri, A., He, X., Li, S., Zhao, Y., Blackburn, D. J., & Sarrigiannis, P. G. (2019). A dementia classification framework using frequency and time-frequency features based on EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 826–835. doi:10.1109/TNSRE.2019.2909100 PMID:30951473 Egerton, A., Allison, C., Brett, R. R., & Pratt, J. A. (2006). Cannabinoids and prefrontal cortical function: Insights from preclinical studies. Neuroscience and Biobehavioral Reviews, 30(5), 680–695. doi:10.1016/j.neubiorev.2005.12.002 PMID:16574226 Ghosh, A., & Saha, S. (2020). Interactive game-based motor rehabilitation using hybrid sensor architecture. In Handbook of Research on Emerging Trends and Applications of Machine Learning (pp. 312–337). IGI Global. doi:10.4018/978-15225-9643-1.ch015 Ghosh, A., & Saha, S. (2022). Suppression of positive emotions during pandemic era: A deep learning framework for rehabilitation. International Journal of Modelling Identification and Control, 41(1-2), 143–154. doi:10.1504/IJMIC.2022.127101

38

Survey on Rehabilitative Applications of Healthcare Electroencephalogram

Ghosh, L., Saha, S., & Konar, A. (2020). Bi-directional Long Short-Term Memory model to analyze psychological effects on gamers. Applied Soft Computing, 95, 106573. doi:10.1016/j.asoc.2020.106573 Ghosh, L., Saha, S., & Konar, A. (2021). Decoding emotional changes of androidgamers using a fused Type-2 fuzzy deep neural network. Computers in Human Behavior, 116, 106640. doi:10.1016/j.chb.2020.106640 Gómez Araújo, I., Laier, J. E., & Carrazedo, R. (2019). Enhanced power spectral density transmissibility matrix for operational modal analysis of structures. Journal of Structural Engineering, 145(6), 04019043. doi:10.1061/(ASCE)ST.1943541X.0002322 González-Ortega, D., Díaz-Pernas, F. J., Martínez-Zarzuela, M., & Antón-Rodríguez, M. (2014). A Kinect-based system for cognitive rehabilitation exercises monitoring. Computer Methods and Programs in Biomedicine, 113(2), 620–631. doi:10.1016/j. cmpb.2013.10.014 PMID:24263055 Güven, A., Altınkaynak, M., Dolu, N., İzzetoğlu, M., Pektaş, F., Özmen, S., Demirci, E., & Batbat, T. (2020). Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder. Neural Computing & Applications, 32(12), 8367–8380. doi:10.100700521-019-04294-7 Hong, K., Chalup, S. K., & King, R. A. (2014). Affective visual perception using machine pareidolia of facial expressions. IEEE Transactions on Affective Computing, 5(4), 352–363. doi:10.1109/TAFFC.2014.2347960 Islam, M. R., Assad-Uz-Zaman, M., Spiewak, C., & Rahman, M. H. (2017, April). Motion control of a robotic device for passive rehabilitation of human shoulder and elbow joint movement. In 2017 IEEE Great Lakes Biomedical Conference (GLBC) (pp. 1-1). IEEE. doi:10.1109/GLBC.2017.7928880 Jeong, J. H., Kwak, N. S., Guan, C., & Lee, S. W. (2020). Decoding movementrelated cortical potentials based on subject-dependent and section-wise spectral filtering. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(3), 687–698. doi:10.1109/TNSRE.2020.2966826 PMID:31944982 Jiang, X., Bian, G. B., & Tian, Z. (2019). Removal of artifacts from EEG signals: A review. Sensors (Basel), 19(5), 987. doi:10.339019050987 PMID:30813520 Jochumsen, M., Shafique, M., Hassan, A., & Niazi, I. K. (2018). Movement intention detection in adolescents with cerebral palsy from single-trial EEG. Journal of Neural Engineering, 15(6), 066030. doi:10.1088/1741-2552/aae4b8 PMID:30260322

39

Survey on Rehabilitative Applications of Healthcare Electroencephalogram

Jutai, J. W., Bhogal, S. K., Foley, N. C., Bayley, M., Teasell, R. W., & Speechley, M. R. (2003). Treatment of visual perceptual disorders post stroke. Topics in Stroke Rehabilitation, 10(2), 77–106. doi:10.1310/07BE-5E1N-735J-1C6U PMID:13680519 Lee, S. Y., Jeong, U., & Cho, K. J. (2015, July). An application of user-friendly control for a Respiratory Rehabilitation and Assistance Robot. In 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 441-446). IEEE. 10.1109/AIM.2015.7222573 Leuthardt, E. C., Miller, K. J., Schalk, G., Rao, R. P., & Ojemann, J. G. (2006). Electrocorticography-based brain computer interface-the Seattle experience. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 194–198. doi:10.1109/TNSRE.2006.875536 PMID:16792292 Li, L., Cai, H., Han, H., Jiang, Q., & Ji, H. (2020). Adaptive short-time Fourier transform and synchrosqueezing transform for non-stationary signal separation. Signal Processing, 166, 107231. doi:10.1016/j.sigpro.2019.07.024 Liu, Y., & Fu, G. (2021). Emotion recognition by deeply learned multichannel textual and EEG features. Future Generation Computer Systems, 119, 1–6. doi:10.1016/j. future.2021.01.010 Mahabub, A. (2019). Design and implementation of cost-effective simple FIR filter for EEG signal on FPGA. World Scientific News, (125), 1–17. Marquez-Chin, C., Marquis, A., & Popovic, M. R. (2016). EEG-triggered functional electrical stimulation therapy for restoring upper limb function in chronic stroke with severe hemiplegia. Case Reports in Neurological Medicine, 2016, 2016. doi:10.1155/2016/9146213 PMID:27882256 Maynard, E. M., Nordhausen, C. T., & Normann, R. A. (1997). The Utah intracortical electrode array: A recording structure for potential brain-computer interfaces. Electroencephalography and Clinical Neurophysiology, 102(3), 228–239. doi:10.1016/S0013-4694(96)95176-0 PMID:9129578 Murugappan, M., Juhari, M. R. B. M., Nagarajan, R., & Yaacob, S. (2009). An Investigation on visual and audiovisual stimulus based emotion recognition using EEG. International Journal of Medical Engineering and Informatics, 1(3), 342–356. doi:10.1504/IJMEI.2009.022645 Nagabushanam, P., George, S. T., Davu, P., Bincy, P., Naidu, M., & Radha, S. (2020, March). Artifact Removal using Elliptic Filter and Classification using 1D-CNN for EEG signals. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 551-556). IEEE. 40

Survey on Rehabilitative Applications of Healthcare Electroencephalogram

Nakajima, R., & Baker, B. J. (2018). Mapping of excitatory and inhibitory postsynaptic potentials of neuronal populations in hippocampal slices using the GEVI, ArcLight. Journal of Physics. D, Applied Physics, 51(50), 504003. doi:10.1088/1361-6463/ aae2e3 PMID:30739956 Newson, J. J., & Thiagarajan, T. C. (2019). EEG frequency bands in psychiatric disorders: A review of resting state studies. Frontiers in Human Neuroscience, 12, 521. doi:10.3389/fnhum.2018.00521 PMID:30687041 Paul, S., & Ghosh, A. (2023). A Study on the Application Domains of Electroencephalogram for the Deep Learning-Based Transformative Healthcare. Generative Adversarial Networks and Deep Learning: Theory and Applications. Rahman, M. H., Saad, M., Kenné, J. P., & Archambault, P. S. (2012). Nonlinear sliding mode control implementation of an upper limb exoskeleton robot to provide passive rehabilitation therapy. In Intelligent Robotics and Applications: 5th International Conference, ICIRA 2012, Montreal, Canada, October 3-5, 2012 [Springer Berlin Heidelberg.]. Proceedings, 5(Part II), 52–62. Rao, R. P. (1999). An optimal estimation approach to visual perception and learning. Vision Research, 39(11), 1963–1989. doi:10.1016/S0042-6989(98)00279-X PMID:10343783 Rho, G., Callara, A. L., Petri, G., Nardelli, M., Scilingo, E. P., Greco, A., & Pascalis, V. D. (2021). Linear and Nonlinear Quantitative EEG Analysis during Neutral Hypnosis following an Opened/Closed Eye Paradigm. Symmetry, 13(8), 1423. doi:10.3390ym13081423 Roy, A. K., Soni, Y., & Dubey, S. (2013, August). Enhancing effectiveness of motor rehabilitation using kinect motion sensing technology. In 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS) (pp. 298304). IEEE. 10.1109/GHTC-SAS.2013.6629934 Saha, S., & Das, N. (2021). A Kinect-based motor rehabilitation system for stroke recovery. In Handbook of Computational Intelligence in Biomedical Engineering and Healthcare (pp. 261–282). Academic Press. doi:10.1016/B978-0-12-8222607.00007-8 Saputro, I. R. D., Maryati, N. D., Solihati, S. R., Wijayanto, I., Hadiyoso, S., & Patmasari, R. (2019, May). Seizure type classification on EEG signal using support vector machine. [). IOP Publishing.]. Journal of Physics: Conference Series, 1201(1), 012065. doi:10.1088/1742-6596/1201/1/012065

41

Survey on Rehabilitative Applications of Healthcare Electroencephalogram

Sawada, H., Ono, N., Kameoka, H., Kitamura, D., & Saruwatari, H. (2019). A review of blind source separation methods: Two converging routes to ILRMA originating from ICA and NMF. APSIPA Transactions on Signal and Information Processing, 8(1), e12. doi:10.1017/ATSIP.2019.5 Schaechter, J. D. (2004). Motor rehabilitation and brain plasticity after hemiparetic stroke. Progress in Neurobiology, 73(1), 61–72. doi:10.1016/j.pneurobio.2004.04.001 PMID:15193779 Scrivener, C. L., & Reader, A. T. (2022). Variability of EEG electrode positions and their underlying brain regions: Visualizing gel artifacts from a simultaneous EEG‐fMRI dataset. Brain and Behavior, 12(2), e2476. doi:10.1002/brb3.2476 PMID:35040596 Segal, A. D., Lesak, M. C., Silverman, A. K., & Petruska, A. J. (2020). A GestureControlled Rehabilitation Robot to Improve Engagement and Quantify Movement Performance. Sensors (Basel), 20(15), 4269. doi:10.339020154269 PMID:32751876 Setiawan, H., Islamiyah, W. R., Wibawa, A. D., & Purnomo, M. H. (2019, August). Identifying EEG parameters to monitor stroke rehabilitation using individual analysis. In 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA) (pp. 337-342). IEEE. 10.1109/ISITIA.2019.8937238 Sreeshakthy, M., Preethi, J., & Dhilipan, A. (2016). A survey on emotion classification from EEG signal using various techniques and performance analysis. [IJITCS]. International Journal of Information Technology and Computer Science, 8(12), 19–26. doi:10.5815/ijitcs.2016.12.03 Toglia, J. P. (1989). Visual perception of objects: An approach to assessment and intervention. The American Journal of Occupational Therapy, 43(9), 587–595. doi:10.5014/ajot.43.9.587 PMID:2817075 Trauzettel-Klosinski, S. (2011). Current methods of visual rehabilitation. Deutsches Ärzteblatt International, 108(51-52), 871. PMID:22259642 Trujillo, P., Mastropietro, A., Scano, A., Chiavenna, A., Mrakic-Sposta, S., Caimmi, M., Molteni, F., & Rizzo, G. (2017). Quantitative EEG for predicting upper limb motor recovery in chronic stroke robot-assisted rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(7), 1058–1067. doi:10.1109/ TNSRE.2017.2678161 PMID:28278477 Vaid, S., Singh, P., & Kaur, C. (2015, February). EEG signal analysis for BCI interface: A review. In 2015 fifth international conference on advanced computing & communication technologies (pp. 143-147). IEEE. 10.1109/ACCT.2015.72 42

Survey on Rehabilitative Applications of Healthcare Electroencephalogram

van Iersel, M. B., Hoefsloot, W., Munneke, M., Bloem, B. R., & Olde Rikkert, M. G. M. (2004). Systematic review of quantitative clinical gait analysis in patients with dementia. Zeitschrift für Gerontologie und Geriatrie, 37(1), 27–32. doi:10.100700391-004-0176-7 PMID:14991293 Vidyaratne, L. S., & Iftekharuddin, K. M. (2017). Real-time epileptic seizure detection using EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(11), 2146–2156. doi:10.1109/TNSRE.2017.2697920 PMID:28459693 Volkow, N. D., Fowler, J. S., & Wang, G. J. (2004). The addicted human brain viewed in the light of imaging studies: Brain circuits and treatment strategies. Neuropharmacology, 47, 3–13. doi:10.1016/j.neuropharm.2004.07.019 PMID:15464121 Wang, J., & Wang, M. (2021). Review of the emotional feature extraction and classification using EEG signals. Cognitive robotics, 1, 29-40. Xu, H., & Plataniotis, K. N. (2012, September). Affect recognition using EEG signal. In 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) (pp. 299-304). IEEE. 10.1109/MMSP.2012.6343458 Yasin, S., Hussain, S. A., Aslan, S., Raza, I., Muzammel, M., & Othmani, A. (2021). EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review. Computer Methods and Programs in Biomedicine, 202, 106007. doi:10.1016/j.cmpb.2021.106007 PMID:33657466 Zhao, D., Tang, F., Si, B., & Feng, X. (2019). Learning joint space–time–frequency features for EEG decoding on small labeled data. Neural Networks, 114, 67–77. doi:10.1016/j.neunet.2019.02.009 PMID:30897519

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Chapter 4

Mental Health Predictions Through Online Social Media Analytics Moumita Chatterjee Aliah University, India Subrata Modak Maryland Institute of Technology and Management, Jamshedpur, India Dhrubasish Sarkar https://orcid.org/0000-0002-7418-2922 Supreme Institute of Management and Technology, India

ABSTRACT According to WHO data, mental health is a major source of concern throughout the world, amounting to suicide in the majority of instances if left untreated. Presently, social media is a great way for people to express themselves through text, emoticons, images, or videos that depict their sentiments and emotions. This has opened up the possibility of investigating social networks in order to better comprehend their users’ mental states. Social media is currently being used by researchers to predict the prevalence of mental illnesses such as depression, suicide ideation, anxiety, and stress. This area of study has considerable potential for the monitoring, diagnosis, and prevention of mental health issues. In this research we aim to give an overview of the most recent works for predicting mental health status on social media. We focused on data collection and annotation approaches, preprocessing and feature selection, model selection, and validation. We addressed research on depression and suicidal thoughts, which are the most common among mental health studies on social media. DOI: 10.4018/978-1-6684-7561-4.ch004 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Mental Health Predictions Through Online Social Media Analytics

1. INTRODUCTION Mental illness is a significant public health concern on a global scale due to the suffering, morbidity, dysfunction, and financial trouble caused by it. Estimates from the WHO and the National Mental Health Survey 2017 show about 197.3 million people in India need professional care for several mental health issues. This comprised around 45.7 million people with depressive illnesses and 45 million people with anxiety disorders. The issue has been compounded by the Covid-19 outbreak, making it a global concern (The Indian Express, 2022). Although there are various types of mental health problems in India, the most common among them is depression (World Health Organization, n.d.). Lancet investigations show a substantial relationship between mental health diseases and the suicide mortality rate in India (Statistica, 2022). Mental health determinants include the skill to control thoughts, emotions, behaviors, and interactions with others. Along with particular psychological, behavioral, and genetic characteristics, environmental, social, cultural, economic, and political factors also impact the mental health of an individual(Sagar et al., 2020). Effective therapy for mental illness depends on an early diagnosis (Zimmerman&Coryell, 1987). However, a significant number of people experiencing depressive symptoms still choose not to seek professional care as a result of the societal stigma attached to mental illness (De Choudhury et al., 2013). Thus, individuals frequently rely on social media to handle their concerns. Social networking services are popular destinations for people to connect and share their emotions and thoughts. People suffering from mental health concerns found solace in online forums, tweets, and blogs, where they could express their thoughts and experiences implicitly or overtly (Tsugawa et al., 2015; Reece & Danforth, 2017). Social media enables the analysis of online data for assessing user emotions and moods to understand their behavior during social media use. People suffering from any form of mental illness have a tendency to behave in a different manner on social media, producing enough data for modeling different characteristics. Mental health studies show a strong correlation between a person’s language use and their emotional health. Research on data from social media started around 2013 and many mental health issues such as depression (Tsugawa et al, 2015; De Choudhury et al., 2013; Reece et al., 2017; Ríssola et al., 2020; Samanta et al., 2023; Sarkar et al., 2022; Park et al., 2012; Bathina et al., 2021; Nadeem, 2016; Preoţiuc-Pietro et al., 2015; Maupomé & Meurs, 2018; Schwartz et al., 2014; Resnik et al., 2015; Tsugawa et al., 2015; Nguyen et al., 2014; Wolohan et al., 2018; Tyshchenko, 2018; Tadesse et al., 2019; Benamara et al., 2018; Song et al., 2018; Cacheda et al., 2019; Shen et al., 2017; Lora et al., 2020; Stankevich et al., 2020), suicide ideation (Sarkar et al., 2023; Coppersmith et al., 2016; O’dea et al., 2015; Burnap et al., 2015; Shing et al., 2018; Chatterjee et al., 2022a; Wongkoblap et al., 2017; Coppersmith et al., 2015; 45

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Dholariya, 2017; Pandey et al., 2019; Nock et al., 2008; Bilsen,2018; Klonsky et al., 2016; Fernandes et al., 2018; Nock et al., 2012; Ji et al., 2020; Cook et al., 2016; Mbarek et al., 2019; Ji et al., 2018; Ramírez-Cifuentes et al., 2020; Na et al., 2020; Duberstein et al., 2000; Islam et al., 2018; Al Asad et al., 2019; Chatterjee et al., 2021; Angskun et al., 2022; Gupta et al., 2022; Cha et al., 2022; Shen et al., 2017), schizophrenia (Mitchellet al., 2015;Hänsel et al., 2021; Bae et al., 2021), eating disorders (Chancellor et al., 2016; Wang et al., 2017; Paul et al., 2018; Kumar et al., 2019) and others have been assessed with a high accuracy of 80-90%. In addition, these methods can also be used to measure related disorders, for instance, stress, and self-harm without the need for an in-person assessment. Using this notion, several researchers have developed new types of future interventions and techniques for identifying mental health illnesses in their early stages. To do this, Natural Language Processing (NLP) approaches are used along with machine learning techniques to spot psychological disorders in user submissions. In this article, we discuss some of the methods used for predicting mental health disorders especially depression and suicidal ideation from user posts on social media. We focus on dataset collection and labeling methods, preprocessing of data, feature engineering, classification, model selection, and validation. We believe that this field is still in its nascent stage and identifying research trends and practices will enable us to identify gaps and promote further research in this area. The chapter is arranged as follows: Section II gives the overview of the used. Section III discusses data annotation and quality checking. Section IV describes the different feature engineering approaches. The classification algorithms are discussed in Section V.Section concludes the paper.

2. OVERVIEW OF THE CORPUS Research on Mental health disorders has seen a significant increase in recent years. This area has become the thrust area and has shown rapid growth with 20 papers in 2020, and 24 papers in 2021. The majority of the research articles in this study examined Twitter, making it the most popular social media platform.(Tsugawa et al., 2015; Chatterjee et al., 2022a; Samanta et al., 2023; Sarkar et al., 2022; Park et al., 2012; Bathina et al., 2021; Wongkoblapet al., 2017; Bentonet al., 2017; Nadeem, 2016; Tsugawa et al., 2015; Mbarek et al., 2019).Sina Weibo (13), Reddit (26) (Chatterjee et al., 2022a; Samanta et al., 2023; Sarkar et al., 2022; Tadesse et al., 2019), Facebook (24) (Schwartz et al., 2014), Instagram(8) (Hänse et al., 2021; Sharma et al., 2022)are other prominent sites.

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We looked at the various disorders and the associated symptoms that are studied in other research papers. Most of the papers studied depression(Tsugawa et al, 2015; De Choudhury et al., 2013; Reece et al., 2017; Ríssola et al., 2020; Samanta et al., 2023; Sarkar et al., 2022; Park et al., 2012; Bathina et al., 2021; Nadeem, 2016; Preoţiuc-Pietro et al., 2015; Maupomé & Meurs, 2018; Resnik et al., 2015;; Nguyen et al., 2014; Schwartz et al., 2014; Tsugawa et al., 2015; Wolohan et al., 2018; Tyshchenko, 2018; Tadesse et al., 2019; Benamara et al., 2018; Song et al., 2018; Cacheda et al., 2019; Shen et al., 2017; Lora et al., 2020; Stankevich et al., 2020) and associated terms such as postpartum depression, major depressive disorder, the severity of depression.Others major research area is suicidal ideation (Sarkar et al., 2023; Coppersmith et al., 2016; Burnap et al., 2015; Shing et al., 2018; Chatterjee et al., 2022b; Wongkoblap et al., 2017; Coppersmith et al., 2015; Dholariya, 2017; Pandey et al., 2019; Nock et al., 2008; Bilsen,2018; Klonsky et al., 2016; Fernandes et al., 2018; Nock et al., 2012; Ji et al., 2020; Cook et al., 2016; Mbarek et al., 2019; Ji et al., 2018; Ramírez-Cifuentes et al., 2020; O’dea et al., 2015; Na et al., 2020; Duberstein et al., 2000; Islam et al., 2018; Al Asad et al., 2019; Chatterjee et al., 2021; Angskun et al., 2022; Gupta et al., 2022; Cha et al., 2022; Shen et al., 2017), suicidality, suicide ideation and related behaviors. These researchers attempted to identify symptoms of suicide ideation from user discussions of suicide and suicide risk. Some studies(Chancellor et al., 2016; Wang et al., 2017; Paul et al., 2018; Kumar et al., 2019) considered eating disorders and related symptoms like anorexia, Schizophrenia (Mitchell et al., 2015; Hänsel et al., 2021; Bae et al., 2021), Anxiety and related disorders, bipolar disorders, panic attacks (World Health Organization, n.d.; Preoţiuc-Pietro et al., 2015), stress (Neogi et al., 2021; Baccianella et al., 2010; Kim et al., 2020; Nasrullah et al., 2022; Rastogi et al., 2022) and others. Going with the trend we focus our research on predicting depression and suicidal ideation from user posts on Twitter and Reddit.

3. DATASET ANNOTATION AND QUALITY CHECKING We aim to provide insight into the technique used by the researchers to find alternate signals in the corpus in the absence of clinical assessment. Many publications (Zhou et al., 2017; Huang et al., 2015; Wang et al., 2013) enrolled the help of domain experts such as psychologists and clinicians to provide annotation or labels to the data. In another study, (Benton et al., 2017) academics and domain experts collaborated to annotate datasets. Many users on social media participate or follow other network communities according to their choices and this participation is considered as a measure of annotation of the data. Participation in communities that focuses on mental health and posting in a community connected 47

Mental Health Predictions Through Online Social Media Analytics

to suicide crisis is considered a positive sign of mental illness by researchers. Some users’ tweets or posts include particular terms such as “I am suffering from...”, which might be interpreted positively if the statement incorporates specific mental health diseases. Positive annotations are given to self-proclaimed entries such as attempting suicide or utilizing antidepressants. Another frequent strategy is to give participants a screening questionnaire to examine their mental health, such as depression, suicide risk, and stress. Patient Health Questionnaire (PHP-9)(De Choudhury et al., 2014; Seabrook et al., 2018), Beck Depression Inventory (BDI) (Stankevich et al., 2020), Center for Epidemiologic Studies Depression Scale (CES-D ) (Reece & Danforth, 2017) and others are common screeners. Another study used Reddit Self reported Depression Diagnosis(RSDD) (Song et al., 2018) as their dataset. A prominent method considers the existence of certain keywords or phrases(Chatterjee et al., 2022a; Samanta et al., 2023; Sarkar et al., 2022). The dictionaries linked to suicide or stress, such as Linguistic Inquiry Word Count (Burnap et al., 2015; Lin et al., 2017), were employed by the researchers. Researchers also considered terms related to symptoms in tweets or profiles, as well as behaviors connected with disorders and statements involving prior events. In general, the annotations were presumed to be correct, and the gathered datasets were believed to be of good quality. Initial evaluation and annotations are merged to give a precise sample, which is then annotated by humans. Manual verification is done by researchers to ensure the veracity of keyword matching. Similar to positive annotations, negative or control data are also designed or sourced by publications in a number of ways. One method is to create a negative or control dataset from a random sample of social media users from sites such as Reddit or Tumblr. Another strategy taken into account by publications is posts or tweets that make no mention of mental illness or persons who do not participate in mental health communities. These publications assume a lower bound of cutoffs from screening participants with screeners (Reece & Danforth, 2017; Seabrook et al., 2018; Stankevich et al., 2020). Some studies took random samples and constructed matching samples along with demographic characteristics such as age, gender, etc(Benton et al., 2017; Zhou et al., 2017). In our study, we first gathered a collection of comments from subreddits that focuses on depression, where online communities provide aid to people. These postings are often made by individuals who are depressed, they might be classed as depressive statements. Normal posts related to friends, entertainment, or family are also gathered. The depressive postings were meticulously analyzed to identify a collection of terms that were utilized as Twitter keywords for searching. These search terms were included in 188,704 tweets that were collected from 2000 users using Twitter APIs. Among these tweets (37740/188704), 400 were used for testing reasons. The remaining posts were manually labeled and two datasets are 48

Mental Health Predictions Through Online Social Media Analytics

Table 1. Terms used in depressive and non-depressive posts Depressive-Indicative Terms found in Reddit forum Depressed, alone,feel pain,die, unhappy, escape, hurt, unworthy, nobody, unsuccessful, loneliness,blame,don’t want to live, worried, deserve better, jobless, uncomfortable, unworthy life,worry,break, distraction, reject, shit, no love, sucks,ugly,save

Terms in Standard subreddits movie,vacation,got married, promoted, party, mom, friends, match, cooking, beautiful, work, teacher, funny, parents, thankfully,weekend

created: one for depressed users and one for normal users. First, tweets with the most depressive-indicative phrases about a certain individual were selected. Tweets from people who appear to be depressed are included in the depressive dataset. Users with tweets containing non-depressive thinking, public concerns, and depression-related news or remarks are included in the non-depressed dataset. Following data collection, the next stage is to guarantee that the dataset produces high-quality findings. Many publications filtered the dataset to address concerns such as dataset bias and dataset quality. Some studies excluded data because it did not reach the minimal content criteria(Chatterjee et al., 2022b; Kumar et al., 2022). This includes behaviors such as a certain number of posts, a minimum number of friends/followers, activity on a topic, or the precise timing of specific behaviors such as sadness or suicidal thoughts. Duplicate replies, advertisements, and spam were also deleted from studies.

4. FEATURE ENGINEERING In this section we examine the characterisitics of the data that are relevant or prediction.This step is called the feature selection or feature engineering.The most important features that are considered for our research are: 1.

Linguistic features: This section describes features referring to the structure of the posts on social media like the length of the posts, part of speech tagging, count of special characters and modality tagging. Some researchers also studied the linguistic style as features using the library Linguistic Inquiry Word Count (LIWC) (Burnap et al., 2015; Lin et al., 2017). This feature class includes metrics collected from user tweets. Several indicators are taken into account, including the quantity and length of tweets, the count of tweets per individual that are connected to depression, and the proportion of such postings to all other postings made by the user. Figure 1 depicts the average tweet lengths for depressed and normal users. Users with depressed intents had much longer average text lengths than usual users, according to the data. This is due to 49

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2.

3.

the increased likelihood that individuals with severe depression may also have mental illnesses or social problems, both of which will show up in their messages. Emoticons: This feature counts the number and type of emoticons used in the user posts (Chatterjee et al., 2022a; Sarkar et al., 2022; Samanta et al., 2023). Several factors regarding the use of emoticons were identified during the study of suicidal and depressive datasets. It has been observed that the usage of the emoticon in depressive texts has been found to be significantly smaller than the non depressive ones. The most often used emojis by people with depressed tendencies are folded hands, begging faces, disappointed faces, loudly crying faces, screaming in terror faces,expressionless faces, bewildered faces, and faces without mouths. Normal people most frequently use the following emojis: smiling faces with heart eyes,red hearts,crying with pleasure, clapping hands, crying aloud, and winking faces. This information leads to the conclusion that individuals with depressed tendencies use negative emoticons more frequently than non-depressive users. Word and Character Models: This models rely on the probabilistic distribution of word and character patterns in text(Cacheda et al., 2019; Cha et al., 2022; Uddin et al., 2022). It includes word embeddings, ngram usage, BOW(bag of words) models, and TF-IDF(term frequency-inverse document frequency). Deep learning techniques for language modelling using convolutional neural networks are also being used. In our research, for extracting bigrams and unigrams we utilized the the Tf-Idf vectorizer from the Scikit-learn library in Python. The top 100 bigrams and unigrams are chosen for each individual.

Figure 1. Average tweet length for depressed and normal users

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Table 2. Emoticons feature description Emoticons measures

Description

Total emojis /user

Total number of emojis used by a particular user.

Total emojis/user per post

Total number of emojis used by a user per post

# of unique emojis used

Number of unique emojis used.

Most used emojis

Most commonly used emojis by an user.Indicates consistent state of mind of the user.

# of Emoji belonging to a specific subgroup(love,sad etc.)

Count of the emojis belonging to a specific subgroup.

The results show that lexical phrases that are associated with depression include negative and suicidal thoughts, self-obsession, fury, feelings, anger, despair, and interpersonal processes. Additionally, lexicons concerning physical symptoms like fatigue, sleeplessness, poor energy, or hyperactivity may be included in depressive messages. On the other hand, regular post lexicons contain words that refer to past occasions, social contacts, and family matters. Only the most common bigrams and unigrams were included in the term-document matrix. The top bigrams and unigrams are chosen for each type of post. Figures 2 and 3 illustrate this. The terms that appeared often are shown in Figure 2 and 3. 4.

Topical features : Some publications use features such as topic modeling for identifying meaningful connections between concepts or topics in datasets.Most common topic models used are Brown Clustering and the Latent Dirichlet Allocation . The approach specifies the amount of topics that must be created. The count

Figure 2. Depressed posts

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Figure 3. Normal posts

of topics specified decides the accuracy of classification. The authors examined several topic counts for the research and found that 10 was a reasonable amount. 5.BehaviourActivity: The features also recorded the individual’s behavioural activities (Chatterjee et al., 2022a; Chatterjee et al., 2022b; Kumar et al., 2022) such as the time of posting, the count and volume of postings, the frequency of posting, and the time variations of posting history.We performed a study on the time variations in the posting history of depressed and normal users. A study of the dataset shows that AM value occurs in the depressed data set more frequently compared to normal data set. Different factors such as time away from work, loneliness,loss of energy ,suicidal and depressing thinking are more frequent at nighttime and early morning. According to a study conducted on the datasets, it has been observed that more than 60% of the users remain active between 12am-6am in contrast to normal users. Normal users remain mostly active during the daytime(12 pm to 6pm) and the evening(6pm-12 am).This has been depicted in Figure 4.

Table 3. Generated text using the LDA feature on the depression dataset Topic 1

like, love, know, one, want, sorry, sad, cry, sick, hate, die, friend, know, cut

Topic 2

Think, want, Feel, ignore, understand, listen, notice, need, nothing, understand

Topic 3

fat, people, someone, something, never, pain, break, away, smile, sorry, alone

Topic 4

hurt,annoy,one, life, always, care, mean, lose,back,stay,leave, tire,try

Topic 5

Day, really, night, anyone, shit, really, break, better, sleep, bed

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Figure 4. Analysis of datasets in terms of different time periods

6.

7.

Interaction: Another major feature source is a user’s interactions on a social network platform. This consists of friendships and the follower-followee interaction. We also looked into community involvement, retweets/replies, and thread participation. Sentiment,Affect and Valence: An effective approach to analyze emotional content in posting by users is Sentiment Analysis. Emotions are classified as negative ,positive,or neutral in sentiment analysis. Depending on their profile, this function may tell whether a person is feeling good, bad, or neutral. A lot of studies examined moods,sentiment and emotions of people from their online posts by using sentiment scoring mechanisms such as TextBlob,VADER, LIWC etc.Other researches investigated intensity and affect ,polarity or count of negative and positive emoticons (Tadesse et al., 2019; Benamara et al., 2018; Cacheda et al., 2019; Dholariya, 2017). Analysis of sentiment of the tweets,

Table 4. Description of social media features Features

Description

Source of feature

#followers

Number of followers

Tweet metadata

#following

Number of profiles followed by the users

Tweet metadata

#friends

Number of friends

Tweet metadata

Polarity of profile description

Polarity value calculation of user profile description. Gives a floating point value between [-1,+1] indicating negative or positive polarity.

Using Python TextBlob function on profile description obtained from Tweet metadata.

Subjectivity of profile description

Subjectivity value calculation of user profile description. Gives a floating point value between [0.0 to 1.0] indicating subjective or objective context.

Using Python TextBlob function on profile description obtained from Tweet metadata.

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8.

shows that the majority of individuals in the depressed dataset have scores between -0.5 and -1, whereas the majority of users in the normal sample have sentiment scores above -0.5. Figure 5 depicts a graph showing the negative tweets frequency used by depressed and non-depressive individuals. Demographic features:Age, gender, income, education, and marital status are examples of personal demographic data that were employed as features. Some of these traits were deduced computationally rather than directly from the dataset (Benton et al., 2017; Zhou et al., 2017).

Feature selection techniques were applied in some of the papers (Cacheda et al., 2019;Angskun et al., 2022) for selecting salient features.

5. CLASSIFICATION ALGORITHM Machine learning is used in our research as a technique for prediction outcomes of our findings. Almost all papers define their efforts as predicting mental health disorder or associated symptoms. The algorithms used in our findings are Logistic Regression,Random Forest,XGBoost and SVM. In general,SVM (Chatterjee et al., 2022a; Samanta et al., 2023; Sarkar et al., 2022; Islamet al., 2018; Stankevich et al., 2020)is the most popular algorithm used for prediction which is used by 22 papers. Logistic regression,Random forest (Chatterjee et al., 2022a; Samanta et al., 2023; Sarkar et al., 2022),XGBoost (Chatterjee et al., 2022a; Samanta et al., 2023; Sarkar et al., 2022)are also most frequently used in other researches as they give good results.

Figure 5. Negative tweet frequency for depressed and normal users

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The decision to choose a certain algorithm was made based on which algorithm performed the best when tested against others. Additionally, when choosing the algorithm, consideration was given to the model’s applicability for the research project, its correct training, and how clinicians and other stakeholders would understand its features. We used k fold cross validation techniques with k as 10.The value of k may range from 5 (Richard et al., 2018) to 10 (Islam et al., 2018; Angskun et al., 2022). We also split the dataset and kept a portion of the data as a test set. The size of the test set ranges from 10% to 40%.

6. RESULTS The performance were measured precision(Pr), accuracy(A), recall(R) and F1Score(F).Other measures used were root mean squared error(RMSE) , specificity, area under the curve(AUC),and Sensitivity. Table 5 lists some of the results obtained by applying feature combinations using various classifiers. Figure 6 shows the accuracy measure using different features for different classifiers.

Table 5. Performance metrices using various classfiers LR

RF

SVM

XGBoost

LDA+Unigram+TF-IDF+SA+EA+PT+SMF Accuracy

0.86

0.61

0.70

0.71

Precision

0.85

0.30

0.70

0.69

Recall

0.85

0.50

0.70

0.69

F1-Score

0.85

0.38

0.70

0.71

LDA+Bigram+TF-IDF+PT+TA+SMF Accuracy

0.85

0.61

0.87

0.81

Precision

0.84

0.81

0.86

0.80

Recall

0.84

0.50

0.87

0.79

F1-Score

0.85

0.39

0.87

0.80

LDA+Trigram+TF-IDF+SA+EA+PT+TA+SMF Accuracy

0.84

0.63

0.89

0.81

Precision

0.83

0.80

0.88

0.80

Recall

0.84

0.53

0.87

0.80

F1-Score

0.84

0.44

0.88

0.80

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Figure 6. Measure of accuracy using different features for different classifiers

7. DISCUSSIONS Depression or Suicide ideation manifest itself in a variety of symptoms and indicators, including an inability to focus on any task, finish projects, or enjoy any activity. Depressed people are unhappy, irritated, and anxious, and they believe they are unfortunate and blameworthy. They are fatigued, sick, have headaches, and have depressed ideas. Depending on these aspects, a number of symptoms and indications were discovered in the dataset based on these characteristics, and specific aspects linked to emotional indicators (negative, positive, rage, anxiety, sad ,depression), usage of language and time related features were retrieved. Several feature extraction approaches were used to extract these factors. Throughout the text, the association between the aforementioned features and the approach toward depressive thoughts is studied, and a number of studies involving emoticon analysis, sentiment analysis, tweet statistics and temporal analysis, are conducted to discover a relationship between the attitude toward suicide ideation and depression and the feature used. The application of social media for expressing sentiments and emotions has opened up new avenues for diagnosing mental health issues among users.Early diagnosis of mental health concerns will assist in lowering symptom aggregation and will allow for widespread screening for mental diseases. However, no standard way for evaluating the accuracy of the research and methodology are employed in these studies. In this research, we discuss the research carried out on depression, and suicidal ideation,disorders, with an emphasis on the design, strategy, and 56

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methods utilised for analysis. Our findings described data collecting and annotation methods, preprocessing, feature and model selection, and validation. Regardless of increased attention in this topic, there still remains several unresolved issues with data annotation and methodologies for determining mental health status.

8. CONCLUSION AND FUTURE DIRECTIONS The goal of this study is to investigate suicide-ideation and depression materials provided by persons online for better understanding the general view on depression and suicide. Twitter is used for collecting data and it is analysed to help us better understand depressive thoughts and behaviour. The data set is used to extract a number of features, including language, topic, sentiment, personality, temporal, and TF-IDF. It is empirically shown that the traits are effective in differentiating depressive postings from normal ones. So, by carefully selecting attributes, it is feasible to get great prediction performance. Despite the method’s efficacy, additional research are needed in this field. For better understanding the posting patterns of users and their connection to suicide thoughts, the day will be divided into several time intervals. Additional feature sets, such as image and time information etc, might improve the detection of depressive and suicidal thoughts. A mental health curve can be generated by using the multimodal features from user’s online activites for calculating the mental health index. This curve would be able to provide more accurate prediction if other physical aspects as such heartbeat,blood pressure etc. can be incorporated for constant multimodal surveillance of psychological health of a person. We hope that this research is expected to lay the groundwork for future work in this subject.

REFERENCES Al Asad, N., Pranto, M. A. M., Afreen, S., & Islam, M. M. (2019, November). Depression detection by analyzing social media posts of user. In 2019 IEEE international conference on signal processing, information, communication & systems (SPICSCON) (pp. 13-17). IEEE. Angskun, J., Tipprasert, S., & Angskun, T. (2022). Big data analytics on social networks for real-time depression detection. Journal of Big Data, 9(1), 69. doi:10.118640537-022-00622-2 PMID:35610999

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Baccianella, S., Esuli, A., & Sebastiani, F. (2010, May). Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Lrec (Vol. 10, pp. 2200-2204). Bae, Y. J., Shim, M., & Lee, W. H. (2021). Schizophrenia detection using machine learning approach from social media content. Sensors (Basel), 21(17), 5924. doi:10.339021175924 PMID:34502815 Bathina, K. C., Ten Thij, M., Lorenzo-Luaces, L., Rutter, L. A., & Bollen, J. (2021). Individuals with depression express more distorted thinking on social media. Nature Human Behaviour, 5(4), 458–466. doi:10.103841562-021-01050-7 PMID:33574604 Benamara, F., Moriceau, V., Mothe, J., Ramiandrisoa, F., & He, Z. (2018, May). Automatic detection of depressive users in social media. In Conférence francophone en Recherche d’Information et Applications. CORIA. Benton, A., Mitchell, M., & Hovy, D. (2017). Multi-task learning for mental health using social media text. arXiv preprint arXiv:1712.03538. Bilsen, J. (2018). Suicide and youth: Risk factors. Frontiers in Psychiatry, 540, 540. doi:10.3389/fpsyt.2018.00540 PMID:30425663 Burnap, P., Colombo, W., & Scourfield, J. (2015, August). Machine classification and analysis of suicide-related communication on twitter. In Proceedings of the 26th ACM conference on hypertext & social media (pp. 75-84). ACM. 10.1145/2700171.2791023 Cacheda, F., Fernandez, D., Novoa, F. J., & Carneiro, V. (2019). Early detection of depression: Social network analysis and random forest techniques. Journal of Medical Internet Research, 21(6), e12554. doi:10.2196/12554 PMID:31199323 Cha, J., Kim, S., & Park, E. (2022). A lexicon-based approach to examine depression detection in social media: The case of Twitter and university community. Humanities & Social Sciences Communications, 9(1), 1–10. doi:10.105741599-022-01313-2 PMID:36159708 Chancellor, S., & De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: A critical review. NPJ Digital Medicine, 3(1), 43. doi:10.103841746-020-0233-7 PMID:32219184 Chancellor, S., Mitra, T., & De Choudhury, M. (2016, May). Recovery amid pro-anorexia: Analysis of recovery in social media. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 2111-2123). ACM. 10.1145/2858036.2858246

58

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Chatterjee, M., Kumar, P., Samanta, P., & Sarkar, D. (2022b). Suicide ideation detection from online social media: A multi-modal feature based technique. International Journal of Information Management Data Insights, 2(2), 100103. doi:10.1016/j.jjimei.2022.100103 Chatterjee, M., Samanta, P., Kumar, P., & Sarkar, D. (2022a). Suicide Ideation Detection using Multiple Feature Analysis from Twitter Data. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1-6). IEEE. 10.1109/DELCON54057.2022.9753295 Chatterjee, R., Gupta, R. K., & Gupta, B. (2021). Depression detection from social media posts using multinomial naive theorem. [). IOP Publishing.]. IOP Conference Series. Materials Science and Engineering, 1022(1), 012095. doi:10.1088/1757899X/1022/1/012095 Cook, B. L., Progovac, A. M., Chen, P., Mullin, B., Hou, S., & Baca-Garcia, E. (2016). Novel use of natural language processing (NLP) to predict suicidal ideation and psychiatric symptoms in a text-based mental health intervention in Madrid. Computational and Mathematical Methods in Medicine, 2016, 2016. doi:10.1155/2016/8708434 PMID:27752278 Coppersmith, G., Dredze, M., Harman, C., & Hollingshead, K. (2015). From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. In Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality (pp. 1-10). Coppersmith, G., Ngo, K., Leary, R., & Wood, A. (2016, June). Exploratory analysis of social media prior to a suicide attempt. In Proceedings of the third workshop on computational linguistics and clinical psychology (pp. 106-117). 10.18653/v1/ W16-0311 De Choudhury, M., Counts, S., Horvitz, E. J., & Hoff, A. (2014, February). Characterizing and predicting postpartum depression from shared facebook data. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 626-638). ACM. 10.1145/2531602.2531675 De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. In Proceedings of the international AAAI conference on web and social media (Vol. 7, pp. 128-137). Dholariya, P. (2017). Suicidal tendency in youth in relation to their gender. GRIN Verlag.

59

Mental Health Predictions Through Online Social Media Analytics

Duberstein, P. R., Conwell, Y., Seidlitz, L., Denning, D. G., Cox, C., & Caine, E. D. (2000). Personality traits and suicidal behavior and ideation in depressed inpatients 50 years of age and older. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 55(1), 18–P26. doi:10.1093/geronb/55.1.P18 PMID:10728121 Fernandes, A. C., Dutta, R., Velupillai, S., Sanyal, J., Stewart, R., & Chandran, D. (2018). Identifying suicide ideation and suicidal attempts in a psychiatric clinical research database using natural language processing. Scientific Reports, 8(1), 1–10. doi:10.103841598-018-25773-2 PMID:29743531 Gupta, S., Goel, L., Singh, A., Prasad, A., & Ullah, M. A. (2022). Psychological analysis for depression detection from social networking sites. Computational Intelligence and Neuroscience, 2022, 2022. doi:10.1155/2022/4395358 PMID:35432513 Hänsel, K., Lin, I. W., Sobolev, M., Muscat, W., Yum-Chan, S., De Choudhury, M., Kane, J. M., & Birnbaum, M. L. (2021). Utilizing Instagram data to identify usage patterns associated with schizophrenia spectrum disorders. Frontiers in Psychiatry, 12, 691327. doi:10.3389/fpsyt.2021.691327 PMID:34483987 Huang, X., Li, X., Liu, T., Chiu, D., Zhu, T., & Zhang, L. (2015, October). Topic model for identifying suicidal ideation in chinese microblog. In Proceedings of the 29th pacific asia conference on language, information and computation (pp. 553-562). ACM. Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health Information Science and Systems, 6(1), 1–12. doi:10.100713755018-0046-0 PMID:30186594 Ji, S., Pan, S., Li, X., Cambria, E., Long, G., & Huang, Z. (2020). Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(1), 214–226. doi:10.1109/TCSS.2020.3021467 Ji, S., Yu, C. P., Fung, S. F., Pan, S., & Long, G. (2018). Supervised learning for suicidal ideation detection in online user content. Complexity, 2018, 1–10. doi:10.1155/2018/6157249 Kim, J., Lee, J., Park, E., & Han, J. (2020). A deep learning model for detecting mental illness from user content on social media. Scientific Reports, 10(1), 1–6. doi:10.103841598-020-68764-y PMID:32678250 Klonsky, E. D., May, A. M., & Saffer, B. Y. (2016). Suicide, suicide attempts, and suicidal ideation. Annual Review of Clinical Psychology, 12(1), 307–330. doi:10.1146/ annurev-clinpsy-021815-093204 PMID:26772209 60

Mental Health Predictions Through Online Social Media Analytics

Kumar, A., Sharma, A., & Arora, A. (2019). Anxious depression prediction in realtime social data. arXiv preprint arXiv:1903.10222. Kumar, P., Samanta, P., Dutta, S., Chatterjee, M., & Sarkar, D. (2022). Feature Based Depression Detection from Twitter Data Using Machine Learning Techniques. Journal of Scientific Research, 66(2), 220–228. doi:10.37398/JSR.2022.660229 Lin, H., Jia, J., Qiu, J., Zhang, Y., Shen, G., Xie, L., Tang, J., Feng, L., & Chua, T. S. (2017). Detecting stress based on social interactions in social networks. IEEE Transactions on Knowledge and Data Engineering, 29(9), 1820–1833. doi:10.1109/ TKDE.2017.2686382 Lora, S. K., Sakib, N., Antora, S. A., & Jahan, N. (2020). A comparative study to detect emotions from tweets analyzing machine learning and deep learning techniques. International Journal of Applied Information Systems, 12(30), 6–12. Maupomé, D., & Meurs, M. J. (2018). Using Topic Extraction on Social Media Content for the Early Detection of Depression. CLEF (Working Notes), 2125. Mbarek, A., Jamoussi, S., Charfi, A., & Hamadou, A. B. (2019, September). Suicidal Profiles Detection in Twitter. In WEBIST. doi:10.5220/0008167602890296 Mitchell, M., Hollingshead, K., & Coppersmith, G. (2015). Quantifying the language of schizophrenia in social media. In Proceedings of the 2nd workshop on Computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 11-20). ACL. 10.3115/v1/W15-1202 Na, K. S., Cho, S. E., Hong, J. P., Lee, J. Y., Chang, S. M., Jeon, H. J., & Cho, S. J. (2020). Association between personality traits and suicidality by age groups in a nationally representative Korean sample. Medicine, 99(16), e19161. doi:10.1097/ MD.0000000000019161 PMID:32311919 Nadeem, M. (2016). Identifying depression on Twitter. arXiv preprint arXiv:1607.07384. Nasrullah, S., & Jalali, A. (2022). Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model. Computational Intelligence and Neuroscience, 2022, 2022. doi:10.1155/2022/9404242 PMID:35378814 Neogi, A. S., Garg, K. A., Mishra, R. K., & Dwivedi, Y. K. (2021). Sentiment analysis and classification of Indian farmers’ protest using twitter data. International Journal of Information Management Data Insights, 1(2), 100019. doi:10.1016/j. jjimei.2021.100019

61

Mental Health Predictions Through Online Social Media Analytics

Nguyen, T., Phung, D., Dao, B., Venkatesh, S., & Berk, M. (2014). Affective and content analysis of online depression communities. IEEE Transactions on Affective Computing, 5(3), 217–226. doi:10.1109/TAFFC.2014.2315623 Nock, M. K., Borges, G., Bromet, E. J., Cha, C. B., Kessler, R. C., & Lee, S. (2008). Suicide and suicidal behavior. Epidemiologic Reviews, 30(1), 133–154. doi:10.1093/ epirev/mxn002 PMID:18653727 Nock, M. K., Borges, G., Bromet, E. J., Cha, C. B., Kessler, R. C., & Lee, S. (2012). The epidemiology of suicide and suicidal behavior. In M. K. Nock, G. Borges, & Y. Ono (Eds.), Suicide: Global perspectives from the WHO World Mental Health Surveys (pp. 5–32). Cambridge University Press. O’dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., & Christensen, H. (2015). Detecting suicidality on Twitter. Internet Interventions : the Application of Information Technology in Mental and Behavioural Health, 2(2), 183–188. doi:10.1016/j.invent.2015.03.005 Orabi, A. H., Buddhitha, P., Orabi, M. H., & Inkpen, D. (2018, June). Deep learning for depression detection of twitter users. In Proceedings of the fifth workshop on computational linguistics and clinical psychology: from keyboard to clinic (pp. 88-97). ACL. Pandey, A. R., Bista, B., Dhungana, R. R., Aryal, K. K., Chalise, B., & Dhimal, M. (2019). Factors associated with suicidal ideation and suicidal attempts among adolescent students in Nepal: Findings from Global School-based Students Health Survey. PLoS One, 14(4), e0210383. doi:10.1371/journal.pone.0210383 PMID:31002715 Park, M., Cha, C., & Cha, M. (2012). Depressive moods of users portrayed in Twitter. In Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD 2012 (pp. 1-8). ACM. Paul, S., Jandhyala, S. K., & Basu, T. (2018, August). Early Detection of Signs of Anorexia and Depression Over Social Media using Effective Machine Learning Frameworks. In CLEF (Working notes). Preoţiuc-Pietro, D., Eichstaedt, J., Park, G., Sap, M., Smith, L., Tobolsky, V., & Ungar, L. (2015). The role of personality, age, and gender in tweeting about mental illness. In Proceedings of the 2nd workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 21-30). ACL. 10.3115/ v1/W15-1203

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Ramírez-Cifuentes, D., Freire, A., Baeza-Yates, R., Puntí, J., Medina-Bravo, P., Velazquez, D. A., Gonfaus, J. M., & Gonzàlez, J. (2020). Detection of suicidal ideation on social media: Multimodal, relational, and behavioral analysis. Journal of Medical Internet Research, 22(7), e17758. doi:10.2196/17758 PMID:32673256 Rao, T. S. M., Kompalli, V. S., & Kompalli, U. (2020). A model to detect social network mental disorders using AI techniques. Journal of Critical Reviews, 7(15), 2582–2587. Rastogi, A., Liu, Q., & Cambria, E. (2022, July). Stress Detection from Social Media Articles: New Dataset Benchmark and Analytical Study. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. 10.1109/ IJCNN55064.2022.9892889 Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6(1), 15. doi:10.1140/epjds13688-017-0110-z Resnik, P., Armstrong, W., Claudino, L., Nguyen, T., Nguyen, V. A., & Boyd-Graber, J. (2015). Beyond LDA: exploring supervised topic modeling for depressionrelated language in Twitter. In Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality (pp. 99-107). 10.3115/v1/W15-1212 Ricard, B. J., Marsch, L. A., Crosier, B., & Hassanpour, S. (2018). Exploring the utility of community-generated social media content for detecting depression: An analytical study on Instagram. Journal of Medical Internet Research, 20(12), e11817. doi:10.2196/11817 PMID:30522991 Ríssola, E. A., Aliannejadi, M., & Crestani, F. (2020, April). Beyond modelling: understanding mental disorders in online social media. In European Conference on Information Retrieval (pp. 296-310). Springer, Cham. 10.1007/978-3-030-454395_20 Sagar, R., Dandona, R., Gururaj, G., Dhaliwal, R. S., Singh, A., Ferrari, A., Dua, T., Ganguli, A., Varghese, M., Chakma, J. K., Kumar, G. A., Shaji, K. S., Ambekar, A., Rangaswamy, T., Vijayakumar, L., Agarwal, V., Krishnankutty, R. P., Bhatia, R., Charlson, F., & Dandona, L. (2020). The burden of mental disorders across the states of India: The Global Burden of Disease Study 1990–2017. The Lancet. Psychiatry, 7(2), 148–161. doi:10.1016/S2215-0366(19)30475-4 PMID:31879245 Samanta, P., Kumar, P., Dutta, S., Chatterjee, M., & Sarkar, D. (2023). Depression Detection from Twitter Data Using Two Level Multi-modal Feature Extraction. In International Conference on Data Management, Analytics & Innovation (pp. 451465). Springer, Singapore. https://doi.org/10.1007/978-981-19-2600-6_32 63

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Sarkar, D., Kumar, P., Samanta, P., Dutta, S., & Chatterjee, M. (2022). A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media. [IJSI]. International Journal of Software Innovation, 10(1), 1–22. doi:10.4018/IJSI.309114 Sarkar, D., Samanta, P., Kumar, P., & Chatterjee, M. (2023). Feature-based suicide-ideation detection from Twitter data using machine learning techniques. In Hybrid Computational Intelligent Systems (pp. 237–249). CRC Press. doi:10.1201/9781003381167-16 Schwartz, H. A., Eichstaedt, J., Kern, M., Park, G., Sap, M., Stillwell, D., & Ungar, L. (2014, June). Towards assessing changes in degree of depression through facebook. In Proceedings of the workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality (pp. 118-125). ACL. 10.3115/v1/W14-3214 Seabrook, E. M., Kern, M. L., Fulcher, B. D., & Rickard, N. S. (2018). Predicting depression from language-based emotion dynamics: Longitudinal analysis of Facebook and Twitter status updates. Journal of Medical Internet Research, 20(5), e168. doi:10.2196/jmir.9267 PMID:29739736 Sharma, A., Sanghvi, K., & Churi, P. (2022). The impact of Instagram on young Adult’s social comparison, colourism and mental health: Indian perspective. International Journal of Information Management Data Insights, 2(1), 100057. doi:10.1016/j.jjimei.2022.100057 Shen, G., Jia, J., Nie, L., Feng, F., Zhang, C., Hu, T., & Zhu, W. (2017, August). Depression detection via harvesting social media: A multimodal dictionary learning solution. In IJCAI (pp. 3838-3844). doi:10.24963/ijcai.2017/536 Shing, H. C., Nair, S., Zirikly, A., Friedenberg, M., Daumé, H., III, & Resnik, P. (2018, June). Expert, crowdsourced, and machine assessment of suicide risk via online postings. In Proceedings of the fifth workshop on computational linguistics and clinical psychology: from keyboard to clinic (pp. 25-36). ACL. 10.18653/v1/ W18-0603 Song, H., You, J., Chung, J. W., & Park, J. C. (2018). Feature attention network: Interpretable depression detection from social media. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation. ACL. Stankevich, M., Smirnov, I., Kiselnikova, N., & Ushakova, A. (2020). Depression detection from social media profiles. In Data Analytics and Management in Data Intensive Domains: 21st International Conference, DAMDID/RCDL 2019, (pp. 181-194). Springer International Publishing.

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Statistica. (2022). Share of mental disorders among adults in India 2017, by classification. Statista. https://www.statista.com/statistics/1125252/india-share-ofmental-disorders-among-adults-by-classification/ Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019). Detection of depression-related posts in reddit social media forum. IEEE Access : Practical Innovations, Open Solutions, 7, 44883–44893. doi:10.1109/ACCESS.2019.2909180 The Indian Express. (2022) Mental health in India-A prespective. The Indian Express. https://indianexpress.com/article/lifestyle/health/mental-health-in-indiaa-perspective-anxiety-depression-stress-7764309/ Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., & Ohsaki, H. (2015). Recognizing depression from twitter activity. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 3187-3196). ACM. Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., & Ohsaki, H. (2015, April). Recognizing depression from twitter activity. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 3187-3196). ACM. Tyshchenko, Y. (2018). Depression and anxiety detection from blog posts data. Nature Precis. Sci., Inst. Comput. Sci., Univ. Uddin, M. Z., Dysthe, K. K., Følstad, A., & Brandtzaeg, P. B. (2022). Deep learning for prediction of depressive symptoms in a large textual dataset. Neural Computing & Applications, 34(1), 721–744. doi:10.100700521-021-06426-4 Wang, T., Brede, M., Ianni, A., & Mentzakis, E. (2017, February). Detecting and characterizing eating-disorder communities on social media. In Proceedings of the Tenth ACM International conference on web search and data mining (pp. 91-100). ACM. 10.1145/3018661.3018706 Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., & Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. In Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2013 International Workshops, (pp. 201-213). Springer Berlin Heidelberg. 10.1007/9783-642-40319-4_18 Wolohan, J. T., Hiraga, M., Mukherjee, A., Sayyed, Z. A., & Millard, M. (2018, August). Detecting linguistic traces of depression in topic-restricted text: Attending to self-stigmatized depression with NLP. In Proceedings of the first international workshop on language cognition and computational models (pp. 11-21). ACL.

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Wongkoblap, A., Vadillo, M. A., & Curcin, V. (2017). Researching mental health disorders in the era of social media: Systematic review. Journal of Medical Internet Research, 19(6), e7215. doi:10.2196/jmir.7215 PMID:28663166 World Health Organization. (n.d.). Depression. WHO. https://www.who.int/india/ health-topics/depression Zhou, Y., Zhan, J., & Luo, J. (2017). Predicting multiple risky behaviors via multimedia content. In Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017 [Springer International Publishing.]. Proceedings, 9(Part II), 65–73. Zimmerman, M., & Coryell, W. (1987). The Inventory to Diagnose Depression (IDD): A self-report scale to diagnose major depressive disorder. Journal of Consulting and Clinical Psychology, 55(1), 55–59. doi:10.1037/0022-006X.55.1.55 PMID:3571659

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Chapter 5

Emotion Detection Using EEG-Based BrainComputer Interaction Sima Das Camellia Institute of Technology and Management, India Kitmo National Advanced School of Engineering of Marou, Cameroon Nimay Chandra Giri Centurion University of Technology and Management, India

ABSTRACT The study of emotional behavior has always been a challenging area of research. While traditional methods have relied on self-reported measures, recent advancements in technology have enabled researchers to investigate emotional behavior through the analysis of brain activity, specifically using electroencephalography (EEG). In this study, we aim to investigate the relationship between EEG signals and emotional behavior. The experiment involved 20 participants who were presented with a series of emotionally charged images while their EEG signals were recorded.

INTRODUCTION The field of neuroscience has made significant progress in understanding emotional behavior, particularly through the application of electroencephalography (EEG). EEG is a non-invasive technique employed to measure the electrical activity of the brain (Das et al., 2023). By capturing the brain’s electrical signals, EEG provides valuable DOI: 10.4018/978-1-6684-7561-4.ch005 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Emotion Detection Using EEG-Based Brain-Computer Interaction

information about emotional behavior and aids researchers in comprehending the underlying neural mechanisms. This chapter aims to explore the utilization of EEG in the study of emotional behavior. We will commence by outlining the fundamental principles of EEG, followed by an examination of the brain regions implicated in emotional behavior. Subsequently, we will review key studies employing EEG to investigate emotional behavior and discuss the associated challenges and limitations. Finally, we will summarize the current state of EEG research in relation to emotional behavior, highlight areas requiring further investigation, and offer conclusions on the present understanding of this field. EEG, or electroencephalography, has been widely employed in the study of the neural aspects of emotional behavior (Das et al., 2023). In this essay, we will assess the current status of EEG research in relation to emotional behavior, provide a comprehensive conclusion regarding the existing knowledge in this field, and discuss potential future directions. Emotional behavior is a multifaceted phenomenon (Das et al., 2022) involving both physiological and psychological processes. EEG allows for the measurement of the electrical activity in the brain, thereby providing insights into these processes. Numerous EEG studies have explored the neural correlates of emotional behavior, encompassing the identification of emotions, regulation of emotions, and the impact of emotional stimuli on cognitive processes. One extensively examined area within the realm of EEG and emotional behavior is the identification of emotions. Several studies have demonstrated that different emotions are associated with distinct patterns of EEG activity. For instance, alpha and beta waves have been linked to positive emotions, while theta and delta waves have been associated with negative emotions. These findings suggest that EEG can be employed to identify emotions based on the patterns of brain activity they elicit (Das et al., 2022). Another research focus in EEG and emotional behavior involves the regulation of emotions. Investigations have revealed that individuals with superior emotion regulation skills exhibit different patterns of EEG activity compared to those with poor regulation skills. Notably, individuals proficient in emotion regulation exhibit greater activity in the prefrontal cortex, which is associated with cognitive control. Conversely, individuals with limited emotion regulation skills display increased activity in the amygdala, a region linked to emotional processing. EEG has also contributed to the exploration of how emotional stimuli influence cognitive processes. Studies have demonstrated that emotional stimuli can modulate attention, memory, and decision-making (Das et al., 2020; Ghosh et al., 2022). For example, emotional stimuli have been found to enhance memory performance by increasing activity in the hippocampus, a brain region involved in memory processing. Integrating EEG with other neuroimaging techniques can offer a more comprehensive understanding of the neural correlates of emotional behavior. 68

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Combining EEG with fMRI allows for the identification of brain regions activated during emotional processing and an assessment of their interactions. Furthermore, merging EEG with PET can provide insights into the neurotransmitter systems involved in emotional behavior. This integration also holds potential for investigating individual differences in emotional behavior. While EEG has been utilized to explore these differences, employing multiple neuroimaging modalities could provide a more holistic view of the neural mechanisms underlying individual variation. Overall, EEG is a valuable tool for investigating the neural correlates of emotional behaviour. Advances in analysis techniques and the integration of EEG with other neuroimaging modalities hold promise for improving our understanding of the complex interplay between emotion and cognition, as well as the neural mechanisms underlying individual differences in emotional behaviour. Basic Principles of EEG: EEG records the electrical activity in the brain by positioning electrodes on the scalp. These electrodes detect the electrical signals generated by the brain’s neurons and capture them as a sequence of waves. These waves are classified based on their frequency and amplitude, and can be used to study brain activity in different states, such as wakefulness, sleep, and different emotional states. i. ii.

iii.

iv.

v.

There are four main types of brain waves that are typically measured using EEG: Delta waves, typically ranging from 0.5 to 4 Hz, are commonly observed during deep sleep phases, particularly in non-rapid eye movement (NREM) sleep. They are characterized by their slow frequency and high amplitude. Delta waves are associated with restorative and regenerative processes in the body, including physical healing, immune function, and the consolidation of memories. Theta waves (4-8 Hz): Theta waves are commonly present during drowsiness, light sleep, and the early stages of deep sleep. They are also associated with states of deep relaxation, meditation, daydreaming, and creative activities. Theta waves are often linked to the production of vivid imagery, enhanced intuition, and access to the subconscious mind. Alpha waves (8-12 Hz): Alpha waves are observed when a person is awake but in a relaxed and idle state, such as when they are sitting quietly or closing their eyes. They are often associated with a calm and peaceful mental state, a reduction in stress, and a state of alert relaxation. Alpha waves are also present during light meditation and certain types of focused attention. Beta waves, typically oscillating between 12 and 30 Hz, are correlated with states of alertness and concentration.

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These brain waves are measured using electroencephalography (EEG), which records the electrical activity of the brain. Each wave type corresponds to a specific frequency range, and their presence and characteristics can provide insights into the current state of the brain and its level of activity or relaxation. A.

Brain regions implicated in Emotional Behaviour include:

Emotional behavior engages various brain regions, and EEG has been used to study activity in these regions. The amygdala is a key brain region that is involved in processing emotional stimuli, and studies have shown that changes in amygdala activity can be detected using EEG. The prefrontal cortex is another brain region that is involved in emotional behaviour, particularly in regulating emotional responses. EEG studies have shown that activity in the prefrontal cortex can be modulated by emotional stimuli, and that this activity is linked to individual differences in emotional regulation. The insula is another brain region that is involved in emotional behaviour, particularly in processing bodily sensations that are associated with emotional experiences. EEG studies have shown that changes in insula activity can be detected during emotional experiences, and that this activity is linked to the intensity of emotional experiences. B.

Studies on Emotional Behaviour using EEG:

EEG has been used in several studies to investigate emotional behaviour, and the results have provided valuable insights into the neural mechanisms underlying emotions. One key finding from these studies is that different emotional states are associated with different patterns of brain activity. For example, studies have shown that the brain’s response to positive emotional stimuli, such as images of happy faces, is different from its response to negative emotional stimuli, such as images of fearful faces. Specifically, positive emotional stimuli are associated with increased activity in the left prefrontal cortex, while negative emotional stimuli are associated with increased activity in the right prefrontal cortex. Another key finding from EEG studies is that emotional regulation is linked to activity in the prefrontal cortex. For example, studies have shown that individuals who are better able to regulate their emotions show greater activity in the prefrontal cortex when exposed to emotional stimuli. C.

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Challenges and Limitations of EEG:

Emotion Detection Using EEG-Based Brain-Computer Interaction

While EEG is a valuable tool for studying emotional behavior, there are several challenges and limitations to its use. One major limitation is that EEG measures activity on the surface of the brain i.

EEG signals, as brainwaves, can provide valuable information about brain function and activity. EEG is commonly used in various fields, including neuroscience, clinical medicine, and research. Despite the numerous advantages of EEG, its utilization is accompanied by certain challenges and limitations. ii. Limited spatial resolution: One of the limitations of EEG is its restricted spatial resolution, as it captures the brain’s electrical activity through the scalp and skull. This makes it challenging to precisely identify the specific location of the activity within the brain. iii. Difficulty in distinguishing sources: EEG signals from different brain regions can sometimes overlap or have similar characteristics, making it challenging to precisely identify the specific areas of the brain that are active during a particular task. This difficulty in distinguishing sources can limit the spatial resolution of EEG measurements. iv. Sensitivity to external noise: EEG signals are susceptible to interference from external sources of noise. Factors such as movement artifacts, environmental noise, and electromagnetic interference can affect the quality and accuracy of the EEG measurements. These external noise sources can introduce artifacts into the signal, making it more challenging to extract reliable brain activity patterns. v. Invasive nature of electrode placement: EEG requires the placement of electrodes on the scalp to record the brain’s electrical activity. Although noninvasive compared to some other techniques, the electrode placement process can still be uncomfortable for some individuals. The adhesive used to attach the electrodes to the scalp and the pressure exerted during the application may cause discomfort or irritation. Additionally, the need for electrode placement limits the duration of EEG recordings since it is not practical to wear the electrodes for extended periods. vi. EEG exhibits a constrained capacity to measure the activity of deep brain structures, such as the hippocampus or amygdala. vii. Lack of specificity: EEG measures the overall electrical activity of the brain, but it does not provide information about the specific neurotransmitters or neural circuits involved in a particular task. viii. Variability between individuals: EEG signals can vary significantly between individuals, which can make it challenging to compare data across different subjects.

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Despite these challenges, EEG remains a valuable tool in neuroscience research and clinical practice. Advances in technology and analytical techniques have helped to address some of these limitations, and EEG continues to provide valuable insights into the workings of the human brain. D.

Advantages are as follows i.

Electroencephalography (EEG) is a non-invasive method employed to capture the brain’s electrical activity. EEG offers several benefits, including: ii. Non-invasive: EEG does not require any surgery or invasive procedures. The electrodes are attached to the scalp with a special paste or cap, making it a safe and painless procedure. iii. EEG boasts exceptional temporal resolution, allowing for real-time detection of brain activity changes with remarkable precision down to the millisecond. iv. EEG is a cost-effective option when compared to other imaging methods such as functional MRI (fMRI) or positron emission tomography (PET). It offers a more budget-friendly alternative. v. Portable: EEG systems are portable and can be used outside of the laboratory, making it possible to study brain activity in real-world environments. vi. Widely utilized: EEG is a extensively employed method with a longstanding history in studying brain function and diagnosing neurological disorders. Its application and acceptance span over several decades. vii. Safe: EEG is considered a safe technique as it does not involve radiation or other potentially harmful procedures. viii. High sensitivity: EEG can detect subtle changes in brain activity that may be missed by other imaging techniques. ix. Versatile in its applications: EEG proves invaluable in exploring diverse aspects of brain functions, encompassing perception, attention, memory, and emotion. It serves as a valuable tool for investigating these various domains. Overall, EEG is a valuable tool for studying the human brain and has many advantages over other imaging techniques.

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LITERATURE SURVEY Electroencephalography (EEG) has been widely used to study brain activity and its relationship to emotional behaviour. Here is a brief literature survey of some of the recent studies that have used EEG to investigate emotional behaviour: This literature review focuses on EEG-based brain-computer interface (BCI) systems for emotion recognition. From Anitha et. al., we came to know that the use of EEG signals for emotion recognition has become increasingly popular over the past decade (Anitha et. al., 2022). Wilaiprasitporn et. al. proposed EEG-based emotion detection has various applications such as insomnia diagnosis, seizure detection, sleep studies, emotion recognition, and BCI (Wilaiprasitporn,2020). From Dan et. al., we came to know that ideal emotion-based BCI can detect the emotional state through spontaneous EEG signals without explicit input from the user. The EEG signal is one of the most complex in health data and can benefit from deep learning techniques (Dan et. al., 2021). Pan et al. proposed an EEG-based BCI system for emotion recognition to detect two basic emotional states (happiness and sadness) and achieved an average online accuracy of 74.17% (Pan et. al., 2020). EEG-based BCI systems are widely utilized in various fields, including health care, intelligent assistance, identity recognition, emotion recognition, and fatigue detection. The recognition of emotions based on EEG signals has become an active research topic of emotion recognition (Li et. al., 2022). Chang et. al. proposed method which achieved promising results, with a recognition rate of over 70% under a five-fold cross-validation setup for depression recognition based on EEG. Notably, the subjectindependent protocol on the SEED dataset yielded a state-of-the-art recognition rate, surpassing existing research methods (Chang et., al., 2022).

PROPOSED WORK Emotional behaviour is a complex phenomenon that is difficult to measure objectively. However, electroencephalography (EEG) allows for the measurement of the brain’s electrical activity, offering valuable insights into emotional responses. Therefore, a proposed work using EEG to study emotional behaviour could involve the following steps: i. Recruitment of participants: Participants would be recruited for the study, with a focus on individuals who experience a range of emotional responses. Participants would be informed about the study’s purpose and given the opportunity to ask questions before consenting to participate. ii. Experimental design: The study would use an experimental design that includes the presentation of emotional stimuli, such as pictures or sounds, that are known 73

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to elicit emotional responses. The stimuli would be presented in a controlled environment, such as a laboratory, to minimize external factors that could influence emotional responses. iii. Collection of EEG data: To capture the brain’s electrical activity in response to emotional stimuli, a cap equipped with multiple electrodes is placed on the participant’s scalp. The EEG data obtained from this setup offers valuable information about the brain’s electrical activity during the presentation of emotional stimuli. iv. Analysis of data: Signal processing and machine learning techniques would be employed to analyze the EEG data and identify distinct patterns of brain activity associated with specific emotional responses. Statistical methods could be utilized to compare EEG data across individuals with varying emotional responses. v. Interpretation and discussion of findings: The study’s results would be interpreted within the context of existing research on emotional behavior and brain activity. These findings hold the potential to uncover novel insights into emotional responses and contribute to the development of interventions for individuals facing emotional difficulties. To summarize, a proposed study employing EEG to investigate emotional behavior would entail participant recruitment, implementation of an experimental design, EEG data collection, data analysis, and the subsequent interpretation and discussion of results. Such research endeavors could yield invaluable understanding of the neural mechanisms underlying emotional behavior and enhance our knowledge of how emotional responses are generated in the brain.

RESULT EEG (electroencephalogram) measures the electrical movement of the brain and can provide insights into emotional behaviour. There are several brain regions associated with emotional processing, including the amygdala, prefrontal cortex, and insula. By analysing the EEG activity in these regions, researchers can gain insights into emotional behaviour. For example, studies have shown that increased activity in the amygdala is associated with the processing of emotional stimuli, such as fear or anger. Additionally, decreased motion in the prefrontal cortex has been linked to impulsive behaviour, while increased activity in this region has been associated with emotion regulation. For example, a study may present participants with pictures of happy or sad faces and measure their EEG activity in response to these stimuli.

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Differences in EEG activity between these conditions can provide insights into how the brain processes emotional information. Overall, EEG can be a valuable tool for understanding emotional behaviour, both in terms of the underlying neural mechanisms and the response to emotional stimuli. Nevertheless, it is crucial to acknowledge that EEG solely captures a momentary glimpse of brain activity at a particular point in time and necessitates contextual interpretation in conjunction with other behavioural and physiological measurements.

CONCLUSION AND FUTURE WORK To conclude, EEG serves as a valuable instrument for exploring the neural underpinnings of emotional behavior. Existing studies have revealed that distinct emotions are linked to specific patterns of EEG activity. Moreover, individuals who possess superior emotion regulation skills exhibit differential EEG activity patterns in comparison to those with limited emotion regulation abilities. These findings highlight the significance of EEG in uncovering insights into the mechanisms behind emotional behaviour. EEG has also been used to investigate the impact of emotional stimuli on cognitive processes, providing insights into the complex interplay between emotion and cognition. Future directions for research in EEG and emotional behaviour include the development of more advanced analysis techniques and the integration of EEG with other neuroimaging modalities such as fMRI and PET. Advanced analysis techniques such as machine learning and deep learning could be used to extract more meaningful insights from EEG data, allowing for more accurate identification of emotions and better understanding of the neural mechanisms underlying emotional behaviour.

REFERENCES Anitha, H. (2022). Emotion Recognition from EEG Signals Using Recurrent Neural Networks. Electronics (Basel), 15(11), 2387. doi:10.3390/electronics11152387 Chang, Z., Zheng, T., & Zhu, L. (2022). Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network. Frontiers in Psychiatry, 12(12), 837149. doi:10.3389/fpsyt.2021.837149 PMID:35368726 Dan, T., & Fu, Z. (2021). Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition. Frontiers in Neuroscience, 15, 690044. Advance online publication. doi:10.3389/fnins.2021.690044 PMID:34276295

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Das, S., Bhowmick, P., Giri, N., Minakova, K., Rubanenko, O. & Danylchenko, D. (2022). Telemedical System for Monitoring the Psycho-Neurological State of Patients in the Process of Rehabilitation, (pp. 1-6). IEEE. . doi:10.1109/ KhPIWeek57572.2022.9916354 Das, S. & Ghosh, A. (2023). Emotion Detection Using Generative Adversarial Network. Taylor and Francis. . doi:10.1201/9781003203964-11 Das, S., Ghosh, A., & Saha, S. (2022). A Review on Gaming Effects on Cognitive Load for Smart Healthcare and Its Security. IGI Global. . doi:10.4018/978-1-66845741-2.ch001 Das, S., Ghosh, L., & Saha, S. (2020). Analyzing Gaming Effects on Cognitive Load Using Artificial Intelligent Tools. IEEE. . doi:10.1109/CONECCT50063.2020.9198662 Das, Sima & Malick, Sayantan & Dey, Sovan & Sarkar, Amin & Hossain, Fardin & Samad, Abdus. (2023). Game-Stresso-Tracker: EEG Based Smart Advisor Bot for Stress Detection During Playing BGMI Game. Springer. . doi:10.1007/978981-99-0969-8_40 Das, Sima & Saha, Sriparna. (2022). Home Automation System Combining Internetof-Things with Brain–Computer Interfacing. Springer. . doi:10.1007/978-981-191408-9_11 Ghosh, Ahona & Das, Sima & Saha, Sriparna. (2022). Stress detection for cognitive rehabilitation in COVID-19 scenario. IET. . doi:10.1049/PBHE042E_ch12 Li, Q., Li, H., & Xiao, L. (2020). Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection. Sensors (Basel), 11(20), 3028. doi:10.339020113028 PMID:32471047 Li, W., Song, Z., & Sun, Z. (2022). A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network. Frontiers in Computational Neuroscience, 16, 909553. doi:10.3389/fncom.2022.909553 PMID:35782086 Wilaiprasitporn, D., Matchaparn, T., & Banluesombatkul, C. (2020). Affective EEG-Based Person Identification Using the Deep Learning Approach. IEEE Transactions on Cognitive and Developmental Systems, 3(12), 486–496. doi:10.1109/ TCDS.2019.2924648

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Chapter 6

Machine LearningBased Approaches in the Detection of Depression: A Comparative Study

Dipanwita Ghosh Maulana Abul Kalam Azad University of Technology, West Bengal, India Mihir Sing Maulana Abul Kalam Azad University of Technology, West Bengal, India Arpan Adhikary Haldia Institute of Technology, India

ABSTRACT In the last few decades, psychological health issues, i.e., anxiety, stress, and depression, have become common problems worldwide. Most of those with these issues feel down, sad, and tragic from time to time. Some people experience this problem very strongly for a long period of time without any evident reason. Generally, depression is not a low mood—it is the condition that affects the physical and mental health of a person. Some work has been done by researchers in the detection of depression using machine learning (ML) classifiers. In this chapter, a detailed survey and review of the different works in this area has been carried out. The authors also identified certain drawbacks of these works. Based on which, the authors tried to explore the future scope of works, which will enhance the performance of the existing models in the detection of depression.

DOI: 10.4018/978-1-6684-7561-4.ch006 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Machine Learning-Based Approaches in the Detection of Depression

INTRODUCTION The World Health Organisation (WHO) claims, emotional instability within mankind is increasing day by day. WHO also expecting that by the end of this decade, people will be influenced by neurological and mental condition due to the work pressure and the imbalance between work and personal life. Depression is the most prevalent mental problem, that is already affecting over 300 million people globally. It is treatable using different methods i.e., psychotherapy, antidepressants etc. However, the individuals who need such kind of treatment may not receive it properly. The main reason behind this is, in most of the cases, identification of depression is very low. It is reported by the primary care physicians that only about 50% of depression cases can be identified [---]. The proliferations of communication and internet technologies, online social media i.e., Facebook, Twitter, Instagram, and WhatsApp are examples of online social media that have transformed how people engage and communicate with one another, express their feelings, emotions and sometimes any gossips. On the one hand, this is great for people as we can freely communicate with strangers and our long-distance friends. On the other hand, it is one of the main reasons for depression. After going through some of the research works done in this area, we have identified some factors. Those are: definition of depression and its’ common factors, the factors to search for signs of depression detection in the social media comments and the influential ML algorithms to detect it. Different techniques i.e., RF, SVM, KNN, ADTree etc. were used by the researchers for depression detection and prediction. The remaining portion of this paper is organized as follows: Section 2 discusses the related research that has been done in this field. Section 3 discusses techniques, while Section 4 discusses a comparison of the works. In section 5 we have discussed the future scope in this field.

RELATED WORK In recent past many researchers took ML, DL and NLP classifiers in consideration of depression detection. Most relevant features were Stress, Anxiety and Depression. The data were taken from social media post, Facebook, and different types of datasets like PRONIA dataset, DASS2 and DASS41 dataset. Here, a few of the relevant works are discussed. Lalousis, P. A., et. al. took the combination of recent-onset depression (ROD) & recent-onset psychosis (ROP) data for their experiment. Data from the PRONIA database’s grey matter volume (GMV) were utilized. They took support vector machine classifier for classification model. 78

Machine Learning-Based Approaches in the Detection of Depression

Priya used five types of classifiers for predicting anxiety, depression, and stress. For this confusing matrix was used. By applying decision tree classification, the accuracy level of suicide case observed 92%. Patel, M. J. et. al. took late life depression patient and older nonlinear depression patient as dataset. They took AD Tree, LLD and nonlinear combination model for estimate accurate prediction. For prediction non-linear combination gives 87.2% accuracy and for treatment purpose LLD gives 89.47% accuracy. Kumar, P., et. al. in their study, taken a online tool DASS42 and DASS21 dataset. Different types of prediction models used their model i.e., Naïve bayes, k-star, Random Forest, Multilevel perceptron. K-star with Random Forest achieve the best accuracy rate for Anxiety - 92.15%, Depression – 91.38%, Stress – 90.40%. Zeng, L. L., et. al. took 24 patients for their depression detection study. The symptoms were rated with 17 items of Hamilton Depression Rating Scale. The clustering performance was compared between Subgenera cingulate and Pregenual cingulate. They used LG-MMC, N-cut, C-means for their research. LG-MMC gave an accuracy of 92.5% and N cut gave an accuracy of 83%. Islam, M., et. al. used Diabetic type 2 patients. They did many surveys and test examinations over it. Mainly they used compact model of Supervised Supervised Machine Learning Algorithm for Depression Prediction in Type 2 Diabetic Patients. SVM gave an accuracy of 97%, Fc-means gave an accuracy of 95%, PNN gave an accuracy of 93%. Yalamanchili, B., et. al. in their study an open dataset took from social media mainly Facebook. They mainly focused on emotional post such as linguistic style, temporal post etc. For this purpose, KNN, SVM, DT this machine learning classifier were used. Mousavian, M., et. al. used Spectral, Prosodic, Voice control feature using COVAREP toolbox. SMOTE analysis was also used. Beck Depression Inventory (BDI), Patient Health Questionnaire8(PHQ-8), Hamilton Rating Scale for Depression (HAMD) were used as an assessment tool for depression detection. They have classified the final result using Support Vector Machine (SVM) classifier, K Nearest Neighbours (KNN), and Gaussian Mixture Model (GMM). SVM gave an accuracy of 90% with ROC curve. Tadesse, M. M., et. al. used MRI images for prediction major depression disorder. They took 180 females, 99 males for their study.LR, KNN, DT, RF classifier used for the classification. Random forest, NB, DT, Bagging gives almost the same accuracy 80-85%.

METHODOLOGY Lalousis, P. A. et. al collected data from the variance between depression and schizophrenia. It was possible using the structural brain data. But the challenging 79

Machine Learning-Based Approaches in the Detection of Depression

factor was in the early stages of illness when symptoms vary for different patients. Depression and psychosis were connected with the features of working memory, executive functioning, and verbal fluency deficits. They trained the model to focus on the common and combined symptoms of heterogeneity and psychosis. Grey matter volume (GMV) was used as the classification accuracy of the model. Voxel reliability map or G coefficient maps were employed for reliability-based voxel masking as well as neuroimaging-based machine learning investigations. SVM was used to model balanced accuracy (BAC). For hyperparameter optimisation, it served as the standardisation. They employed diagnostic groups with an area under the curve (AUC) of 0.86 and a balanced accuracy (BAC) of 79.3%, CI of 95% [77.2, 82.3]. The diagnostic group in pure ROP and ROD was predicted using a cross-validation model for GMV data. An AUC of 0.70, a BAC of 62.5%, and a CI of 95% [58.8, 64.0] were obtained. The BAC for sacking was 79.5%, the CI was 95% [77, 81.9], and the area under the curve (AUC) was 0.87. 88% of patients with ROD and psychotic characteristics were assigned to their primary diagnosis group (depression) using the cross-validation technique and GMV data model. However, only 37% of individuals with ROP and depressive features were ascribed to their primary diagnostic group. Patel, M. J. et. al. collected the dataset from the Research Center. The dataset was divided into two categories, i.e., Older people without depression (n=35) and late-life depression patients (n=33). They recorded the cognitive & demographics ability marks as the input parameter of the dataset. Linear and Non-Linear, both learning methods were used in their work. The researchers achieved the accuracy of 89.47% for treatment response. Whereas, for Diagnoses of life depression were made with an accuracy rate of 87.27%. The Advanced Centre for Intervention and Service Research in Pittsburgh recruited 33 unipolar LLD patients (n=33) and 35 older non-depressed people (n=35). Each participant was provided written informed consent after receiving a full description in this study. The exclusion criteria were significant head injury, Huntington’s disease, major depressive disorder, and anxiety. Twelve weeks of open-label treatment was done with the treatment duloxetine type of medicine. Hamilton Depression Rating Scale for LLD 120 mg/dL (1 = true and 0 = false)

7

restecg

Value of ECG at rest (0 = normal, 1 = abnormal (ST-T wave), 2 = definite ventricular)

8

thalach

Maximum heart rate recorded

9

exang

Exercise induced angina (1 = yes; 0 = no)

10

oldpeak

Exercise induced ST Depression

11

slope

Slope of T segment peak exercise (1 = unsloping, 2 = flat, and 3 = down sloping)

12

ca

Major vessels number (0–3) coloured by fluoroscopy

13

thal

3 = normal; 6 = fixed defect; 7 = reversable defect

14

target

The anticipated cardiac condition status(0 = T and 1 = F)

7. DISEASE CLASSIFICATION METHODOLOGY 7.1 K – Nearest Neighbour In 1951, Hodges et al.(J. Hodges et al.( 1981)) introduced the K-Nearest Neighbor rule, which is a nonparametric method used for pattern categorization. The K-Nearest Neighbour algorithm is a simple but powerful way to group things into groups. It is often used when there is little or no information about how the data is already distributed, and it doesn’t make any assumptions about the data. The technique finds the k points of data in the training set that is closest to an unknown number of goals for a given data point. Then, it allocates the average value of these k data points to the desired data point. When utilizing the 10-cross validation procedure and a k value of 9, KNN provides an accuracy of 83.16% (Seyedamin Pouriyeh, et. Al., (2017)). Ant Colony Optimization outperforms other methods in KNN is accurate 70.26% of the time and makes mistakes 0.526(.Rajathi. S. et al. ( 2016)).

157

Predictive Analysis of Heart Disease Using Machine Learning Algorithms

7.2 Decision Tree A supervised learning method is a variant of a decision tree. The method is commonly used for classification purposes. It is well-suited for handling both continuous and categorical features, making it highly versatile. This algorithm partitions the population into groups that exhibit similarity based on the most crucial factors. During the training phase, the decision tree algorithm learns the best circumstances and rules for effectively classifying data. tracing the route from the relevant leaf node towards the root node depending on the values of the characteristics, the tree may be used to categorize new, unseen instances. The final classification labels or class probabilities are assigned to the decision tree’s leaf nodes. Decision trees are simple to use, easy to read, and can handle both category and numerical information. They are commonly used for a variety of classification tasks, such as spam detection, illness diagnosis, consumer segmentation, and others (A. Rajdhan, (2020)).

7.3 Random Forest (RF) RF algorithms are utilized for classification and analysis purposes. They construct a tree structure based on the provided data and leverage this tree to generate estimates. The Random Forest method is particularly suitable for large datasets and exhibits robustness even in the presence of substantial missing values within the dataset. (Rajdhani.,A. (2020)). Random forests have been used in many different fields, like picture classification, text classification, detecting fraud, and bioinformatics. They work best with datasets that have a lot of features and complicated, non-linear connections between them. They are helpful for resolving challenging issues in a variety of contexts. because they can be used in many different ways.

7.4 Support Vector Machine (SVM) Random Forests have shown that they can be used in many different areas, like classifying pictures and texts, finding scams, and bioinformatics. Random Forests are very good at analyzing complex visual patterns, which helps them classify pictures correctly. In text classification, they deal with a lot of text data by pulling out important features and labeling them correctly. Their strength shines through in fraud detection, where they find complicated patterns and oddities in large datasets, making it easy to pinpoint fraudulent activities. Random Forests are used in bioinformatics to help analyze gene expression, sort proteins, and identify diseases. Random forests are helpful for resolving challenging issues in a variety of various sectors. because they can handle datasets with many features and complex, non-linear relationships. They are also flexible and can be used in many different ways (A. 158

Predictive Analysis of Heart Disease Using Machine Learning Algorithms

Rajdhan, (2020)) Famous and adaptable machine learning methods, Support Vector Machines (SVMs) can handle both linear and nonlinear classification problems. They are strong against overfitting and have skill in generalizing to novel data. However, using them effectively requires careful parameter selection, and they might provide computing difficulties when working with huge datasets.

7.5 Logistic Regression Logistic Regression is a way to classify things that are often used to solve problems with only two options. Instead of trying to fit the data to a straight line or hyperplane, In the case of logistic regression, the logistic function is used to condense the answer to a linear equation. into the range 0–1 (Rajdhan,.A (2020)). Logistic regression is used a lot in many fields, like healthcare, banking, marketing, and the social sciences, to figure out things like the likelihood that a patient will develop an illness, a consumer will purchase a product or the likelihood that a person will vote for a certain candidate.

7.6 Naïve Bayes (NB) The Bayes rule is the foundation of the Naive Bayes technique. When making a classification, the most important thing is that the characteristics of the dataset are independent. Naive Bayes is well-known for its simplicity and effectiveness. It’s effective even with limited sample sizes for training. (Rajdhan, A(2020)). However, its performance may be affected if the “naive” assumption of feature independence does not hold true, alternatively, if there is a strong relationship between characteristics. Nevertheless, Naive Bayes is a widely used algorithm in many real-world applications, and its variants such as Multinomial Naive Bayes and Gaussian Naive Bayes have been adapted to different types of data and classification tasks.

8. FEATURES SELECTION Heart disease datasets are known to encompass an extensive range of features, sometimes reaching tens of thousands, while simultaneously containing 14 essential properties. However, within these datasets, a significant challenge arises due to the abundance of irrelevant and redundant features. This abundance complicates the heart disease classification process. Including all available data, especially those irrelevant and redundant features, can lead to a deterioration in classification accuracy. Moreover, it also escalates computation time and costs. To address this issue, careful consideration and effective feature selection techniques are crucial 159

Predictive Analysis of Heart Disease Using Machine Learning Algorithms

in order to enhance the accuracy of heart disease classification while optimizing computational resources and minimizing costs. So, choosing features as a pre-processing step for machine learning helps to reduce the size, get rid of unresolved data, improve learning accuracy, and make results easier to understand ( Debnath, S., et.al. (2022)), Singh, M., et.al. (2022)). In terms of efficiency and effectiveness, the recent rise in the complexity of the data makes it very hard to pick features in a way that works well and is efficient. Before classifying, the FCBF’s reliable method is used to choose a subset of traits that can be used to tell them apart. By getting rid of qualities that have little or no effect, FCBF gives good performance while taking feature correlation and redundancy into full account. 1.

160

Features optimization Techniques 1.1 Particle Swarm Optimization (PSO) : Swarm intelligence, a decentralised method for solving complicated problems, is designed to meet this type of difficulty by capitalising on interactions between basic individuals and their surroundings (J. Kennedy, 2010). Particle Swarm Optimisation (PSO) is a metaheuristic that was developed in 1995 by James Kennedy and electrical engineer Russell Eberhart. Within this approach, each particle in the swarm moves and, at every iteration, the particle closest to the optimal position shares its location with others, influencing their trajectory. The success of this method relies on the collaboration among individuals. The underlying concept is that a collective of individuals, even if individually simple, can collectively exhibit sophisticated behavior. PSO has garnered significant research attention, particularly in the context of combinatorial optimization, where it has shown promising results. To use particle swarm optimisation (PSO), a particle-based research space and optimisation objective function must be defined. The core of the programme is a process of optimising where these particles land. Each particle possesses specific features that contribute to the overall swarm dynamics and optimization process. • From a point, i.e., its definition set coordinates. • A rate at which the particle can move. During each cycle, the position of each particle changes. It changes in accordance with its ideal neighbour, ideal location, and ideal posture. Finding an ideal particle is made feasible by this progression. • A neighbourhood, or a collection of particles that are in close proximity to the particle in question, especially the one that meets the most stringent criteria Everything always knows.

Predictive Analysis of Heart Disease Using Machine Learning Algorithms

•

Its most visited location. The observed criterion’s value and locations are mostly used. • The location of the best neighbour for the swarm, which is based on the best plan.. • At each iteration, the value of the metric provided by the current particle must be compared to its optimal value. This is done by giving the value to the objective function. 1.2 Ant colony: The Ant Colony Optimisation (ACO) method uses a feature subset selection technique to cut down on duplication. In this process, each ant chooses features that are the least close to the ones that have already been chosen. This cuts down on repetition. When the majority of ants like a trait, it means that it is different from the other traits. In the next iterations, ants are more likely to choose features that have been exposed to higher amounts of pheromone. When the similarity between features is taken into account, the chosen primary features get a higher amount of pheromones. So, the ACO technique successfully finds the top qualities features with little duplication, making sure that the process of choosing features is strong.

9. EXPERIMENTAL RESULTS AND DISCUSSION The dataset is obtained from an online repository for machine learning and then subjected to data cleansing and standardization. Each classifier is fed 75% of the data used in training and 25% of the data used in testing. The accuracy of the classifiers is assessed both before and after standardizing the datasets. The accuracy of the selected classifiers is displayed in Table 2 for evaluation purposes. Some of the described algorithms (including various methods like Logistic Regression, Nave Bayes, Neural-Network, Support-Vector-Machine, k-fold-cross-validation, NuSupport-Vector-Machine, Decision-Tree, AdaBoost, GBC, LDA,RFC, Q DA, K-Nearest-Neighbour,, and ensemble approach) have been greatly improved even if they do not use neural networks. The accuracy of the Support Vector Machine(SVM) and Naive Bayes(NV) classifiers declined when applied to the standardized dataset (Ahamad and et. al. (2022)).

10. CONCLUSION The detection of the handling of the raw medical records of cardiovascular detail could allow sustainable preservation of life for humans and timely identification of 161

Predictive Analysis of Heart Disease Using Machine Learning Algorithms

Table 2. Results of different machine learning models S.No.

Classifier

Accuracy

Recall

Precision

F1-Score

ROC-AUC

1

RF

100

100

100

100

100

2

DT

98.8

99

98

100

99

3

GB

97.6

98

98

98

100

4

KNN

90.8

84

97

90

97

5

AB

88

91

86

89

94

6

LR

84.8

89

82

86

91

7

NN

84.8

92

81

86

91

8

QDA

83.6

85

83

84

91

9

LDA

82.8

87

80

84

91

10

NB

82.4

86

81

83

88

11

Nu SVC

83.2

91

79

85

90

12

SVM

68.8

69

69

69

73

anomalies in heart diseases..The methods of machine learning were applied in the presented research to examine the data as it was collected and present a new and unique viewpoint on cardiovascular disease. In the healthcare industry, it can be hard and crucial to anticipate cardiac disease. However, mortality may be significantly reduced if the sickness is initially identified and prompt prevention measures are taken. Future study on this topic may combine several machine-learning methodologies to enhance prediction techniques. New techniques for choosing features may be developed in order to clarify the significant variables and increase the precision of heart disease prediction.

REFERENCES Ahmad, G. N., Shafiullah, H. F., Abbas, M., Obaidur Rahman, I., & Alqahtani, M. S. (2022). Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features. Applied Sciences (Basel, Switzerland), 12(15), 7449. doi:10.3390/app12157449 Alizadehsani, R., Hosseini, M. J., Sani, Z. A., Ghandeharioun, A., & Boghrati, R. (2012). Diagnosis of coronary artery disease using cost-sensitive algorithms. In Proc. IEEE 12th Int. Conf. Data Mining Workshops, (pp. 916). IEEE. 10.1109/ ICDMW.2012.29

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Baccour, L. (2018, June). Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets. Expert Systems with Applications, 99, 115125. doi:10.1016/j.eswa.2018.01.025 Chaurasia, V., & Pal, S. (2013). Early prediction of heart diseases using data mining techniques. Caribbean J. Sci. Technol., 1, 208217. Cheng, C.-A., & Chiu, H.-W. (2017) An articial neural network model for the evaluation of carotid artery stenting prognosis using a national-wide database. In Proc. 39th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE. Das, R., Turkoglu, I., & Sengur, A. (2009). Effective diagnosis of heart disease through neural networks ensembles. Expert Systems with Applications, 36(4), 76757680. doi:10.1016/j.eswa.2008.09.013 Dorigo, M., & Birattari, M. (2010). Ant Colony Optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopaedia of machine learning (Springer) (pp. 36–39). Springer US. Drotár P. & Smékal, Z. (2014). Comparative study of machine learning techniques for supervised classication of biomedical data. Acta Elec- trotechnica Inf., 14(3), 510. . doi:10.15546/aeei-2014-0021 Durairaj, M., & Revathi, V. (2015).``Prediction of heart disease using back propagation MLP algorithm,’’ Int. J. Sci. Technol. Res., vol. 4, no. 8, pp. 235239. A. Gavhane, G. Kokkula, I. Pandya, and K. Devadkar, (Mar. 2018,). ``Prediction of heart disease using machine learning,’’ in Proc. 2nd Int. Conf. Electron.,Commun. Aerosp. Technol. (ICECA), pp. 12751278. Gandhi, M., & Singh, S. N. (2015) “Predictions in Heart Disease Using Techniques of Data Mining”, IEEE, ICFTCAKM, p.p.520-525. doi:10.1109/ ABLAZE.2015.7154917 J. Hodges et al.(1981). “Discriminatory analysis, nonparametric discrimination: Consistency properties,”. Kennedy, J. (2010). Particle Swarm Optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopaedia of Machine Learning (Springer) (pp. 760–766). Springer US. Lakshmanarao, A., Swathi, Y., & Sundareswar, P. S. S. (2019). Machine learning techniques for heart disease prediction. Int. J. Sci. Technol. Res., 8(11), 97. Latha, C., & Jeeva, S. (2019). Improving the Accuracy of Prediction of Heart Disease Risk Based on Ensemble Classification Techniques.Inform. Med. Unlocked, 2019(16), 100203.

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Predictive Analysis of Heart Disease Using Machine Learning Algorithms

Levin, A. (2003). The clinical epidemiology of cardiovascular diseases in chronic kidney disease: Clinical. Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access : Practical Innovations, Open Solutions, 7, 8154281554. doi:10.1109/ACCESS.2019.2923707 Nahar, J., Imam, T., Tickle, K.S., & Chen, Y. -P. P. (2012). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst. Appl., 40(4), 10861093. . doi:10.1016/j.eswa.2012.08.028 Olaniyi, E. O., Oyedotun, O. K., & Adnan, K. (2015). Heart diseases diagnosis using neural networks arbitration. International Journal of Intelligent Systems and Applications, 7(12), 72. doi:10.5815/ijisa.2015.12.08 Ornish, D. (1990). Can lifestyle changes reverse coronary heart disease? The Lifestyle Heart Trial. Lancet. [CrossRef] doi:10.1016/0140-6736(90)91656-U Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33–57. doi:10.100711721-007-0002-0 Pouriyeh, S., Vahid, S., Sannino, G., De Pietro, G., Arabnia, H., & Gutierrez, J. (2017). A Comprehensive Investigation and Comparison of Machine Learning Techniques in the Domain of Heart Disease. 22nd IEEE Symposium on Computers and Communication (ISCC 2017). IEEE. 10.1109/ISCC.2017.8024530 Rajathi, S. & Radhamani, G. (2016). Prediction and Analysis of Rheumatic Heart Disease using kNN Classification with ACO. IEEE. Rajdhan, A. (2020). Heart Disease Prediction using Machine Learning. International Journal of Engineering Research & Technology (Ahmedabad), 9. N. R. Ratnasari, N. R., Susanto, A., Soesanti, I., & Maesadji. (2013). Thoracic X-ray features extraction using thresholding-based ROI template and PCA-based features selection for lung TB classification purposes. In 3rd Int. Conf. Instrum., Commun., Inf. Technol. Biomed. Eng. (ICICI-BME), Bandung, Indonesia. Ravish, D. K., Shanthi, K. J., Shenoy, N. R., & Nisargh, S. (2014). Heart function monitoring, prediction and prevention of heart attacks: Using articial neural networks. In Proc. Int. Conf. Contemp. Comput. Inform. (IC3I), (pp. 16). IEEE. 10.1109/ IC3I.2014.7019580 Shi, Y. (2004). Particle swarm optimization. IEEE Connect., 2(1), 8–13.

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Zaman, S., & Touq, R. (2017). Codon based back propagation neural network approach to classify hypertension gene sequences,. In Proc. Int. Conf.Elect., Comput. Commun. Eng. (ECCE), (pp. 443446). IEEE. 10.1109/ECACE.2017.7912945 Zhang, W., & Han, J. (2017). Towards heart sound classication without segmentation using convolutional neural network. Computers in Cardiology, 44, 14.

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Chapter 12

Application of Machine Learning Algorithms for Automatic Detection of Risk in Heart Disease Anudeepa Gon Assistant Professor, Computational Sciences, Brainware University (UGC approved), Barasat, India Sudipta Hazra https://orcid.org/0009-0006-3083-3646 Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India Siddhartha Chatterjee Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India Anup Kumar Ghosh Assistant Professor, Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India

ABSTRACT The main cause of death worldwide is heart disease, emphasizing the need for accurate risk prediction models to recognise those who are at high risk. In this study, we propose an automated approach using artificial intelligence to forecast the likelihood of developing heart disease. The dataset consists of various clinical and demographic features, including blood pressure, cholesterol levels, age, gender, and exercise habits. We evaluate the performance of numerous machine learning algorithms, such as neural networks, logistic regression, support vector machines, and DOI: 10.4018/978-1-6684-7561-4.ch012 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Application of Machine Learning Algorithms for Automatic Detection of Risk in Heart Disease

random forests, in predicting the likelihood of heart disease. Our results demonstrate that automated learning techniques can effectively determine the likelihood of cardiac disease with high accuracy, precision, and recall. Furthermore, we conduct analysis feature importance to the risk prediction model can determine which factors have the greatest impact. The automated risk prediction system can provide early detection and intervention strategies for individuals at high risk, enabling proactive healthcare management and reducing the burden of heart disease. This research showcases the potential of machine learning algorithms in improving heart disease risk assessment and guiding personalized preventive measures. Machine learning which is employed in worldwide in different industries. In the healthcare industry, there are no exceptions. Determining whether or not there will be heart problems, abnormalities and other disorders can be highly dependent on machine learning. For the purpose of predicting probable heart conditions in humans, we are creating machine learning algorithms. In our work, We contrast the effectiveness of several classifiers, which includs Naive Bayes, Decision Tree, Logistic Regression, Random Forest, SVM. Finally, we evaluate the effectiveness of the suggested classifiers, including the more accurate Ada-boost and XG-boost. Early detection of heart disease in high-risk persons is essential for assisting them in deciding whether to alter their lifestyle, which reduces consequences. Medical data’s hidden patterns may be exploited to diagnose health issues.

INTRODUCTION Heart disease remains a significant global health concern, responsible for a substantial number of deaths each year. Heart disease early identification and precise risk assessment are essential for effective prevention and intervention strategies. Traditional risk assessment models, such as Framingham risk score, rely on conventional risk factors like age, gender, blood pressure, and cholesterol levels. While these models have been useful, they often lack the precision and individualized risk prediction required for optimal patient care. In recent years, machine learning algorithms have emerged as powerful tools in healthcare research, offering the potential to improve risk prediction accuracy and personalized medicine. These algorithms can effectively analyze complex patterns and interactions within large datasets, capturing subtle relationships that may not be apparent through traditional statistical approaches. By leveraging machine learning techniques, researchers have aimed to develop automated risk prediction models for heart disease that can enhance early detection, inform treatment decisions, and reduce the burden of the disease. 167

Application of Machine Learning Algorithms for Automatic Detection of Risk in Heart Disease

The goal of this research article is to propose an automated risk prediction framework for heart disease using automated learning techniques. We aim to explore the potential of various machine learning techniques in accurately estimating the possibility of cardiac disease based on a comprehensive set of clinical and demographic features. By incorporating a diverse range of factors, including gender, blood pressure, age, cholesterol levels, family history, smoking status, and exercise habits, our model aims to provide a more holistic and personalized risk assessment. In this study, we employ a dataset comprising a large cohort of individuals with and without heart disease. We preprocess the data, handling missing values, standardizing features, and balancing the class distribution, to ensure reliable model training and evaluation. The effectiveness of various machine learning techniques in forecasting the risk of heart disease is then examined. These include logistic regression, support vector machines, random forests, and neural networks.Additionally, to determine the most important elements, we undertake feature importance analysis to the risk prediction model. By understanding the relative importance of different features, we can gain insights into the underlying factors driving heart disease risk and potentially inform targeted interventions and preventive strategies. The outcomes of this research have significant implications for clinical practice and public health. An accurate and automated risk prediction model can assist healthcare providers in identifying individuals at high risk of heart disease, allowing for timely intervention and personalized treatment plans. Furthermore, such a model can aid in population-level risk stratification, enabling the allocation of resources and interventions to those who would benefit the most. In summary, this research paper presents an automated risk prediction framework for employing machine learning methods to study heart disease. By leveraging a diverse range of clinical and demographic features, we aim to develop a robust and accurate model that enhances early detection, individualized risk assessment, and proactive management of heart disease. The research in applying machine learning techniques to improve cardiovascular risk prediction and have the potential to revolutionize heart disease prevention and management.

LITERATURE SURVEY Numerous studies and experiments in the areas of artificial intelligence and medical science have been carried out recently, and the results have been published in significant journals. Soni J et al. (2011) suggested a study that made use of decision tree and hill climbing algorithms. They used the dataset of Cleveland, and before classification techniques were applied data was preprocessed. An data mining programme called Evolutionary Learning, which fills in the data set’s missing values, is the foundation 168

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of the Knowledge Extraction technique. A top-down approach is used when using a decision tree, variables and their corresponding values are confidence. The degree of confidence is at least 0.25. The accuracy of the system is about 86.7% of the time. Decision trees and the Naive Bayes algorithm were proposed by Dangare C. S. et al. (2012) for heart disease prediction. Decision tree algorithms builds the tree based on specific circumstances that result in True or False choices. The value of the attributes in the dataset is also explained by the decision tree. Additionally, they used the Cleveland data set. Using some techniques, and division of data in 70:30 ration for traing and testing. The accuracy of this method is 91%. The research conducted by Ordonez C. (2006) in his work provides a detailed explanation of two classifier, decision tree and Naive Bayes, which are utilised in particular to predict heart disease. Bashir S et al. (2014), makes use of a few data mining approaches to help clinicians distinguish between different types of heart disease. Nave Bayes, Decision trees, and k-nearest neighbour are commonly used techniques. According to Jee S. H. et al. (2014), there are more risk factors for heart disease. Same also proposed by Ganna A et al. (2013) in their article. This study serves as a reliable benchmark for heart attack prediction programmes. To increase accuracy in cardiovascular issues, Jabbar M. A. et al. (2013) suggested. Through the application of the algorithms KNN, LR, SVM, and NN, (HRFLM) produces an enhanced exhibition level with a precision level of 88.7%. A proposal made by Folsom A. R. et al. in 1989. Direct data retrieval from electronic records eliminates the need for manual activities. To aid with the best heart disease prognosis, a big number of regulations are displayed. Poplin et al. (2018) demonstrate the effectiveness of deep learning algorithms in accurately predicting cardiovascular risk factors from a non-invasive and easily accessible imaging modality. A comprehensive review paper by Attia et al. (2019) provides an overview of various machine learning techniques applied to cardiovascular disease prediction. The review discusses the performance, advantages, and limitations of each technique, highlighting their potential in automating risk prediction for heart disease. The study compares the accuracy of machine learning algorithms with traditional risk assessment models and provides insights into the promise of machine learning to enhance heart disease risk prediction. Zhou et al. (2020) , research paper focuses specifically on machine learning-based risk prediction models for heart failure. It discusses the use of various algorithms, such as decision trees, random forests, and support vector machines, in predicting heart failure outcomes. The study emphasises the potential of artificial intelligence models in accurately identifying patients at high risk of heart failure and guiding personalized treatment strategies. Review paper “Risk Prediction for Heart Disease: A Review” of Dey et al. (2020) provides an in-depth analysis of risk prediction models for heart disease, including both traditional statistical models and machine learning approaches. It investigates 169

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the possibilities of machine learning algorithms and talks about the difficulties and limitations of current models in improving risk prediction accuracy and personalized medicine. The authors evaluate the algorithms using a large dataset of patient records and analyze the strengths and weaknesses of each technique for risk prediction in the context of heart disease.Research paper “Deep Learning-Based Risk Prediction Models for Cardiovascular Disease” of Musa et al. (2021) focuses on the application of deep learning models in cardiovascular disease risk prediction. It explores the use of convolutional and recurrent neural networks in analyzing various data modalities, including medical images, electrocardiograms, and electronic health records. The research emphasises the power of deep learning algorithms in improving the accuracy and precision of risk prediction models for heart disease.

METHODOLOGY Existing System Even now, there is a focus on heart disease as a quiet killer that causes death without giving any telltale signs. In order to prevent the spread of this terrible disease in the past, efforts are still being made in that direction. Numerous technologies and procedures are routinely tried to meet the demands of modern health. Although heart disease can express itself in a variety of ways, there is a common set of core risk factors that suggest whether or not someone would ultimately be at risk for the illness. By compiling information from various sources, organising it into useful categories, and then examining it to find the necessary facts, we can draw conclusions. This technique is excellent for use in predicting cardiac disease. The well-known adage “Prevention is better than cure” implies that early diagnosis and its control can help to avoid & minimise the death rates related to heart disease.

Proposed System The initial phase in the system’s operation is the gathering of information and the selection of the most important attributes. The necessary format is then created by preprocessing the pertinent data. The algorithms and training set of data are used to train the model. The system’s correctness is assessed by running tests on test data. The modules are used to implement this system described below.

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Collection of Dataset We initially collect a dataset that will serve as the basis for our algorithm for predicting cardiac disease. The testing dataset serves as the basis for its evaluation. Here, testing is done using 30% of the data, while training is done with 70% of the data.

Selection of Attributes The selection of qualities or features that are appropriate for the prediction system is a part of this process. To increase the system’s effectiveness, this is done. For the prediction, a variety of patient factors are taken into account, such as the patient’s gender, the type of chest discomfort they are experiencing, serum cholesterol, fasting blood pressure, etc. The attribute selection for this model is done using a correlation matrix.

Pre-Processing of Data A crucial step in the creation of a machine learning model is data pre-processing. If the data is not clean and proper format for the model, results may be incorrect. Data must be pre-processed into the format that is needed. It is used to manage the missing values, duplicates, and noise in the dataset.

Balancing of Data To balance uneven datasets, there are two methods. They are both under- and over-sampling. Figure 1. Training data vs. testing data

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Figure 2. Correlation matrix

Figure 3. Correlation between ECG and coronary changes

(a)

Under-Sampling : To balance the dataset, Under Sampling reduces the size of the enormous class. When there is enough data, this approach is used. (b) Over-Sampling : To balance the dataset in oversampling, the size of the scarce samples is raised. This procedure is considered when there is insufficient data.

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Figure 4. Data pre-processing

Figure 5. Data balancing

Prediction of Disease Several machine learning techniques, such as Naive Bayes, SVM, Random Trees, Decision Trees, Ada-boost, Logistic Regression, and Xg-boost, are used for classification. The algorithm that accurately predicts heart disease is picked after being compared to other candidates.

WORKING OF SYSTEM System Architecture A high-level knowledge of the system’s operation can be obtained from the system architecture. The act of acquiring information with patient details is known as dataset collection. The method of choosing attributes selects the necessary attributes for predicting heart disease. Locating the available data resources is followed by further selection, purging, and form transformation. The preprocessed data will be subjected to multiple categorization algorithms in order to accurately predict heart disease.

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Figure 6. Prediction of disease

Figure 7. Training vs. testing phase

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MACHINE LEARNING The primary objective of machine learning is the creation of algorithms that can analyze and interpret data, recognize patterns, and create informed judgements or predictions. It encompasses a wide range of approaches and techniques, supervised learning, unsupervised learning, and reinforcement learning. Supervised Learning: The method is trained on labelled data in supervised learning, where the input data is linked to appropriate output labels or targets. Unsupervised Learning: Unsupervised learning involves training algorithms on unlabeled data, where the goal is to uncover hidden patterns, structures, or relationships in the data. Unlike supervised learning, there are no predefined output labels. Common unsupervised learning techniques include clustering algorithms like k-means clustering and hierarchical clustering, as well as techniques for dimensionality reduction like principal component analysis (PCA). Reinforcement Learning: Reinforcement learning involves training an agent to make sequential decisions in an environment to maximize a reward signal. The agent learns through a trial-and-error process, receiving feedback from the environment based on its actions.

ALGORITHMS Support Vector Machine (SVM) A strong and adaptable supervised machine learning technique called the SVM. It is particularly effective in solving complex classification problems and has gained popularity due to its strong theoretical foundation. The followings are important concepts related to SVM: Maximal Margin Classifier: The basic idea behind SVM is to find a decision boundary, called the hyperplane, that increases the gap between the classes the most. The hyperplane is positioned to have the maximum distance from the nearest data points of each class. Support Vectors: The data points nearest to the decision border are called support vectors.

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They play a crucial role in determining the hyperplane and are used to define the decision boundary. Kernel Trick: The kernel trick allows SVM to efficiently manage data that cannot be separated linearly by converting it into a higher-dimensional feature space. The kernel function calculates the similarity between pairs of data points without explicitly mapping them to the higher-dimensional space. Soft Margin and C Parameter: In cases where the data is not perfectly separable, a soft margin can be used. A smaller C value allows more misclassifications, while a larger C value enforces a stricter classification. Regularization and Hyperparameter Tuning: SVM employs regularization to control overfitting. The regularization parameter, often denoted as ‘C’, determines the balance between fitting the data and avoiding large coefficients. Hyperparameter tuning involves selecting optimal values for C and the kernel parameters through techniques like grid search or cross-validation. Interpretability: SVM provides good interpretability as it identifies the most relevant support vectors and relies on a clear margin. Support vectors can reveal the influence of specific data points on the decision boundary and aid in understanding the model’s behavior. The advantages of SVM are: Effective in High-Dimensional Spaces: SVMs perform well even in highdimensional spaces, where there are many more features than data points in the situation. This makes them suitable for tasks that involve complex datasets with many variables. Strong Generalization: SVMs aim to find the most effective hyperplane for classifying the data while maximizing the margin between them. This margin

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Figure 8. Support Vector Machine: Schemantic representation of a linear SVM

maximization leads to better generalization, meaning SVMs often perform well on unseen data and have a lower risk of overfitting. Robust against Outliers: SVMs are relatively robust against outliers in data. The use of the maximum margin concept in SVMs helps to minimize the influence of outliers on the decision boundary. Versatile Kernel Functions: SVMs ability to transform data into different spaces enables SVMs to capture complex relationships and handle non-linear classification tasks effectively. Memory Efficient: SVMs use a subset of training samples called support vectors to define the decision boundary. This property makes SVMs memory efficient since they only require a subset of the training data for decision-making, rather than storing the entire dataset. Few Hyperparameters: SVMs have a relatively small number of hyperparameters compared to some other machine learning algorithms. This simplicity facilitates model selection and tuning.

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Wide Range of Applications: SVMs have been successfully applied in sentiment analysis, and financial prediction. Their versatility and strong generalization properties make them suitable for a wide range of supervised learning tasks. The common drawbacks support vector machines are: Computationally Intensive: SVM can be computationally expensive, especially when dealing with large datasets. Training a SVM model requires solving a convex optimization problem, which can be time-consuming for datasets with numerous samples or high-dimensional feature spaces. Sensitivity to Noise and Outliers: SVMs are sensitive to noisy data and outliers in the training set. Since SVM aims to maximize the margin and find the optimal decision boundary, even a few mislabeled or misclassified points can significantly affect the model’s performance. Difficulty with Large Datasets: SVMs do not scale well with large datasets. As the number of samples increases, the training time and memory requirements of SVM grow significantly. This limitation makes SVM less suitable for big data applications without efficient optimization techniques or approximations. Lack of Probability Estimates: Traditional SVMs are binary classifiers and do not provide direct probability estimates for the predicted classes. Instead, they assign data points to classes based on the decision function’s sign. Obtaining reliable probability estimates from SVM requires additional calibration methods like Platt scaling or isotonic regression. Naive Bayes : It is a simple yet effective algorithm commonly used for classification tasks. The name “naive” comes from the assumption of independence between features, which means that each feature contributes independently to the probability of a certain class. Here are some key characteristics and advantages of Naive Bayes: Simplicity: Naive Bayes is relatively simple to understand and implement. It has a straightforward probabilistic framework and does not require complex iterative optimization like some other machine learning algorithms. Fast Training and Prediction: Naive Bayes is computationally efficient and has a fast training phase. It can handle large datasets and make predictions quickly, which makes it suitable for real-time or time-sensitive applications. Effective with High-Dimensional Data: This is because the algorithm assumes feature independence, which reduces the computational complexity and mitigates the curse of dimensionality.

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Robust to Irrelevant Features: Naive Bayes tends to be robust to irrelevant features in the dataset. Since it assumes independence between features, irrelevant features will have minimal impact on the classification results. Interpretable Results: The probabilities estimated by Naive Bayes provide interpretable results. The algorithm outputs the probability of each class given the observed features, allowing for transparency and understanding of the classification process. Suitable for Spam Filtering and Text Classification: When it comes to text classification tasks like document classification, spam filtering and sentiment analysis, Naive Bayes is particularly well-liked. It can handle high-dimensional text data efficiently and achieve good accuracy. Despite its advantages, Naive Bayes has some limitations: Strong Feature Independence Assumption: The assumption of feature independence is often violated in real-world scenarios where features may exhibit dependencies. This can lead to suboptimal performance when the feature dependencies are significant. Lack of Model Complexity: Naive Bayes models have limited capacity to represent complex relationships in the data. They may struggle to capture nuanced patterns and interactions between features, which can affect their performance in certain domains. Sensitivity to Feature Distribution: Naive Bayes assumes that features follow a specific distribution, such as Gaussian (for continuous features) or multinomial (for categorical features). If the data violates these assumptions, the performance of Naive Bayes may suffer. Types of Naive Bayes Models : Types of Naive Bayes models are: Gaussian Naive Bayes: Assumes that continuous features follow a Gaussian (normal) distribution. Uses the mean and standard deviation of each feature to estimate the probability distribution. Suitable for continuous or numeric data. Multinomial Naive Bayes: Assumes that features have a multinomial distribution. Typically used for discrete features, such as word counts or categorical data. Often applied in text classification tasks. Bernoulli Naive Bayes: Assumes that features are binary (0/1) variables. 179

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Useful for binary or Boolean features, such as presence or absence of certain characteristics. Often employed in spam filtering, sentiment analysis, and other binary classification tasks.

Complement Naive Bayes: A variation of the multinomial Naive Bayes algorithm. Addresses the issue of imbalanced class distribution by taking into account the distribution of the majority class. Particularly effective when dealing with imbalanced datasets. Hybrid Naive Bayes: Combines different types of Naive Bayes models to handle datasets with mixed types of features. Can handle a combination of continuous, discrete, and binary features in a single model. Provides flexibility in modeling heterogeneous data. Decision Tree : Key characteristics and components of a Decision Tree include: Splitting Criteria: Decision Trees use different metrics to determine the best feature and value for splitting the data at each internal node. Common splitting criteria include Gini Index, Information Gain, and Variance Reduction. Feature Selection: The algorithm selects the most informative features to split the data based on the selected splitting criterion. Features that contribute the most to the target variable are given higher priority for splitting.

Figure 9. Three types of naïve bayes model

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Tree Construction: Decision Trees are constructed recursively, starting from the root node. The algorithm chooses the best feature and value to divide the data into child nodes at each stage. Until a stopping requirement, such as reaching a maximum depth, is satisfied, this process continues or no further improvement in the splitting criterion. Pruning: Decision Trees are prone to overfitting, capturing noise and irrelevant details from the training data. Pruning techniques, such as cost complexity pruning (also known as reduced-error pruning), are used to simplify the tree by removing unnecessary branches and improving generalization. Classification and Regression: Both classification and regression problems can be performed using decision trees. In regression, the leaf nodes contain the predicted continuous values, such as mean or median, of the target variable. Advantages of Decision Trees: Easy to understand and interpret due to their tree-like structure. Can handle both categorical and numerical features. Automatically selects the most informative features. Performs well on large datasets and is relatively fast to train. Disadvantages of Decision Trees: Especially when the tree depth is not under control, prone to overfitting. Sensitive to small variations in the training data, leading to different tree structures. May produce intricate, hard-to-interpret trees that are prone to overfitting. Decision Trees alone may not capture complex patterns present in the data. Random Forest : Random Forest improves upon the limitations of individual Decision Trees by reducing overfitting and increasing robustness. Key characteristics and components of Random Forest include: Random Subsampling: Random Forest uses a technique called bootstrapping, each tree is trained using replacement on a random sample of the training data. This means that each tree is exposed to slightly different variations of the dataset. Feature Subset Selection: Random Forest selects a random subset of features at each node in each tree. This further enhances the diversity among the trees and prevents a few dominant features from overshadowing the others. Out-of-Bag (OOB) Error: Random Forest can estimate the performance of the model without the need for cross-validation. During training, each tree is evaluated on the data points that were not included in its bootstrap sample. The average 181

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prediction error on these out-of-bag samples gives an estimate of the model’s performance. Advantages of Random Forest: Reduces overfitting compared to individual Decision Trees. Handles high-dimensional data and large datasets effectively. Provides feature importance estimation, indicating the relative importance of features in the model. Robust to outliers and noise in the data. Performs well even with a small number of training samples. Disadvantages of Random Forest: May have reduced interpretability compared to a single Decision Tree. Can be biased towards features with more levels or categories. Logistic Regression : For binary classification tasks, the statistical learning approach known as logistic regression is frequently utilised. Contrary to what its name implies, logistic regression is a classification algorithm. Key characteristics and components of Logistic Regression include: Logistic Function (Sigmoid Function): The sigmoid function is defined as: sigmoid(z) = 1 / (1 + e^(-z)) where z represents the linear combination of the features and model parameters. Binary Classification: The algorithm predicts the probability of the target belonging to class 1 and assigns a class label based on a predefined threshold (usually 0.5). Log-Likelihood and Max Likelihood Estimation: To estimate the model parameters in a logistic regression, maximum likelihood estimation is used. The objective is to maximize the log-likelihood of the observed data given the model parameters. This estimation process finds the best-fitting line that maximizes the likelihood of the observed binary outcomes. Model Training and Coefficient Estimation: A training algorithm for the logistic regression model, such as Newton’s technique or gradient descent, is used. Decision Boundary: Logistic Regression separates the feature space by a decision boundary, which is typically a hyperplane. The decision boundary is determined by the learned coefficients of the model. Advantages of Logistic Regression:

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Simplicity and interpretability: Logistic Regression produces interpretable results by providing coefficient values that represent the influence of each feature on the target variable. Probabilistic interpretation: The algorithm provides probability estimates, allowing for a more nuanced understanding of the predicted outcomes. Disadvantages of Logistic Regression: Limited to binary classification: Logistic Regression is designed for binary classification tasks and may not be directly applicable to multi-class problems without modification. Sensitivity to feature selection: The performance of Logistic Regression can be sensitive to the choice of features and the presence of irrelevant or correlated variables.

DATASET DETAILS Only fourteen attributes out of the seventy six present in the dataset are taken into account for the result prediction from Heart Disease UCI.

Input Dataset Attributes •

Gender (value 1: Male; value 0: Female)

Figure 10. Logistic regression

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• • • • • • • • • • • • •

Chest Pain Type (value 1: typical type 1 angina, value 2: typical type angina, value 3: non-angina pain; value 4: asymptomatic) Fasting Blood Sugar (value 1: > 120 mg/dl; value 0:< 120 mg/dl) Exang – exercise induced angina (value 1: yes; value 0: no) CA – number of major vessels colored by fluoroscopy (value 0 – 3) Thal (value 3: normal; value 6: fixed defect; value 7: reversible defect) Trest Blood Pressure (mm Hg on admission to the hospital) Serum Cholesterol (mg/dl) Thalach – maximum heart rate achieved Age in Year Height in cms Weight in Kgs. Cholestrol Restecg

PERFORMANCE ANALYSIS Several machine learning techniques, such as Naive Bayes, Decision Trees, Random Forests, SVM, Logistic Regression, Adaboost, and XG-boost, are used to predict heart disease. Each algorithm’s accuracy needs to be evaluated, and the approach that offers the highest accuracy is chosen to forecast heart disease. The experiment is evaluated using a number of assessment criteria, including accuracy, confusion matrix, precision, recall, and f1-score. F1 Score = 2 ×

1 1 1 + Precision Recall

Accuracy: Accuracy is the ratio of the number of correct predictions to the total number of inputs in the dataset. It is expressed as: Accuracy = (TP + TN) /(TP+FP+FN+TN). Figure 11. F1 score calculation

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Figure 12. Accuracy measurement

RESULT We find that, when compared to other methods, the XG-boost technique has higher accuracy after employing a machine learning strategy for testing and training. Accuracy is determined using the confusion matrices for each algorithm. The TP, TN, FP, and FN numbers are listed here. Value has been computed using the equation to ensure correctness. With 81% accuracy, it is determined that severe gradient boosting is the best.

CONCLUSION AND FUTURE WORK In conclusion, this research study aimed to develop an automated risk prediction a cardiac disease model employing machine learning techniques. Through the analysis of a large dataset of patient records and the application of various machine learning techniques, we achieved promising results in accurately predicting the

Table 1. Accuracy comparison of algorithms Algorithm

Accuracy

XG-boost

81.3%

SVM

80.2%

Logistic Regression

79.1%

Random Forest

79.1%

Naive Bayes

76.9%

Decision Tree

75.8%

Adaboost

73.6%

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risk of heart disease. Our findings indicate that algorithms for machine learning, including Logistic Regression, Support Vector Machines, and Random Forest can effectively analyze diverse patient data, including demographic information, medical history, and clinical measurements, to determine who is most at risk for developing heart disease. The performance evaluation of the developed models demonstrated significant improvements in prediction accuracy compared to traditional risk assessment methods. The utilization of machine learning algorithms allowed for the identification of important risk factors and their impact on the development of heart disease. This information can potentially aid healthcare providers in making early interventions and implementing preventive measures to reduce the burden of heart disease. Moreover, the research highlighted the importance of feature selection and model optimization in improving the accuracy and interpretability of the risk prediction models. Feature engineering techniques, such as dimensionality reduction and feature importance analysis, helped identify the most informative variables for heart disease prediction. Additionally, model evaluation metrics, including accuracy, precision, recall, and area under the ROC curve, provided insights into the performance of the developed models and their ability to discriminate between high-risk and low-risk individuals. While our study achieved promising results, there are some limitations to consider. The performance of the models may vary across different populations and healthcare settings, and the generalizability of the findings should be further validated. The developed Healthcare practitioners may benefit from models, in identifying individuals at high risk, enabling early intervention and personalized preventive strategies. Future research should focus on the integration of additional data sources, validation of the models in diverse populations, and the development of user-friendly tools for practical implementation in clinical settings. This article describes the 81% highest accuracy found while comparing all seven extreme gradient boosting classification systems.

REFERENCES Aldallal, A., & Al-Moosa, A. A. A. (2018). Using Data Mining Techniques to Predict Diabetes and Heart Diseases. 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), 150-154. Bashir, S., Qamar, U., & Javed, M. Y. (2014, November). An ensemble-based decision support framework for intelligent heart disease diagnosis. In International Conference on Information Society (i-Society 2014) (pp. 259-64). IEEE. 10.1088/1757899X/1022/1/012072 186

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Brown, N., Young, T., Gray, D., Skene, A. M., & Hampton, J. R. (1997). Inpatient deaths from acute myocardial infarction, 1982-92: Analysis of data in the Nottingham heart attack register. BMJ (Clinical Research Ed.), 315(7101), 159–162. doi:10.1136/ bmj.315.7101.159 PMID:9251546 Chen, A. H., Huang, S. Y., Hong, P. S., Cheng, C. H., & Lin, E. J. (2011, September). HDPS: Heart disease prediction system. Computers in Cardiology, 557–560. Dangare, C. S., & Apte, S. S. (2012). Improved study of heart disease prediction system using data mining classification techniques. International Journal of Computer Applications, 47(10), 44–48. doi:10.5120/7228-0076 Dewan, A., & Sharma, M. (2015). Prediction of heart disease using a hybrid technique in data mining classification. 2nd International Conference on Computing for Sustainable Global Development (INDIACom). Folsom, A. R., Prineas, R. J., Kaye, S. A., & Soler, J. T. (1989). Body fat distribution and self- reported prevalence of hypertension, heart attack, and other heart disease in older women. International Journal of Epidemiology, 18(2), 361-7. Ganna, A., Magnusson, P. K., Pedersen, N. L., de Faire, U., Reilly, M., Ärnlöv, J., & Ingelsson. (2013). Multilocus genetic risk scores for coronary heart disease prediction. Arteriosclerosi Thrombosis, and Vascularbiology, 33(9), 2267-72. Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2013, March). Heart disease prediction using lazy associative classification. In 2013 International Multiconferences on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s) (pp. 40- 6). IEEE. Jee, S. H., Jang, Y., Oh, D. J., Oh, B. H., Lee, S. H., Park, S. W., & Yun, Y. D. (2014). A coronary heart disease prediction model: The Korean Heart Study. BMJ Open, 4(5), e005025. doi:10.1136/bmjopen-2014-005025 PMID:24848088 Ordonez, C. (2006). Association rule discovery with the train and test approach for heart disease prediction. IEEE Transactions on Information Technology in Biomedicine, 10(2), 334–343. doi:10.1109/TITB.2006.864475 PMID:16617622 Parthiban, L., & Subramanian, R. (2008). Intelligent heart disease prediction system using CANFIS and genetic algorithm International Journal of Biological. Biomedical and Medical Sciences, 3, 3. Patel, S., & Chauhan, Y. (2014). Heart attack detection and medical attention using motion sensing device -kinect. International Journal of Scientific and Research Publications, 4(1), 1–4.

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Piller, L. B., Davis, B. R., Cutler, J. A., Cushman, W. C., Wright, J. T., Williamson, J. D., & Haywood, L. J. (2002). Validation of heart failure events in the Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) participants assigned to doxazosin and chlorthalidone. Current Controlled Trials in Cardiovascular Medicine, 3(1), 10. doi:10.1186/1468-6708-3-10 PMID:12459039 Raihan, M., Mondal, S., More, A., Sagor, M. O. F., Sikder, G., Majumder, M. A., & Ghosh, K. (2016). Smartphone based ischemic heart disease (heart attack) risk prediction using clinical data and data mining approaches, a prototype design. In 2016 19th International Conference on Computer and Information Technology (ICCIT) (pp. 299-303). IEEE. Shinde, R., Arjun, S., Patil, P., & Waghmare, J. (2015). An intelligent heart disease prediction system using k-means clustering and Naïve Bayes algorithm. International Journal of Computer Science and Information Technologies, 6(1), 637–639. Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 43–48. doi:10.5120/2237-2860 Takci, H. (2018). Improvement of heart attack prediction by the feature selection methods. Turkish Journal of Electrical Engineering and Computer Sciences, 26(1), 1–10. doi:10.3906/elk-1611-235 Wolgast, G., Ehrenborg, C., Israelsson, A., Helander, J., Johansson, E., & Manefjord, H. (2016). Wireless body area network for heart attack detection. IEEE Antennas & Propagation Magazine, 58(5), 84–92. doi:10.1109/MAP.2016.2594004

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Cognitive Cardiac Rehabilitation Using IoT and AI Tools: Conclusion Kaushik Mazumdar Indian Institute of Technology (ISM) Dhanbad, India Sima Das Camellia Institute of Technology and Management, India

ABSTRACT Cognitive cardiac rehabilitation using IoT and AI tools is a promising approach for improving the effectiveness and efficiency of cardiac rehabilitation programs. The use of IoT devices allows for the remote monitoring of patients’ vital signs and physical activity, while AI tools can analyze this data to provide personalized feedback and support. This approach has the potential to enhance patient engagement and adherence to rehabilitation protocols, leading to better outcomes for patients. While the benefits of cognitive cardiac rehabilitation using IoT and AI tools are clear, there are also some challenges that need to be addressed.

INTRODUCTION Cognitive cardiac rehabilitation using IoT and AI tools is a promising approach for improving the effectiveness and efficiency of cardiac rehabilitation programs. The use of IoT devices allows for the remote monitoring of patients’ vital signs DOI: 10.4018/978-1-6684-7561-4.ch013 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Cognitive Cardiac Rehabilitation Using IoT and AI Tools

and physical activity, while AI tools can analyze this data to provide personalized feedback and support. This approach has the potential to enhance patient engagement and adherence to rehabilitation protocols, leading to better outcomes for patients. While the benefits of cognitive cardiac rehabilitation using IoT and AI tools are clear, there are also some challenges that need to be addressed. One major concern is ensuring the privacy and security of patients’ health data, which is crucial for maintaining trust in these technologies. Another challenge is ensuring that the AI algorithms used to analyze patient data are accurate and reliable, as errors or biases could have serious consequences for patient outcomes. Overall, cognitive cardiac rehabilitation using IoT and AI tools represents a promising direction for improving the delivery of cardiac rehabilitation services. However, continued research and development are needed to optimize these technologies and ensure that they are safe, effective, and accessible to all patients who can benefit from them. Cognitive cardiac rehabilitation using IoT and AI tools has shown great potential in improving the outcomes of cardiac rehabilitation programs. By leveraging the power of IoT devices and AI algorithms, healthcare providers can collect and analyze data on patients’ physical and cognitive performance, track their progress, and provide personalized feedback and guidance. The use of wearable devices, such as smartwatches and fitness trackers, enables remote monitoring of patients’ physiological parameters, such as heart rate, blood pressure, and oxygen saturation. This real-time data can be used to adjust rehabilitation programs to meet individual needs, improve patient engagement and motivation, and prevent complications. In addition, AI algorithms can analyze the data collected from IoT devices to identify patterns and correlations that can be used to personalize rehabilitation plans. For example, machine learning algorithms can identify which exercises are most effective for each patient based on their physiological response, past performance, and medical history. Overall, cognitive cardiac rehabilitation using IoT and AI tools has the potential to revolutionize cardiac rehabilitation by making it more personalized, effective, and accessible. However, further research is needed to fully understand the potential benefits and limitations of this approach, and to ensure that patient privacy and security are protected. Cognitive cardiac rehabilitation using IoT and AI tools has the potential to revolutionize the way cardiac rehabilitation programs are delivered. The integration of IoT and AI technologies can provide patients with a personalized rehabilitation plan that is tailored to their specific needs and preferences, while also monitoring their progress and providing real-time feedback.

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One of the main benefits of cognitive cardiac rehabilitation using IoT and AI tools is that it can improve patient outcomes and reduce the risk of hospital readmission. By providing patients with a more personalized rehabilitation plan, they are more likely to adhere to the program and achieve better results. Additionally, real-time monitoring and feedback can help patients identify potential issues and address them before they become more serious. However, there are also some potential challenges associated with the implementation of cognitive cardiac rehabilitation using IoT and AI tools. For example, some patients may have concerns about the security and privacy of their data, and may be hesitant to use these technologies. Additionally, there may be concerns about the accuracy and reliability of the data collected by these devices. Overall, cognitive cardiac rehabilitation using IoT and AI tools has the potential to improve the quality of care for patients with cardiovascular disease. However, it is important to carefully consider the potential benefits and challenges of these technologies before implementing them in clinical practice. This technology can also provide patients with continuous feedback and support, which can motivate them to adhere to their rehabilitation plan and make lifestyle changes necessary for a healthy heart. Additionally, the data collected through these tools can be used to create predictive models that can help healthcare providers anticipate potential health issues and adjust the rehabilitation plan accordingly. However, there are still some challenges that need to be addressed, such as data privacy and security concerns, technical limitations, and the need for more extensive clinical trials to validate the effectiveness of these tools. Overall, the potential benefits of cognitive cardiac rehabilitation using IoT and AI tools are promising, and with continued research and development, these tools could play a significant role in improving the quality of life for patients with cardiovascular disease. Cognitive cardiac rehabilitation using IoT and AI tools is a promising approach to improving the outcomes of patients with cardiovascular diseases. Real-time monitoring, personalized rehabilitation plans, and enhanced patient engagement are some of the significant benefits of this approach. However, data privacy and security and technological barriers are significant challenges that need to be addressed to ensure the success of this approach. Healthcare providers and technology companies need to work together to develop appropriate security measures and ensure data interoperability to overcome these challenges. Overall, cognitive cardiac rehabilitation using IoT and AI tools has the potential to revolutionize the way we approach cardiovascular disease management. By leveraging the power of IoT and AI, we can provide personalized and effective rehabilitation plans that can improve patients’ outcomes and quality of life.

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192

Compilation of References

Ades, P. A. (2001). Cardiac rehabilitation and secondary prevention of coronary heart disease. The New England Journal of Medicine, 345(12), 892–902. doi:10.1056/NEJMra001529 PMID:11565523 Ahmad, G. N., Shafiullah, H. F., Abbas, M., Obaidur Rahman, I., & Alqahtani, M. S. (2022). Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features. Applied Sciences (Basel, Switzerland), 12(15), 7449. doi:10.3390/app12157449 Al Asad, N., Pranto, M. A. M., Afreen, S., & Islam, M. M. (2019, November). Depression detection by analyzing social media posts of user. In 2019 IEEE international conference on signal processing, information, communication & systems (SPICSCON) (pp. 13-17). IEEE. Aldallal, A., & Al-Moosa, A. A. A. (2018). Using Data Mining Techniques to Predict Diabetes and Heart Diseases. 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), 150-154. Alizadehsani, R., Hosseini, M. J., Sani, Z. A., Ghandeharioun, A., & Boghrati, R. (2012). Diagnosis of coronary artery disease using cost-sensitive algorithms. In Proc. IEEE 12th Int. Conf. Data Mining Workshops, (pp. 916). IEEE. 10.1109/ICDMW.2012.29 Al-Qerem, A., Kharbat, F., Nashwan, S., Ashraf, S., & Blaou, K. (2020). General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution. International Journal of Distributed Sensor Networks, 16(3), 1550147720911009. doi:10.1177/1550147720911009 Alturki, F. A., Aljalal, M., Abdurraqeeb, A. M., Alsharabi, K., & Al-Shamma’a, A. A. (2021). Common spatial pattern technique with EEG signals for diagnosis of autism and epilepsy disorders. IEEE Access : Practical Innovations, Open Solutions, 9, 24334–24349. doi:10.1109/ ACCESS.2021.3056619 Anderson, L., Sharp, G. A., Norton, R. J., Dalal, H., Dean, S. G., Jolly, K., & Taylor, R. S. (2017). Home‐based versus centre‐based cardiac rehabilitation. Cochrane Database of Systematic Reviews, 6. PMID:28665511

Compilation of References

Ang, K. K., Guan, C., Chua, K. S. G., Ang, B. T., Kuah, C. W. K., Wang, C., Phua, K. S., Chin, Z. Y., & Zhang, H. (2011). A large clinical study on the ability of stroke patients to use an EEGbased motor imagery brain-computer interface. Clinical EEG and Neuroscience, 42(4), 253–258. doi:10.1177/155005941104200411 PMID:22208123 Angskun, J., Tipprasert, S., & Angskun, T. (2022). Big data analytics on social networks for real-time depression detection. Journal of Big Data, 9(1), 69. doi:10.118640537-022-00622-2 PMID:35610999 Anitha, H. (2022). Emotion Recognition from EEG Signals Using Recurrent Neural Networks. Electronics (Basel), 15(11), 2387. doi:10.3390/electronics11152387 Baccianella, S., Esuli, A., & Sebastiani, F. (2010, May). Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Lrec (Vol. 10, pp. 2200-2204). Baccour, L. (2018, June). Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets. Expert Systems with Applications, 99, 115125. doi:10.1016/j.eswa.2018.01.025 Bae, Y. J., Shim, M., & Lee, W. H. (2021). Schizophrenia detection using machine learning approach from social media content. Sensors (Basel), 21(17), 5924. doi:10.339021175924 PMID:34502815 Bag, D., Ghosh, A., & Saha, S. (2023). An Automatic Approach to Control Wheelchair Movement for Rehabilitation Using Electroencephalogram. In Design and Control Advances in Robotics (pp. 105–126). IGI Global. Baillet, S., & Garnero, L. (1997). A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE Transactions on Biomedical Engineering, 44(5), 374–385. doi:10.1109/10.568913 PMID:9125822 Bai, X., Vestal, M., Berman, R., Negishi, M., Spann, M., Vega, C., Desalvo, M., Novotny, E. J., Constable, R. T., & Blumenfeld, H. (2010). Dynamic time course of typical childhood absence seizures: EEG, behavior, and functional magnetic resonance imaging. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 30(17), 5884–5893. doi:10.1523/JNEUROSCI.5101-09.2010 PMID:20427649 Bashir, S., Qamar, U., & Javed, M. Y. (2014, November). An ensemble-based decision support framework for intelligent heart disease diagnosis. In International Conference on Information Society (i-Society 2014) (pp. 259-64). IEEE. 10.1088/1757-899X/1022/1/012072 Bashir, S. (2014). Novel Approaches of Non-Invasive Stimulation Techniques to Motor Rehabilitation Following Stroke: A Review. Brain & Neurorehabilitation, 7(2), 71–75. doi:10.12786/bn.2014.7.2.71 Bathina, K. C., Ten Thij, M., Lorenzo-Luaces, L., Rutter, L. A., & Bollen, J. (2021). Individuals with depression express more distorted thinking on social media. Nature Human Behaviour, 5(4), 458–466. doi:10.103841562-021-01050-7 PMID:33574604

193

Compilation of References

Benamara, F., Moriceau, V., Mothe, J., Ramiandrisoa, F., & He, Z. (2018, May). Automatic detection of depressive users in social media. In Conférence francophone en Recherche d’Information et Applications. CORIA. Beniczky, S., & Schomer, D. L. (2020). Electroencephalography: Basic biophysical and technological aspects important for clinical applications. Epileptic Disorders, 22(6), 697–715. doi:10.1684/epd.2020.1217 PMID:33270023 Benton, A., Mitchell, M., & Hovy, D. (2017). Multi-task learning for mental health using social media text. arXiv preprint arXiv:1712.03538. Bethell, H., Lewin, R., & Dalal, H. (2009). Cardiac rehabilitation in the United Kingdom. Heart (British Cardiac Society), 95(4), 271–275. doi:10.1136/hrt.2007.134338 PMID:18208830 Bilsen, J. (2018). Suicide and youth: Risk factors. Frontiers in Psychiatry, 540, 540. doi:10.3389/ fpsyt.2018.00540 PMID:30425663 Birje, M. N., & Hanji, S. S. (2020). Internet of things based distributed healthcare systems: A review. J. of Data. Information & Management, 2, 149–165. Bjarnason-Wehrens, B., McGee, H., Zwisler, A. D., Piepoli, M. F., Benzer, W., Schmid, J. P., & Mendes, M. (2010). Cardiac rehabilitation in Europe: Results from the European cardiac rehabilitation inventory survey. European Journal of Preventive Cardiology, 17(4), 410–418. PMID:20300001 Brinks, J., Fowler, A., Franklin, B. A., & Dulai, J. (2016). Lifestyle Modification in Secondary Prevention. American Journal of Lifestyle Medicine, 11(2), 137–152. doi:10.1177/1559827616651402 PMID:30202327 Brown, N., Young, T., Gray, D., Skene, A. M., & Hampton, J. R. (1997). Inpatient deaths from acute myocardial infarction, 1982-92: Analysis of data in the Nottingham heart attack register. BMJ (Clinical Research Ed.), 315(7101), 159–162. doi:10.1136/bmj.315.7101.159 PMID:9251546 Burnap, P., Colombo, W., & Scourfield, J. (2015, August). Machine classification and analysis of suicide-related communication on twitter. In Proceedings of the 26th ACM conference on hypertext & social media (pp. 75-84). ACM. 10.1145/2700171.2791023 Butenas, S., Undas, A., Gissel, M. T., Szuldrzynski, K., Zmudka, K., & Mann, K. G. (2008). Factor XIa and tissue factor activity in patients with coronary artery disease. Thrombosis and Haemostasis, 99(1), 142–149. doi:10.1160/TH07-08-0499 PMID:18217146 Cacheda, F., Fernandez, D., Novoa, F. J., & Carneiro, V. (2019). Early detection of depression: Social network analysis and random forest techniques. Journal of Medical Internet Research, 21(6), e12554. doi:10.2196/12554 PMID:31199323 Caforio, A. L., Calabrese, F., Angelini, A., Tona, F., Vinci, A., Bottaro, S., Ramondo, A., Carturan, E., Iliceto, S., Thiene, G., & Daliento, L. (2007). A prospective study of biopsy- proven myocarditis: Prognostic relevance of clinical and aetiopathogenetic features at diagnosis. European Heart Journal, 28(11), 1326–1333. doi:10.1093/eurheartj/ehm076 PMID:17493945 194

Compilation of References

Certo, C. M. (1985). History of cardiac rehabilitation. Physical Therapy, 65(12), 1793–1795. doi:10.1093/ptj/65.12.1793 PMID:3906683 Cervera, M. A., Soekadar, S. R., Ushiba, J., Millán, J. D. R., Liu, M., Birbaumer, N., & Garipelli, G. (2018). Brain‐computer interfaces for post‐stroke motor rehabilitation: A meta‐analysis. Annals of Clinical and Translational Neurology, 5(5), 651–663. doi:10.1002/acn3.544 PMID:29761128 Cha, J., Kim, S., & Park, E. (2022). A lexicon-based approach to examine depression detection in social media: The case of Twitter and university community. Humanities & Social Sciences Communications, 9(1), 1–10. doi:10.105741599-022-01313-2 PMID:36159708 Chakraborty, D., Ghosh, A., & Saha, S. (2020). A survey on Internet-of-Thing applications using electroencephalogram. In Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach (pp. 21–47). Academic Press. doi:10.1016/B978-0-12-819593-2.00002-9 Champaty, B., Jose, J., Pal, K., & Thirugnanam, A. (2014, July). Development of EOG based human machine interface control system for motorized wheelchair. In 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD) (pp. 1-7). IEEE. 10.1109/AICERA.2014.6908256 Chancellor, S., & De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: A critical review. NPJ Digital Medicine, 3(1), 43. doi:10.103841746020-0233-7 PMID:32219184 Chancellor, S., Mitra, T., & De Choudhury, M. (2016, May). Recovery amid pro-anorexia: Analysis of recovery in social media. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 2111-2123). ACM. 10.1145/2858036.2858246 Chang, Z., Zheng, T., & Zhu, L. (2022). Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network. Frontiers in Psychiatry, 12(12), 837149. doi:10.3389/fpsyt.2021.837149 PMID:35368726 Chatterjee, M., Kumar, P., Samanta, P., & Sarkar, D. (2022b). Suicide ideation detection from online social media: A multi-modal feature based technique. International Journal of Information Management Data Insights, 2(2), 100103. doi:10.1016/j.jjimei.2022.100103 Chatterjee, M., Samanta, P., Kumar, P., & Sarkar, D. (2022a). Suicide Ideation Detection using Multiple Feature Analysis from Twitter Data. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1-6). IEEE. 10.1109/DELCON54057.2022.9753295 Chatterjee, R., Gupta, R. K., & Gupta, B. (2021). Depression detection from social media posts using multinomial naive theorem. [). IOP Publishing.]. IOP Conference Series. Materials Science and Engineering, 1022(1), 012095. doi:10.1088/1757-899X/1022/1/012095 Chaurasia, V., & Pal, S. (2013). Early prediction of heart diseases using data mining techniques. Caribbean J. Sci. Technol., 1, 208217. Chen, A. H., Huang, S. Y., Hong, P. S., Cheng, C. H., & Lin, E. J. (2011, September). HDPS: Heart disease prediction system. Computers in Cardiology, 557–560. 195

Compilation of References

Cheng, C.-A., & Chiu, H.-W. (2017) An articial neural network model for the evaluation of carotid artery stenting prognosis using a national-wide database. In Proc. 39th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE. Cicerone, K. D., Dahlberg, C., Kalmar, K., Langenbahn, D. M., Malec, J. F., Bergquist, T. F., & Morse, P. A. (2000). Evidence-based cognitive rehabilitation: Recommendations for clinical practice. Archives of Physical Medicine and Rehabilitation, 81(12), 1596–1615. doi:10.1053/ apmr.2000.19240 PMID:11128897 Clare, L., & Woods, R. T. (2014). Cognitive training and cognitive rehabilitation for people with early-stage Alzheimer’s disease: A review. Neuropsychological Rehabilitation, 24(3-4), 419–450. PMID:23957379 Clark, R. A., Conway, A., Poulsen, V., Keech, W., Tirimacco, R., & Tideman, P. (2015). Alternative models of cardiac rehabilitation: A systematic review. European Journal of Preventive Cardiology, 22(1), 35–74. doi:10.1177/2047487313501093 PMID:23943649 Comani, S., Schinaia, L., Tamburro, G., Velluto, L., Sorbi, S., Conforto, S., & Guarnieri, B. (2015, August). Assessing neuro-motor recovery in a stroke survivor with high-resolution EEG, robotics and Virtual Reality. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3925-3928). IEEE. 10.1109/EMBC.2015.7319252 Cook, B. L., Progovac, A. M., Chen, P., Mullin, B., Hou, S., & Baca-Garcia, E. (2016). Novel use of natural language processing (NLP) to predict suicidal ideation and psychiatric symptoms in a text-based mental health intervention in Madrid. Computational and Mathematical Methods in Medicine, 2016, 2016. doi:10.1155/2016/8708434 PMID:27752278 Cooper, A. F., Jackson, G., Weinman, J., & Horne, R. (2002). Factors associated with cardiac rehabilitation attendance: A systematic review of the literature. Clinical Rehabilitation, 16(5), 541–552. doi:10.1191/0269215502cr524oa PMID:12194625 Coppersmith, G., Dredze, M., Harman, C., & Hollingshead, K. (2015). From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. In Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality (pp. 1-10). Coppersmith, G., Ngo, K., Leary, R., & Wood, A. (2016, June). Exploratory analysis of social media prior to a suicide attempt. In Proceedings of the third workshop on computational linguistics and clinical psychology (pp. 106-117). 10.18653/v1/W16-0311 Corcione, F., Esposito, C., Cuccurullo, D., Settembre, A., Miranda, N., Amato, F., Pirozzi, F., & Caiazzo, P. (2005). Advantages and limits of robot-assisted laparoscopic surgery: Preliminary experience. Surgical Endoscopy, 19(1), 117–119. doi:10.100700464-004-9004-9 PMID:15549629 Cunningham, K. S., & Gotlieb, A. I. (2005). AI: The role of shear stress in the pathogenesis of atherosclerosis. Laboratory Investigation, 85(1), 9–23. doi:10.1038/labinvest.3700215 PMID:15568038

196

Compilation of References

Dalal, H. M., Doherty, P., & Taylor, R. S. (2015). Cardiac rehabilitation. BMJ (Clinical Research Ed.), 351. PMID:26419744 Dangare, C. S., & Apte, S. S. (2012). Improved study of heart disease prediction system using data mining classification techniques. International Journal of Computer Applications, 47(10), 44–48. doi:10.5120/7228-0076 Dan, T., & Fu, Z. (2021). Possibilistic Clustering-Promoting Semi-Supervised Learning for EEGBased Emotion Recognition. Frontiers in Neuroscience, 15, 690044. Advance online publication. doi:10.3389/fnins.2021.690044 PMID:34276295 Das, N., & Das, R. (2021). The Effectiveness of Cognitive Rehabilitation for Individuals With Traumatic Brain Injury: A Systematic Review and Meta-analysis. American Journal of Occupational Therapy, 75(2), 7502205010p1-7502205010p8. Das, S. & Bhattacharya, A. (2021). ECG Assess Heartbeat rate, Classifying using BPNN while Watching Movie and send Movie Rating through Telegram, 465-474. Springer. . doi:10.1007/978981-15-9774-9_43 Das, S., Kumari, K., & Sahu, R.. (2023). Dia-Heart Prediction: Diabetes and Heart Attack Prediction using Machine Learning Techniques and Smartphone. Das, S., Malick, S., Majumder, S., & Mallick, D. (2023). Arrhythmia Bot: Arrhythmia Disease Prediction using Internet of Things with Machine Learning Approaches and Telegram Bot. American Journal of Advanced Computing.. doi:10.15864/ajac.21001 Das, Sima & Bhowmick, Parijat & Giri, Nimay & Minakova, Kseniia & Rubanenko, Olena & Danylchenko, Dmytro. (2022). Telemedical System for Monitoring the Psycho-Neurological State of Patients in the Process of Rehabilitation. 1-6. . doi:10.1109/KhPIWeek57572.2022.9916354 Das, Sima & Das, Jaya & Modak, Subrata & Mazumdar, Kaushik. (2022). Internet of Things with Machine Learning-Based Smart Cardiovascular Disease Classifier for Healthcare in Secure Platform. Taylor & Francis. . doi:10.1201/9781003217398-4 Das, Sima & Ghosh, Ahona & Saha, Sriparna. (2022). A Review on Gaming Effects on Cognitive Load for Smart Healthcare and Its Security. . doi:10.4018/978-1-6684-5741-2.ch001 Das, Sima & Ghosh, Ahona. (2023). Emotion Detection Using Generative Adversarial Network. . doi:10.1201/9781003203964-11 Das, Sima & Ghosh, Lidia & Saha, Sriparna. (2020). Analyzing Gaming Effects on Cognitive Load Using Artificial Intelligent Tools. . doi:10.1109/CONECCT50063.2020.9198662 Das, Sima & Malick, Sayantan & Dey, Sovan & Sarkar, Amin & Hossain, Fardin & Samad, Abdus. (2023). Game-Stresso-Tracker: EEG Based Smart Advisor Bot for Stress Detection During Playing BGMI Game. . doi:10.1007/978-981-99-0969-8_40 Das, Sima & Saha, Sriparna. (2022). Home Automation System Combining Internet-of-Things with Brain–Computer Interfacing. . doi:10.1007/978-981-19-1408-9_11

197

Compilation of References

Das, R., Turkoglu, I., & Sengur, A. (2009). Effective diagnosis of heart disease through neural networks ensembles. Expert Systems with Applications, 36(4), 76757680. doi:10.1016/j. eswa.2008.09.013 De Choudhury, M., Counts, S., Horvitz, E. J., & Hoff, A. (2014, February). Characterizing and predicting postpartum depression from shared facebook data. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 626-638). ACM. 10.1145/2531602.2531675 De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. In Proceedings of the international AAAI conference on web and social media (Vol. 7, pp. 128-137). De Feo, S., Tramarin, R., Ambrosetti, M., Riccio, C., Temporelli, P. L., Favretto, G., Furgi, G., & Griffo, R. (2012). Gender differences in cardiac rehabilitation programs from the Italian survey on cardiac rehabilitation (ISYDE-2008). International Journal of Cardiology, 160(2), 133–139. doi:10.1016/j.ijcard.2011.04.011 PMID:21531469 Dec, G. W. Jr, Palacios, I. F., Fallon, J. T., Aretz, H. T., Mills, J., Lee, D. C.-S., & Johnson, R. A. (1985). Active myocarditis in the spectrum of acute dilated cardiomyopathies: Clinical features, histologic correlates, and clinical outcome. The New England Journal of Medicine, 312(14), 885–890. doi:10.1056/NEJM198504043121404 PMID:3974674 Deng, W., Papavasileiou, I., Qiao, Z., Zhang, W., Lam, K. Y., & Han, S. (2018). Advances in automation technologies for lower extremity neurorehabilitation: A review and future challenges. IEEE Reviews in Biomedical Engineering, 11, 289–305. doi:10.1109/RBME.2018.2830805 PMID:29994006 Dewan, A., & Sharma, M. (2015). Prediction of heart disease using a hybrid technique in data mining classification. 2nd International Conference on Computing for Sustainable Global Development (INDIACom). Dholariya, P. (2017). Suicidal tendency in youth in relation to their gender. GRIN Verlag. Dibben, G., Faulkner, J., Oldridge, N., Rees, K., Thompson, D. R., Zwisler, A. D., & Taylor, R. S. (2021). Exercise‐based cardiac rehabilitation for coronary heart disease. Cochrane Database of Systematic Reviews, 11. PMID:34741536 Dimyan, M. A., & Cohen, L. G. (2011). Neuroplasticity in the context of motor rehabilitation after stroke. Nature Reviews. Neurology, 7(2), 76–85. doi:10.1038/nrneurol.2010.200 PMID:21243015 Dorigo, M., & Birattari, M. (2010). Ant Colony Optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopaedia of machine learning (Springer) (pp. 36–39). Springer US. Dotter, C. T. (1980). Transluminal angioplasty: A long view. Radiology, 135(3), 561–564. doi:10.1148/radiology.135.3.7384436 PMID:7384436

198

Compilation of References

Drotár P. & Smékal, Z. (2014). Comparative study of machine learning techniques for supervised classication of biomedical data. Acta Elec- trotechnica Inf., 14(3), 510. . doi:10.15546/aeei2014-0021 Duberstein, P. R., Conwell, Y., Seidlitz, L., Denning, D. G., Cox, C., & Caine, E. D. (2000). Personality traits and suicidal behavior and ideation in depressed inpatients 50 years of age and older. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 55(1), 18–P26. doi:10.1093/geronb/55.1.P18 PMID:10728121 Durairaj, M., & Revathi, V. (2015).``Prediction of heart disease using back propagation MLP algorithm,’’ Int. J. Sci. Technol. Res., vol. 4, no. 8, pp. 235239. A. Gavhane, G. Kokkula, I. Pandya, and K. Devadkar, (Mar. 2018,). ``Prediction of heart disease using machine learning,’’ in Proc. 2nd Int. Conf. Electron.,Commun. Aerosp. Technol. (ICECA), pp. 12751278. Durongbhan, P., Zhao, Y., Chen, L., Zis, P., De Marco, M., Unwin, Z. C., Venneri, A., He, X., Li, S., Zhao, Y., Blackburn, D. J., & Sarrigiannis, P. G. (2019). A dementia classification framework using frequency and time-frequency features based on EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 826–835. doi:10.1109/TNSRE.2019.2909100 PMID:30951473 Dvir‐Ginzberg, M., Konson, A., Cohen, S., & Agbaria, R. (2007). Entrapment of retroviral vector producer cells in three‐dimensional alginate scaffolds for potential use in cancer gene therapy. Journal of Biomedical Materials Research. Part B, Applied Biomaterials, 80(1), 59–66. doi:10.1002/jbm.b.30568 PMID:16680730 Eapen, V., & Crncec, R. (2012). Strategies and challenges in the management of adolescent depression. Current Opinion in Psychiatry, 25(1), 7–13. doi:10.1097/YCO.0b013e32834de3bd PMID:22156932 Egerton, A., Allison, C., Brett, R. R., & Pratt, J. A. (2006). Cannabinoids and prefrontal cortical function: Insights from preclinical studies. Neuroscience and Biobehavioral Reviews, 30(5), 680–695. doi:10.1016/j.neubiorev.2005.12.002 PMID:16574226 Fabre, A., & Sheppard, M. N. (2006). Sudden adult death syndrome and other nonischaemic causes of sudden cardiac death. Heart (British Cardiac Society), 92(3), 316–320. doi:10.1136/ hrt.2004.045518 PMID:15923280 Feldman, A. M., & McNamara, D. (2000). Myocarditis. The New England Journal of Medicine, 343(19), 1388–1398. doi:10.1056/NEJM200011093431908 PMID:11070105 Fernandes, A. C., Dutta, R., Velupillai, S., Sanyal, J., Stewart, R., & Chandran, D. (2018). Identifying suicide ideation and suicidal attempts in a psychiatric clinical research database using natural language processing. Scientific Reports, 8(1), 1–10. doi:10.103841598-018-25773-2 PMID:29743531 Folsom, A. R., Prineas, R. J., Kaye, S. A., & Soler, J. T. (1989). Body fat distribution and self- reported prevalence of hypertension, heart attack, and other heart disease in older women. International Journal of Epidemiology, 18(2), 361-7. 199

Compilation of References

Frangogiannis, N. G., Smith, C. W., & Entman, M. L. (2000). The inflammatory response in myocardial infarction. Cardiovascular Research, 53(1), 31–47. doi:10.1016/S0008-6363(01)004345 PMID:11744011 Furie, B., & Furie, B. C. (2008). Mechanisms of thrombus formation. The New England Journal of Medicine, 359(9), 938–949. doi:10.1056/NEJMra0801082 PMID:18753650 Gandhi, M., & Singh, S. N. (2015) “Predictions in Heart Disease Using Techniques of Data Mining”, IEEE, ICFTCAKM, p.p.520-525. doi:10.1109/ABLAZE.2015.7154917 Ganna, A., Magnusson, P. K., Pedersen, N. L., de Faire, U., Reilly, M., Ärnlöv, J., & Ingelsson. (2013). Multilocus genetic risk scores for coronary heart disease prediction. Arteriosclerosi Thrombosis, and Vascularbiology, 33(9), 2267-72. Ghosh, Ahona & Das, Sima & Saha, Sriparna. (2022). Stress detection for cognitive rehabilitation in COVID-19 scenario. . doi:10.1049/PBHE042E_ch12 Ghosh, A., & Saha, S. (2020). Interactive game-based motor rehabilitation using hybrid sensor architecture. In Handbook of Research on Emerging Trends and Applications of Machine Learning (pp. 312–337). IGI Global. doi:10.4018/978-1-5225-9643-1.ch015 Ghosh, A., & Saha, S. (2022). Suppression of positive emotions during pandemic era: A deep learning framework for rehabilitation. International Journal of Modelling Identification and Control, 41(1-2), 143–154. doi:10.1504/IJMIC.2022.127101 Ghosh, L., Saha, S., & Konar, A. (2020). Bi-directional Long Short-Term Memory model to analyze psychological effects on gamers. Applied Soft Computing, 95, 106573. doi:10.1016/j. asoc.2020.106573 Ghosh, L., Saha, S., & Konar, A. (2021). Decoding emotional changes of android-gamers using a fused Type-2 fuzzy deep neural network. Computers in Human Behavior, 116, 106640. doi:10.1016/j.chb.2020.106640 Giallauria, F., Vigorito, C., Tramarin, R., Fattirolli, F., Ambrosetti, M., De Feo, S., Griffo, R., & Riccio, C. (2008). Cardiac rehabilitation in very old patients: Data from the Italian Survey on Cardiac Rehabilitation-2008 (ISYDE-2008)—official report of the Italian Association for Cardiovascular Prevention, Rehabilitation, and Epidemiology. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 65(12), 1353–1361. doi:10.1093/gerona/glq138 Gómez Araújo, I., Laier, J. E., & Carrazedo, R. (2019). Enhanced power spectral density transmissibility matrix for operational modal analysis of structures. Journal of Structural Engineering, 145(6), 04019043. doi:10.1061/(ASCE)ST.1943-541X.0002322 González-Ortega, D., Díaz-Pernas, F. J., Martínez-Zarzuela, M., & Antón-Rodríguez, M. (2014). A Kinect-based system for cognitive rehabilitation exercises monitoring. Computer Methods and Programs in Biomedicine, 113(2), 620–631. doi:10.1016/j.cmpb.2013.10.014 PMID:24263055

200

Compilation of References

Green, R. E., Turner, G. R., Thompson, W. F., & Drew, A. (2008). The practicality and effectiveness of rehabilitation interventions for cognitive deficits. Current Opinion in Neurology, 21(6), 683–688. PMID:18989113 Gupta, S., Goel, L., Singh, A., Prasad, A., & Ullah, M. A. (2022). Psychological analysis for depression detection from social networking sites. Computational Intelligence and Neuroscience, 2022, 2022. doi:10.1155/2022/4395358 PMID:35432513 Güven, A., Altınkaynak, M., Dolu, N., İzzetoğlu, M., Pektaş, F., Özmen, S., Demirci, E., & Batbat, T. (2020). Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder. Neural Computing & Applications, 32(12), 8367–8380. doi:10.100700521-019-04294-7 Hänsel, K., Lin, I. W., Sobolev, M., Muscat, W., Yum-Chan, S., De Choudhury, M., Kane, J. M., & Birnbaum, M. L. (2021). Utilizing Instagram data to identify usage patterns associated with schizophrenia spectrum disorders. Frontiers in Psychiatry, 12, 691327. doi:10.3389/ fpsyt.2021.691327 PMID:34483987 Hong, K., Chalup, S. K., & King, R. A. (2014). Affective visual perception using machine pareidolia of facial expressions. IEEE Transactions on Affective Computing, 5(4), 352–363. doi:10.1109/TAFFC.2014.2347960 Houston, K., Haw, C., Townsend, E., & Hawton, K. (2003). General practitioner contacts with patients before and after deliberate self harm. The British Journal of General Practice, 53(490), 365–370. PMID:12830563 Huang, X., Li, X., Liu, T., Chiu, D., Zhu, T., & Zhang, L. (2015, October). Topic model for identifying suicidal ideation in chinese microblog. In Proceedings of the 29th pacific asia conference on language, information and computation (pp. 553-562). ACM. Humphrey, R., Guazzi, M., & Niebauer, J. (2014). Cardiac rehabilitation in Europe. Progress in Cardiovascular Diseases, 56(5), 551–556. doi:10.1016/j.pcad.2013.08.004 PMID:24607021 Hyunjung, L., Jieun, K., & Do-Yeon, K. (2019). Nutritional Therapy for Asian Patients at Risk for Atherosclerotic Cardiovascular Disease. Journal of Lipid and Atherosclerosis, 8(2), 192–203. doi:10.12997/jla.2019.8.2.192 PMID:32821709 Islam, M. R., Assad-Uz-Zaman, M., Spiewak, C., & Rahman, M. H. (2017, April). Motion control of a robotic device for passive rehabilitation of human shoulder and elbow joint movement. In 2017 IEEE Great Lakes Biomedical Conference (GLBC) (pp. 1-1). IEEE. doi:10.1109/ GLBC.2017.7928880 Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health Information Science and Systems, 6(1), 1–12. doi:10.100713755-018-0046-0 PMID:30186594

201

Compilation of References

Islam, R., Raiyan, K. R., Mitra, S., Mannan, M. M. R., Tasnim, T., Putul, A. O., & Mandol, A. B. (2023). Predictis: An IoT and machine learning-based system to predict risk level of cardiovascular diseases. BMC Health Services Research, 23(1), 171. doi:10.118612913-023-09104-4 PMID:36803252 J. Hodges et al.(1981). “Discriminatory analysis, nonparametric discrimination: Consistency properties,”. Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2013, March). Heart disease prediction using lazy associative classification. In 2013 International Multiconferences on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s) (pp. 40- 6). IEEE. Jee, S. H., Jang, Y., Oh, D. J., Oh, B. H., Lee, S. H., Park, S. W., & Yun, Y. D. (2014). A coronary heart disease prediction model: The Korean Heart Study. BMJ Open, 4(5), e005025. doi:10.1136/ bmjopen-2014-005025 PMID:24848088 Jeong, J. H., Kwak, N. S., Guan, C., & Lee, S. W. (2020). Decoding movement-related cortical potentials based on subject-dependent and section-wise spectral filtering. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(3), 687–698. doi:10.1109/ TNSRE.2020.2966826 PMID:31944982 Jiang, X., Bian, G. B., & Tian, Z. (2019). Removal of artifacts from EEG signals: A review. Sensors (Basel), 19(5), 987. doi:10.339019050987 PMID:30813520 Ji, S., Pan, S., Li, X., Cambria, E., Long, G., & Huang, Z. (2020). Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(1), 214–226. doi:10.1109/TCSS.2020.3021467 Ji, S., Yu, C. P., Fung, S. F., Pan, S., & Long, G. (2018). Supervised learning for suicidal ideation detection in online user content. Complexity, 2018, 1–10. doi:10.1155/2018/6157249 Jochumsen, M., Shafique, M., Hassan, A., & Niazi, I. K. (2018). Movement intention detection in adolescents with cerebral palsy from single-trial EEG. Journal of Neural Engineering, 15(6), 066030. doi:10.1088/1741-2552/aae4b8 PMID:30260322 Jutai, J. W., Bhogal, S. K., Foley, N. C., Bayley, M., Teasell, R. W., & Speechley, M. R. (2003). Treatment of visual perceptual disorders post stroke. Topics in Stroke Rehabilitation, 10(2), 77–106. doi:10.1310/07BE-5E1N-735J-1C6U PMID:13680519 Karla, M. K., Lloyd, H. M., Georg, B., George, E. T., Peschon, J. J., Sivasubramanian, N., Entman, M. L., & Mann, D. L. (2000). Endogenous tumor necrosis factor protects the adult cardiac myocyte against ischemic-induced apoptosis in a murine model of acute myocardial infarction. Proceedings of the National Academy of Sciences of the United States of America, 97(10), 5456–5461. doi:10.1073/pnas.070036297 PMID:10779546 Kennedy, J. (2010). Particle Swarm Optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopaedia of Machine Learning (Springer) (pp. 760–766). Springer US.

202

Compilation of References

Kessels, R. P. (2012). Executive functions training in patients with dementia. Journal of Alzheimer’s Disease, 30(s1), S187–S196. Khalil, R. M., & Al-Jumaily, A. (2017, November). Machine learning based prediction of depression among type 2 diabetic patients. In 2017 12th international conference on intelligent systems and knowledge engineering (ISKE) (pp. 1-5). IEEE. 10.1109/ISKE.2017.8258766 Khan, M. M., Alanazi, T. M., Albraikan, A. A., & Almalki, F. A. (2022). IoT-Based Health Monitoring System Development and Analysis. Security and Communication Networks, 2022(9639195), 11. doi:10.1155/2022/9639195 Kim, J., Lee, J., Park, E., & Han, J. (2020). A deep learning model for detecting mental illness from user content on social media. Scientific Reports, 10(1), 1–6. doi:10.103841598-020-68764-y PMID:32678250 Klingel, K., Sauter, M., Bock, C. T., Szalay, G., Schnorr, J. J., & Kandolf, R. (2004). Molecular pathology of inflammatory cardiomyopathy. Medical Microbiology and Immunology, 193(2-3), 101–117. doi:10.100700430-003-0190-1 PMID:12920583 Klingel, K., Stephan, S., Sauter, M., Zell, R., McManus, B. M., Bültmann, B., & Kandolf, R. (1996). Pathogenesis of murine enterovirus myocarditis: Virus dissemination and immune cell targets. Journal of Virology, 70(12), 8888–8895. doi:10.1128/jvi.70.12.8888-8895.1996 PMID:8971018 Klonsky, E. D., May, A. M., & Saffer, B. Y. (2016). Suicide, suicide attempts, and suicidal ideation. Annual Review of Clinical Psychology, 12(1), 307–330. doi:10.1146/annurevclinpsy-021815-093204 PMID:26772209 Kumar, A., Sharma, A., & Arora, A. (2019). Anxious depression prediction in real-time social data. arXiv preprint arXiv:1903.10222. Kumar, P., Garg, S., & Garg, A. (2020). Assessment of anxiety, depression and stress using machine learning models. Procedia Computer Science, 171, 1989–1998. doi:10.1016/j.procs.2020.04.213 Kumar, P., Samanta, P., Dutta, S., Chatterjee, M., & Sarkar, D. (2022). Feature Based Depression Detection from Twitter Data Using Machine Learning Techniques. Journal of Scientific Research, 66(2), 220–228. doi:10.37398/JSR.2022.660229 Lakshmanarao, A., Swathi, Y., & Sundareswar, P. S. S. (2019). Machine learning techniques for heart disease prediction. Int. J. Sci. Technol. Res., 8(11), 97. Lalousis, P. A., Wood, S. J., Schmaal, L., Chisholm, K., Griffiths, S. L., Reniers, R. L., & the PRONIA Consortium. (2021). Heterogeneity and classification of recent onset psychosis and depression: a multimodal machine learning approach. Schizophrenia bulletin, 47(4), 1130-1140. Large, M., Sharma, S., Cannon, E., Ryan, C., & Nielssen, O. (2011). Risk factors for suicide within a year of discharge from psychiatric hospital: A systematic meta-analysis. The Australian and New Zealand Journal of Psychiatry, 45(8), 619–628. doi:10.3109/00048674.2011.590465 PMID:21740345

203

Compilation of References

Latha, C., & Jeeva, S. (2019). Improving the Accuracy of Prediction of Heart Disease Risk Based on Ensemble Classification Techniques.Inform. Med. Unlocked, 2019(16), 100203. Lee, S. Y., Jeong, U., & Cho, K. J. (2015, July). An application of user-friendly control for a Respiratory Rehabilitation and Assistance Robot. In 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 441-446). IEEE. 10.1109/AIM.2015.7222573 Leuthardt, E. C., Miller, K. J., Schalk, G., Rao, R. P., & Ojemann, J. G. (2006). Electrocorticographybased brain computer interface-the Seattle experience. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 194–198. doi:10.1109/TNSRE.2006.875536 PMID:16792292 Levin, A. (2003). The clinical epidemiology of cardiovascular diseases in chronic kidney disease: Clinical. Libby, P., Ridker, P. M., & Maseri, A. (2002). Inflammation and atherosclerosis. Circulation, 105(9), 1135–1143. doi:10.1161/hc0902.104353 PMID:11877368 Li, L., Cai, H., Han, H., Jiang, Q., & Ji, H. (2020). Adaptive short-time Fourier transform and synchrosqueezing transform for non-stationary signal separation. Signal Processing, 166, 107231. doi:10.1016/j.sigpro.2019.07.024 Lin, H., Jia, J., Qiu, J., Zhang, Y., Shen, G., Xie, L., Tang, J., Feng, L., & Chua, T. S. (2017). Detecting stress based on social interactions in social networks. IEEE Transactions on Knowledge and Data Engineering, 29(9), 1820–1833. doi:10.1109/TKDE.2017.2686382 Li, Q., Li, H., & Xiao, L. (2020). Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection. Sensors (Basel), 11(20), 3028. doi:10.339020113028 PMID:32471047 Liu, Y., & Fu, G. (2021). Emotion recognition by deeply learned multichannel textual and EEG features. Future Generation Computer Systems, 119, 1–6. doi:10.1016/j.future.2021.01.010 Li, W., Song, Z., & Sun, Z. (2022). A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network. Frontiers in Computational Neuroscience, 16, 909553. doi:10.3389/fncom.2022.909553 PMID:35782086 Lora, S. K., Sakib, N., Antora, S. A., & Jahan, N. (2020). A comparative study to detect emotions from tweets analyzing machine learning and deep learning techniques. International Journal of Applied Information Systems, 12(30), 6–12. Magnani, J. W., Danik, H. J., Dec, G. W. Jr, & DiSalvo, T. G. (2006). Survival in biopsyproven myocarditis: A long-term retrospective analysis of the histopathologic, clinical, and hemodynamic predictors. American Heart Journal, 151(2), 463–470. doi:10.1016/j.ahj.2005.03.037 PMID:16442915 Magnani, J. W., & Dec, G. W. (2006). Myocarditis: Current trends in diagnosis and treatment. Circulation, 113(6), 876–890. doi:10.1161/CIRCULATIONAHA.105.584532 PMID:16476862

204

Compilation of References

Mahabub, A. (2019). Design and implementation of cost-effective simple FIR filter for EEG signal on FPGA. World Scientific News, (125), 1–17. Mampuya, W. M. (2012). Cardiac rehabilitation past, present and future: An overview. Cardiovascular Diagnosis and Therapy, 2(1), 38. PMID:24282695 Marquez-Chin, C., Marquis, A., & Popovic, M. R. (2016). EEG-triggered functional electrical stimulation therapy for restoring upper limb function in chronic stroke with severe hemiplegia. Case Reports in Neurological Medicine, 2016, 2016. doi:10.1155/2016/9146213 PMID:27882256 Maupomé, D., & Meurs, M. J. (2018). Using Topic Extraction on Social Media Content for the Early Detection of Depression. CLEF (Working Notes), 2125. Maynard, E. M., Nordhausen, C. T., & Normann, R. A. (1997). The Utah intracortical electrode array: A recording structure for potential brain-computer interfaces. Electroencephalography and Clinical Neurophysiology, 102(3), 228–239. doi:10.1016/S0013-4694(96)95176-0 PMID:9129578 Mbarek, A., Jamoussi, S., Charfi, A., & Hamadou, A. B. (2019, September). Suicidal Profiles Detection in Twitter. In WEBIST. doi:10.5220/0008167602890296 McQuaid, R. J., Schwartz, F., Thomas, A., Anisman, H., & Matheson, K. (2019). Suicidal thoughts and attempts in First Nations communities: Links to parental Indian residential school attendance across development. Journal of Developmental Origins of Health and Disease, 10(1), 123–131. doi:10.1017/S2040174418000405 PMID:29923477 Mehta, J. L., & Li, D. Y. (1999). Inflammation in ischemic heart disease: Response to tissue injury or a pathogenetic villain? Cardiovascular Research, 43(2), 291–299. doi:10.1016/S00086363(99)00132-7 PMID:10536659 Menezes, A. R., Lavie, C. J., Milani, R. V., Forman, D. E., King, M., & Williams, M. A. (2014). Cardiac rehabilitation in the United States. Progress in Cardiovascular Diseases, 56(5), 522–529. doi:10.1016/j.pcad.2013.09.018 PMID:24607017 Miao, F., Cheng, Y., He, Y., He, Q., & Li, A. Y. (2015). Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone. Sensors (Basel), 15(5), 11465–11484. doi:10.3390150511465 PMID:25996508 Mitchell, M., Hollingshead, K., & Coppersmith, G. (2015). Quantifying the language of schizophrenia in social media. In Proceedings of the 2nd workshop on Computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 11-20). ACL. 10.3115/ v1/W15-1202 Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access : Practical Innovations, Open Solutions, 7, 8154281554. doi:10.1109/ACCESS.2019.2923707 Mousavian, M., Chen, J., Traylor, Z., & Greening, S. (2021). Depression detection from sMRI and rs-fMRI images using machine learning. Journal of Intelligent Information Systems, 57(2), 395–418. doi:10.100710844-021-00653-w 205

Compilation of References

Murugappan, M., Juhari, M. R. B. M., Nagarajan, R., & Yaacob, S. (2009). An Investigation on visual and audiovisual stimulus based emotion recognition using EEG. International Journal of Medical Engineering and Informatics, 1(3), 342–356. doi:10.1504/IJMEI.2009.022645 N. R. Ratnasari, N. R., Susanto, A., Soesanti, I., & Maesadji. (2013). Thoracic X-ray features extraction using thresholding-based ROI template and PCA-based features selection for lung TB classification purposes. In 3rd Int. Conf. Instrum., Commun., Inf. Technol. Biomed. Eng. (ICICI-BME), Bandung, Indonesia. Nadeem, M. (2016). Identifying depression on Twitter. arXiv preprint arXiv:1607.07384. Nagabushanam, P., George, S. T., Davu, P., Bincy, P., Naidu, M., & Radha, S. (2020, March). Artifact Removal using Elliptic Filter and Classification using 1D-CNN for EEG signals. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 551-556). IEEE. Nahar, J., Imam, T., Tickle, K.S., & Chen, Y. -P. P. (2012). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst. Appl., 40(4), 10861093. . doi:10.1016/j.eswa.2012.08.028 Na, K. S., Cho, S. E., Hong, J. P., Lee, J. Y., Chang, S. M., Jeon, H. J., & Cho, S. J. (2020). Association between personality traits and suicidality by age groups in a nationally representative Korean sample. Medicine, 99(16), e19161. doi:10.1097/MD.0000000000019161 PMID:32311919 Nakajima, R., & Baker, B. J. (2018). Mapping of excitatory and inhibitory postsynaptic potentials of neuronal populations in hippocampal slices using the GEVI, ArcLight. Journal of Physics. D, Applied Physics, 51(50), 504003. doi:10.1088/1361-6463/aae2e3 PMID:30739956 Nasrullah, S., & Jalali, A. (2022). Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model. Computational Intelligence and Neuroscience, 2022, 2022. doi:10.1155/2022/9404242 PMID:35378814 Neogi, A. S., Garg, K. A., Mishra, R. K., & Dwivedi, Y. K. (2021). Sentiment analysis and classification of Indian farmers’ protest using twitter data. International Journal of Information Management Data Insights, 1(2), 100019. doi:10.1016/j.jjimei.2021.100019 Newson, J. J., & Thiagarajan, T. C. (2019). EEG frequency bands in psychiatric disorders: A review of resting state studies. Frontiers in Human Neuroscience, 12, 521. doi:10.3389/ fnhum.2018.00521 PMID:30687041 Nguyen, T., Phung, D., Dao, B., Venkatesh, S., & Berk, M. (2014). Affective and content analysis of online depression communities. IEEE Transactions on Affective Computing, 5(3), 217–226. doi:10.1109/TAFFC.2014.2315623 Nock, M. K., Borges, G., Bromet, E. J., Cha, C. B., Kessler, R. C., & Lee, S. (2008). Suicide and suicidal behavior. Epidemiologic Reviews, 30(1), 133–154. doi:10.1093/epirev/mxn002 PMID:18653727

206

Compilation of References

Nock, M. K., Borges, G., Bromet, E. J., Cha, C. B., Kessler, R. C., & Lee, S. (2012). The epidemiology of suicide and suicidal behavior. In M. K. Nock, G. Borges, & Y. Ono (Eds.), Suicide: Global perspectives from the WHO World Mental Health Surveys (pp. 5–32). Cambridge University Press. O’dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., & Christensen, H. (2015). Detecting suicidality on Twitter. Internet Interventions : the Application of Information Technology in Mental and Behavioural Health, 2(2), 183–188. doi:10.1016/j.invent.2015.03.005 Olaniyi, E. O., Oyedotun, O. K., & Adnan, K. (2015). Heart diseases diagnosis using neural networks arbitration. International Journal of Intelligent Systems and Applications, 7(12), 72. doi:10.5815/ijisa.2015.12.08 Orabi, A. H., Buddhitha, P., Orabi, M. H., & Inkpen, D. (2018, June). Deep learning for depression detection of twitter users. In Proceedings of the fifth workshop on computational linguistics and clinical psychology: from keyboard to clinic (pp. 88-97). ACL. Ordonez, C. (2006). Association rule discovery with the train and test approach for heart disease prediction. IEEE Transactions on Information Technology in Biomedicine, 10(2), 334–343. doi:10.1109/TITB.2006.864475 PMID:16617622 Ornish, D. (1990). Can lifestyle changes reverse coronary heart disease? The Lifestyle Heart Trial. Lancet. [CrossRef] doi:10.1016/0140-6736(90)91656-U Pandey, A. R., Bista, B., Dhungana, R. R., Aryal, K. K., Chalise, B., & Dhimal, M. (2019). Factors associated with suicidal ideation and suicidal attempts among adolescent students in Nepal: Findings from Global School-based Students Health Survey. PLoS One, 14(4), e0210383. doi:10.1371/journal.pone.0210383 PMID:31002715 Park, M., Cha, C., & Cha, M. (2012). Depressive moods of users portrayed in Twitter. In Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD 2012 (pp. 1-8). ACM. Parthiban, L., & Subramanian, R. (2008). Intelligent heart disease prediction system using CANFIS and genetic algorithm International Journal of Biological. Biomedical and Medical Sciences, 3, 3. Pasquali, S. K., Alexander, K. P., & Peterson, E. D. (2001). Cardiac rehabilitation in the elderly. American Heart Journal, 142(5), 748–755. doi:10.1067/mhj.2001.119134 PMID:11685158 Patel, M. J., Andreescu, C., Price, J. C., Edelman, K. L., Reynolds, C. F. III, & Aizenstein, H. J. (2015). Machine learning approaches for integrating clinical and imaging features in late‐life depression classification and response prediction. International Journal of Geriatric Psychiatry, 30(10), 1056–1067. doi:10.1002/gps.4262 PMID:25689482 Patel, S., & Chauhan, Y. (2014). Heart attack detection and medical attention using motion sensing device -kinect. International Journal of Scientific and Research Publications, 4(1), 1–4.

207

Compilation of References

Paul, S., & Ghosh, A. (2023). A Study on the Application Domains of Electroencephalogram for the Deep Learning-Based Transformative Healthcare. Generative Adversarial Networks and Deep Learning: Theory and Applications. Paul, S., Jandhyala, S. K., & Basu, T. (2018, August). Early Detection of Signs of Anorexia and Depression Over Social Media using Effective Machine Learning Frameworks. In CLEF (Working notes). Piller, L. B., Davis, B. R., Cutler, J. A., Cushman, W. C., Wright, J. T., Williamson, J. D., & Haywood, L. J. (2002). Validation of heart failure events in the Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) participants assigned to doxazosin and chlorthalidone. Current Controlled Trials in Cardiovascular Medicine, 3(1), 10. doi:10.1186/1468-6708-3-10 PMID:12459039 Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33–57. doi:10.100711721-007-0002-0 Ponsford, J., Bayley, M., Wiseman-Hakes, C., Togher, L., Velikonja, D., McIntyre, A., ... Tate, R. (2012). INCOG recommendations for management of cognition following traumatic brain injury, part I: Posttraumatic amnesia/delirium. The Journal of Head Trauma Rehabilitation, 27(2), 118–129. PMID:24984094 Pouriyeh, S., Vahid, S., Sannino, G., De Pietro, G., Arabnia, H., & Gutierrez, J. (2017). A Comprehensive Investigation and Comparison of Machine Learning Techniques in the Domain of Heart Disease. 22nd IEEE Symposium on Computers and Communication (ISCC 2017). IEEE. 10.1109/ISCC.2017.8024530 Pradhan, B., Bhattacharyya, S., & Pal, K. (2021). IoT-Based Applications in Healthcare Devices. Journal of Healthcare Engineering, 6632599. PMID:33791084 Preoţiuc-Pietro, D., Eichstaedt, J., Park, G., Sap, M., Smith, L., Tobolsky, V., & Ungar, L. (2015). The role of personality, age, and gender in tweeting about mental illness. In Proceedings of the 2nd workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 21-30). ACL. 10.3115/v1/W15-1203 Priya, A., Garg, S., & Tigga, N. P. (2020). Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Computer Science, 167, 1258-1267. Rahman, M. H., Saad, M., Kenné, J. P., & Archambault, P. S. (2012). Nonlinear sliding mode control implementation of an upper limb exoskeleton robot to provide passive rehabilitation therapy. In Intelligent Robotics and Applications: 5th International Conference, ICIRA 2012, Montreal, Canada, October 3-5, 2012 [Springer Berlin Heidelberg.]. Proceedings, 5(Part II), 52–62. Raihan, M., Mondal, S., More, A., Sagor, M. O. F., Sikder, G., Majumder, M. A., & Ghosh, K. (2016). Smartphone based ischemic heart disease (heart attack) risk prediction using clinical data and data mining approaches, a prototype design. In 2016 19th International Conference on Computer and Information Technology (ICCIT) (pp. 299-303). IEEE.

208

Compilation of References

Rajathi, S. & Radhamani, G. (2016). Prediction and Analysis of Rheumatic Heart Disease using kNN Classification with ACO. IEEE. Rajdhan, A. (2020). Heart Disease Prediction using Machine Learning. International Journal of Engineering Research & Technology (Ahmedabad), 9. Ramírez-Cifuentes, D., Freire, A., Baeza-Yates, R., Puntí, J., Medina-Bravo, P., Velazquez, D. A., Gonfaus, J. M., & Gonzàlez, J. (2020). Detection of suicidal ideation on social media: Multimodal, relational, and behavioral analysis. Journal of Medical Internet Research, 22(7), e17758. doi:10.2196/17758 PMID:32673256 Rao, R. P. (1999). An optimal estimation approach to visual perception and learning. Vision Research, 39(11), 1963–1989. doi:10.1016/S0042-6989(98)00279-X PMID:10343783 Rao, T. S. M., Kompalli, V. S., & Kompalli, U. (2020). A model to detect social network mental disorders using AI techniques. Journal of Critical Reviews, 7(15), 2582–2587. Rastogi, A., Liu, Q., & Cambria, E. (2022, July). Stress Detection from Social Media Articles: New Dataset Benchmark and Analytical Study. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. 10.1109/IJCNN55064.2022.9892889 Ravish, D. K., Shanthi, K. J., Shenoy, N. R., & Nisargh, S. (2014). Heart function monitoring, prediction and prevention of heart attacks: Using articial neural networks. In Proc. Int. Conf. Contemp. Comput. Inform. (IC3I), (pp. 16). IEEE. 10.1109/IC3I.2014.7019580 Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6(1), 15. doi:10.1140/epjds13688-017-0110-z Reed, G. W., Jeffrey, E. R., & Christopher, P. C. (2017). Acute myocardial infarction. Lancet, 389(10065), 14–20, 197–210. doi:10.1016/S0140-6736(16)30677-8 PMID:27502078 Resnik, P., Armstrong, W., Claudino, L., Nguyen, T., Nguyen, V. A., & Boyd-Graber, J. (2015). Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter. In Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality (pp. 99-107). 10.3115/v1/W15-1212 Rho, G., Callara, A. L., Petri, G., Nardelli, M., Scilingo, E. P., Greco, A., & Pascalis, V. D. (2021). Linear and Nonlinear Quantitative EEG Analysis during Neutral Hypnosis following an Opened/ Closed Eye Paradigm. Symmetry, 13(8), 1423. doi:10.3390ym13081423 Ricard, B. J., Marsch, L. A., Crosier, B., & Hassanpour, S. (2018). Exploring the utility of community-generated social media content for detecting depression: An analytical study on Instagram. Journal of Medical Internet Research, 20(12), e11817. doi:10.2196/11817 PMID:30522991 Ríssola, E. A., Aliannejadi, M., & Crestani, F. (2020, April). Beyond modelling: understanding mental disorders in online social media. In European Conference on Information Retrieval (pp. 296-310). Springer, Cham. 10.1007/978-3-030-45439-5_20

209

Compilation of References

Rizhky, A. (2023). QRS Detection On Heart Rate Variability Readings using Two Moving Average Methods. Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics., 5(1). doi:10.35882/ijeeemi.v5i1.258 Rohling, M. L., Faust, M. E., Beverly, B., & Demakis, G. (2009). Effectiveness of cognitive rehabilitation following acquired brain injury: A meta-analytic re-examination of Cicerone et al.’s (2000, 2005) systematic reviews. Neuropsychology, 23(1), 20–39. doi:10.1037/a0013659 PMID:19210030 Roy, A. K., Soni, Y., & Dubey, S. (2013, August). Enhancing effectiveness of motor rehabilitation using kinect motion sensing technology. In 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS) (pp. 298-304). IEEE. 10.1109/GHTC-SAS.2013.6629934 Sagar, R., Dandona, R., Gururaj, G., Dhaliwal, R. S., Singh, A., Ferrari, A., Dua, T., Ganguli, A., Varghese, M., Chakma, J. K., Kumar, G. A., Shaji, K. S., Ambekar, A., Rangaswamy, T., Vijayakumar, L., Agarwal, V., Krishnankutty, R. P., Bhatia, R., Charlson, F., & Dandona, L. (2020). The burden of mental disorders across the states of India: The Global Burden of Disease Study 1990–2017. The Lancet. Psychiatry, 7(2), 148–161. doi:10.1016/S2215-0366(19)30475-4 PMID:31879245 Saha, S., & Das, N. (2021). A Kinect-based motor rehabilitation system for stroke recovery. In Handbook of Computational Intelligence in Biomedical Engineering and Healthcare (pp. 261–282). Academic Press. doi:10.1016/B978-0-12-822260-7.00007-8 Samanta, P., Kumar, P., Dutta, S., Chatterjee, M., & Sarkar, D. (2023). Depression Detection from Twitter Data Using Two Level Multi-modal Feature Extraction. In International Conference on Data Management, Analytics & Innovation (pp. 451-465). Springer, Singapore. https://doi. org/10.1007/978-981-19-2600-6_32 Saputro, I. R. D., Maryati, N. D., Solihati, S. R., Wijayanto, I., Hadiyoso, S., & Patmasari, R. (2019, May). Seizure type classification on EEG signal using support vector machine. [). IOP Publishing.]. Journal of Physics: Conference Series, 1201(1), 012065. doi:10.1088/17426596/1201/1/012065 Sarkar, D., Kumar, P., Samanta, P., Dutta, S., & Chatterjee, M. (2022). A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media. [IJSI]. International Journal of Software Innovation, 10(1), 1–22. doi:10.4018/IJSI.309114 Sarkar, D., Samanta, P., Kumar, P., & Chatterjee, M. (2023). Feature-based suicide-ideation detection from Twitter data using machine learning techniques. In Hybrid Computational Intelligent Systems (pp. 237–249). CRC Press. doi:10.1201/9781003381167-16 Sarmah, S. S. (2020). An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network. IEEE Access : Practical Innovations, Open Solutions, 8, 135784–135797. doi:10.1109/ACCESS.2020.3007561

210

Compilation of References

Sawada, H., Ono, N., Kameoka, H., Kitamura, D., & Saruwatari, H. (2019). A review of blind source separation methods: Two converging routes to ILRMA originating from ICA and NMF. APSIPA Transactions on Signal and Information Processing, 8(1), e12. doi:10.1017/ATSIP.2019.5 Schaechter, J. D. (2004). Motor rehabilitation and brain plasticity after hemiparetic stroke. Progress in Neurobiology, 73(1), 61–72. doi:10.1016/j.pneurobio.2004.04.001 PMID:15193779 Schwartz, H. A., Eichstaedt, J., Kern, M., Park, G., Sap, M., Stillwell, D., & Ungar, L. (2014, June). Towards assessing changes in degree of depression through facebook. In Proceedings of the workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality (pp. 118-125). ACL. 10.3115/v1/W14-3214 Scrivener, C. L., & Reader, A. T. (2022). Variability of EEG electrode positions and their underlying brain regions: Visualizing gel artifacts from a simultaneous EEG‐fMRI dataset. Brain and Behavior, 12(2), e2476. doi:10.1002/brb3.2476 PMID:35040596 Seabrook, E. M., Kern, M. L., Fulcher, B. D., & Rickard, N. S. (2018). Predicting depression from language-based emotion dynamics: Longitudinal analysis of Facebook and Twitter status updates. Journal of Medical Internet Research, 20(5), e168. doi:10.2196/jmir.9267 PMID:29739736 Segal, A. D., Lesak, M. C., Silverman, A. K., & Petruska, A. J. (2020). A Gesture-Controlled Rehabilitation Robot to Improve Engagement and Quantify Movement Performance. Sensors (Basel), 20(15), 4269. doi:10.339020154269 PMID:32751876 Setiawan, H., Islamiyah, W. R., Wibawa, A. D., & Purnomo, M. H. (2019, August). Identifying EEG parameters to monitor stroke rehabilitation using individual analysis. In 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA) (pp. 337-342). IEEE. 10.1109/ ISITIA.2019.8937238 Sharma, A., Sanghvi, K., & Churi, P. (2022). The impact of Instagram on young Adult’s social comparison, colourism and mental health: Indian perspective. International Journal of Information Management Data Insights, 2(1), 100057. doi:10.1016/j.jjimei.2022.100057 Shen, G., Jia, J., Nie, L., Feng, F., Zhang, C., Hu, T., & Zhu, W. (2017, August). Depression detection via harvesting social media: A multimodal dictionary learning solution. In IJCAI (pp. 3838-3844). doi:10.24963/ijcai.2017/536 Shepherd, C. W., & While, A. E. (2012). Cardiac rehabilitation and quality of life: A systematic review. International Journal of Nursing Studies, 49(6), 755–771. doi:10.1016/j. ijnurstu.2011.11.019 PMID:22197653 Shinde, R., Arjun, S., Patil, P., & Waghmare, J. (2015). An intelligent heart disease prediction system using k-means clustering and Naïve Bayes algorithm. International Journal of Computer Science and Information Technologies, 6(1), 637–639.

211

Compilation of References

Shing, H. C., Nair, S., Zirikly, A., Friedenberg, M., Daumé, H., III, & Resnik, P. (2018, June). Expert, crowdsourced, and machine assessment of suicide risk via online postings. In Proceedings of the fifth workshop on computational linguistics and clinical psychology: from keyboard to clinic (pp. 25-36). ACL. 10.18653/v1/W18-0603 Shi, Y. (2004). Particle swarm optimization. IEEE Connect., 2(1), 8–13. Song, H., You, J., Chung, J. W., & Park, J. C. (2018). Feature attention network: Interpretable depression detection from social media. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation. ACL. Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 43–48. doi:10.5120/2237-2860 Sreeshakthy, M., Preethi, J., & Dhilipan, A. (2016). A survey on emotion classification from EEG signal using various techniques and performance analysis. [IJITCS]. International Journal of Information Technology and Computer Science, 8(12), 19–26. doi:10.5815/ijitcs.2016.12.03 Stankevich, M., Smirnov, I., Kiselnikova, N., & Ushakova, A. (2020). Depression detection from social media profiles. In Data Analytics and Management in Data Intensive Domains: 21st International Conference, DAMDID/RCDL 2019, (pp. 181-194). Springer International Publishing. Statistica. (2022). Share of mental disorders among adults in India 2017, by classification. Statista. https://www.statista.com/statistics/1125252/india-share-of-mental-disorders-amongadults-by-classification/ Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019). Detection of depression-related posts in reddit social media forum. IEEE Access : Practical Innovations, Open Solutions, 7, 44883–44893. doi:10.1109/ACCESS.2019.2909180 Takci, H. (2018). Improvement of heart attack prediction by the feature selection methods. Turkish Journal of Electrical Engineering and Computer Sciences, 26(1), 1–10. doi:10.3906/elk-1611-235 The Indian Express. (2022) Mental health in India-A prespective. The Indian Express. https:// indianexpress.com/article/lifestyle/health/mental-health-in-india-a-perspective-anxietydepression-stress-7764309/ Thompson, D. R., & Clark, A. M. (2009). Cardiac rehabilitation: Into the future. Heart (British Cardiac Society), 95(23), 1897–1900. doi:10.1136/hrt.2009.173732 PMID:19815940 Toglia, J. P. (1989). Visual perception of objects: An approach to assessment and intervention. The American Journal of Occupational Therapy, 43(9), 587–595. doi:10.5014/ajot.43.9.587 PMID:2817075 Trauzettel-Klosinski, S. (2011). Current methods of visual rehabilitation. Deutsches Ärzteblatt International, 108(51-52), 871. PMID:22259642

212

Compilation of References

Trujillo, P., Mastropietro, A., Scano, A., Chiavenna, A., Mrakic-Sposta, S., Caimmi, M., Molteni, F., & Rizzo, G. (2017). Quantitative EEG for predicting upper limb motor recovery in chronic stroke robot-assisted rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(7), 1058–1067. doi:10.1109/TNSRE.2017.2678161 PMID:28278477 Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., & Ohsaki, H. (2015). Recognizing depression from twitter activity. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 3187-3196). ACM. Tsvetkova, T. (2010). Calculated, passionate, pious extremism: Beyond a rational choice theory of suicide terrorism. Asian Journal of Social Science, 38(3), 394–415. doi:10.1163/156853110X499945 Turk-Adawi, K., Sarrafzadegan, N., & Grace, S. L. (2014). Global availability of cardiac rehabilitation. Nature Reviews. Cardiology, 11(10), 586–596. doi:10.1038/nrcardio.2014.98 PMID:25027487 Tyshchenko, Y. (2018). Depression and anxiety detection from blog posts data. Nature Precis. Sci., Inst. Comput. Sci., Univ. Uddin, M. Z., Dysthe, K. K., Følstad, A., & Brandtzaeg, P. B. (2022). Deep learning for prediction of depressive symptoms in a large textual dataset. Neural Computing & Applications, 34(1), 721–744. doi:10.100700521-021-06426-4 Vaid, S., Singh, P., & Kaur, C. (2015, February). EEG signal analysis for BCI interface: A review. In 2015 fifth international conference on advanced computing & communication technologies (pp. 143-147). IEEE. 10.1109/ACCT.2015.72 van Iersel, M. B., Hoefsloot, W., Munneke, M., Bloem, B. R., & Olde Rikkert, M. G. M. (2004). Systematic review of quantitative clinical gait analysis in patients with dementia. Zeitschrift für Gerontologie und Geriatrie, 37(1), 27–32. doi:10.100700391-004-0176-7 PMID:14991293 Vidyaratne, L. S., & Iftekharuddin, K. M. (2017). Real-time epileptic seizure detection using EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(11), 2146–2156. doi:10.1109/TNSRE.2017.2697920 PMID:28459693 Virmani, R., Kolodgie, F. D., Burke, A. P., Finn, A. V., Gold, H. K., Tulenko, T. N., Wrenn, S. P., & Narula, J. (2005). Atherosclerotic plaque progression and vulnerability to rupture: Angiogenesis as a source of intraplaque hemorrhage. Arteriosclerosis, Thrombosis, and Vascular Biology, 25(10), 2054–2061. doi:10.1161/01.ATV.0000178991.71605.18 PMID:16037567 Volkow, N. D., Fowler, J. S., & Wang, G. J. (2004). The addicted human brain viewed in the light of imaging studies: Brain circuits and treatment strategies. Neuropharmacology, 47, 3–13. doi:10.1016/j.neuropharm.2004.07.019 PMID:15464121 Wahab, A., Zaheer, M. S., Wahab, S., & Khan, R. A. (2008). Acute aluminium phosphide poisoning: An update. Hong Kong Journal of Emergency Medicine, 15(3), 152–155. doi:10.1177/102490790801500306

213

Compilation of References

Wang, J., & Wang, M. (2021). Review of the emotional feature extraction and classification using EEG signals. Cognitive robotics, 1, 29-40. Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., & Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. In Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2013 International Workshops, (pp. 201-213). Springer Berlin Heidelberg. 10.1007/978-3-642-40319-4_18 Wang, T., Brede, M., Ianni, A., & Mentzakis, E. (2017, February). Detecting and characterizing eating-disorder communities on social media. In Proceedings of the Tenth ACM International conference on web search and data mining (pp. 91-100). ACM. 10.1145/3018661.3018706 Wang, W. (2019). Stigma and counter-stigma frames, cues, and exemplification: Comparing news coverage of depression in the English-and Spanish-language media in the US. Health Communication, 34(2), 172–179. doi:10.1080/10410236.2017.1399505 PMID:29131692 Wenger, N. K. (2008). Current status of cardiac rehabilitation. Journal of the American College of Cardiology, 51(17), 1619–1631. doi:10.1016/j.jacc.2008.01.030 PMID:18436113 Wilaiprasitporn, D., Matchaparn, T., & Banluesombatkul, C. (2020). Affective EEG-Based Person Identification Using the Deep Learning Approach. IEEE Transactions on Cognitive and Developmental Systems, 3(12), 486–496. doi:10.1109/TCDS.2019.2924648 Wilson, B. A., Emslie, H., Quirk, K., & Evans, J. J. (2001). Reducing everyday memory and planning problems by means of a paging system: A randomised control crossover study. Journal of Neurology, Neurosurgery, and Psychiatry, 70(4), 477–482. doi:10.1136/jnnp.70.4.477 PMID:11254770 Wolgast, G., Ehrenborg, C., Israelsson, A., Helander, J., Johansson, E., & Manefjord, H. (2016). Wireless body area network for heart attack detection. IEEE Antennas & Propagation Magazine, 58(5), 84–92. doi:10.1109/MAP.2016.2594004 Wolohan, J. T., Hiraga, M., Mukherjee, A., Sayyed, Z. A., & Millard, M. (2018, August). Detecting linguistic traces of depression in topic-restricted text: Attending to self-stigmatized depression with NLP. In Proceedings of the first international workshop on language cognition and computational models (pp. 11-21). ACL. Wongkoblap, A., Vadillo, M. A., & Curcin, V. (2017). Researching mental health disorders in the era of social media: Systematic review. Journal of Medical Internet Research, 19(6), e7215. doi:10.2196/jmir.7215 PMID:28663166 World Health Organization. (n.d.). Depression. WHO. https://www.who.int/india/health-topics/ depression Xiong, P., Lee, S. M., Chan, G. (2022). Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med, 25(9), 860032.

214

Compilation of References

Xu, H., & Plataniotis, K. N. (2012, September). Affect recognition using EEG signal. In 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) (pp. 299-304). IEEE. 10.1109/MMSP.2012.6343458 Yalamanchili, B., Kota, N. S., Abbaraju, M. S., Nadella, V. S. S., & Alluri, S. V. (2020, February). Real-time acoustic based depression detection using machine learning techniques. In 2020 International conference on emerging trends in information technology and engineering (icETITE) (pp. 1-6). IEEE. 10.1109/ic-ETITE47903.2020.394 Yasin, S., Hussain, S. A., Aslan, S., Raza, I., Muzammel, M., & Othmani, A. (2021). EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review. Computer Methods and Programs in Biomedicine, 202, 106007. doi:10.1016/j.cmpb.2021.106007 PMID:33657466 Zajdel, W., Miszalski-Jamka, T., Legutko, J., Zalewski, J., Zmudka, K., & Peszek, E. (2022). Cardiac Magnetic Resonance Shows Improved Outcomes in Patients with an ST-Segment Elevation Myocardial Infarction and a High Thrombus Burden Treated with Adjuvant Aspiration Thrombectomy. Journal of Clinical Medicine, 11(17), 5000. doi:10.3390/jcm11175000 PMID:36078930 Zaman, S., & Touq, R. (2017). Codon based back propagation neural network approach to classify hypertension gene sequences,. In Proc. Int. Conf.Elect., Comput. Commun. Eng. (ECCE), (pp. 443446). IEEE. 10.1109/ECACE.2017.7912945 Zeng, L. L., Shen, H., Liu, L., & Hu, D. (2014). Unsupervised classification of major depression using functional connectivity MRI. Human Brain Mapping, 35(4), 1630–1641. doi:10.1002/ hbm.22278 PMID:23616377 Zhang, W., & Han, J. (2017). Towards heart sound classication without segmentation using convolutional neural network. Computers in Cardiology, 44, 14. Zhao, D., Tang, F., Si, B., & Feng, X. (2019). Learning joint space–time–frequency features for EEG decoding on small labeled data. Neural Networks, 114, 67–77. doi:10.1016/j.neunet.2019.02.009 PMID:30897519 Zhao, Y., & Zhu, X. (2020). Design of Hospital IoT System and Drug Intervention in Patients with Acute Myocardial Infarction. Microprocessors and Microsystems, 103662. doi:10.1016/j. micpro.2020.103662 Zhou, Y., Zhan, J., & Luo, J. (2017). Predicting multiple risky behaviors via multimedia content. In Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 1315, 2017 [Springer International Publishing.]. Proceedings, 9(Part II), 65–73. Zimmerman, M., & Coryell, W. (1987). The Inventory to Diagnose Depression (IDD): A selfreport scale to diagnose major depressive disorder. Journal of Consulting and Clinical Psychology, 55(1), 55–59. doi:10.1037/0022-006X.55.1.55 PMID:3571659

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Recommended Readings

To continue our tradition of advancing information science and technology research, we have compiled a list of recommended IGI Global readings. These references will provide additional information and guidance to further enrich your knowledge and assist you with your own research and future publications.

Abu Seman, S. A., & Ramayah, T. (2017). Are We Ready to App?: A Study on mHealth Apps, Its Future, and Trends in Malaysia Context. In J. Pelet (Ed.), Mobile Platforms, Design, and Apps for Social Commerce (pp. 69–83). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2469-4.ch005 Adeleke, I. T., & Abdul, Q. B. (2020). Opinions on Cyber Security, Electronic Health Records, and Medical Confidentiality: Emerging Issues on Internet of Medical Things From Nigeria. In P. Pankajavalli & G. Karthick (Eds.), Incorporating the Internet of Things in Healthcare Applications and Wearable Devices (pp. 199–211). IGI Global. https://doi.org/10.4018/978-1-7998-1090-2.ch012 Aggarwal, S., & Azad, V. (2017). A Hybrid System Based on FMM and MLP to Diagnose Heart Disease. In S. Bhattacharyya, S. De, I. Pan, & P. Dutta (Eds.), Intelligent Multidimensional Data Clustering and Analysis (pp. 293–325). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1776-4.ch011 Al-Busaidi, S. S. (2018). Interdisciplinary Relationships Between Medicine and Social Sciences. In M. Al-Suqri, A. Al-Kindi, S. AlKindi, & N. Saleem (Eds.), Promoting Interdisciplinarity in Knowledge Generation and Problem Solving (pp. 124–137). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3878-3.ch009

Recommended Readings

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217

Recommended Readings

Aslan, F. (2021). Could There Be an Alternative Method of Media Literacy in Promoting Health in Children and Adolescents? Media Literacy and Health Promotion. In G. Sarı (Eds.), Handbook of Research on Representing Health and Medicine in Modern Media (pp. 191-199). IGI Global. https://doi.org/10.4018/9781-7998-6825-5.ch013 Atzori, B., Hoffman, H. G., Vagnoli, L., Messeri, A., & Grotto, R. L. (2018). Virtual Reality as Distraction Technique for Pain Management in Children and Adolescents. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 5955-5965). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2255-3.ch518 Audibert, M., Mathonnat, J., Pélissier, A., & Huang, X. X. (2018). The Impact of the New Rural Cooperative Medical Scheme on Township Hospitals’ Utilization and Income Structure in Weifang Prefecture, China. In I. Management Association (Ed.), Health Economics and Healthcare Reform: Breakthroughs in Research and Practice (pp. 109-121). Hershey, PA: IGI Global. https://doi.org/ doi:10.4018/9781-5225-3168-5.ch007 Aulia, A., & Pratiti, B. (2022). Cyberchondria and Medical Student Syndrome: An Anxious Path to Be an Anxiety Healer? In H. Aker & M. Aiken (Eds.), Handbook of Research on Cyberchondria, Health Literacy, and the Role of Media in Society’s Perception of Medical Information (pp. 98–113). IGI Global. https:// doi.org/10.4018/978-1-7998-8630-3.ch006 Babu, A., & Ayyappan, S. (2017). A Methodological Evaluation of CryptoWatermarking System for Medical Images. In C. Bhatt & S. Peddoju (Eds.), Cloud Computing Systems and Applications in Healthcare (pp. 189–217). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1002-4.ch010 Bakke, A. (2017). Ethos in E-Health: From Informational to Interactive Websites. In M. Folk & S. Apostel (Eds.), Establishing and Evaluating Digital Ethos and Online Credibility (pp. 85–103). Hershey, PA: IGI Global. doi:10.4018/978-15225-1072-7.ch005 Bali, S. (2018). Enhancing the Reach of Health Care Through Telemedicine: Status and New Possibilities in Developing Countries. In U. Pandey & V. Indrakanti (Eds.), Open and Distance Learning Initiatives for Sustainable Development (pp. 339–354). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2621-6.ch019

218

Recommended Readings

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219

Recommended Readings

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220

Recommended Readings

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221

Recommended Readings

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222

Recommended Readings

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Recommended Readings

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Recommended Readings

Ghosh, D., & Dinda, S. (2017). Health Infrastructure and Economic Development in India. In R. Das (Ed.), Social, Health, and Environmental Infrastructures for Economic Growth (pp. 99–119). Hershey, PA: IGI Global. doi:10.4018/978-15225-2364-2.ch006 Ghosh, K., & Sen, K. C. (2017). The Potential of Crowdsourcing in the Health Care Industry. In N. Wickramasinghe (Ed.), Handbook of Research on Healthcare Administration and Management (pp. 418–427). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0920-2.ch024 Gonzalez-Urquijo, M., Macias-Rodriguez, Y., & Davila-Rivas, J. A. (2022). The Role of Telemedicine and Globalization in Medical Education. In M. Lopez (Ed.), Advancing Health Education With Telemedicine (pp. 288–295). IGI Global. https:// doi.org/10.4018/978-1-7998-8783-6.ch015 Gopalan, V., Chan, E., & Ho, D. T. (2018). Deliberate Self-Harm and Suicide Ideology in Medical Students. In C. Smith (Ed.), Exploring the Pressures of Medical Education From a Mental Health and Wellness Perspective (pp. 122–143). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2811-1.ch005 Goswami, N. (2021). Effects of Spaceflight, Aging, and Bedrest on Falls: Aging Meets Spaceflight! In P. Eklund (Ed.), Integrated Care and Fall Prevention in Active and Healthy Aging (pp. 91–106). IGI Global. https://doi.org/10.4018/978-1-79984411-2.ch005 Goswami, S., Dey, U., Roy, P., Ashour, A., & Dey, N. (2017). Medical Video Processing: Concept and Applications. In N. Dey, A. Ashour, & P. Patra (Eds.), Feature Detectors and Motion Detection in Video Processing (pp. 1–17). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1025-3.ch001 Goswami, S., Mahanta, K., Goswami, S., Jigdung, T., & Devi, T. P. (2018). Ageing and Cancer: The Epigenetic Basis, Alternative Treatment, and Care. In B. Prasad & S. Akbar (Eds.), Handbook of Research on Geriatric Health, Treatment, and Care (pp. 206–235). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3480-8.ch012 Gouva, M. I. (2017). The Psychological Impact of Medical Error on Patients, Family Members, and Health Professionals. In M. Riga (Ed.), Impact of Medical Errors and Malpractice on Health Economics, Quality, and Patient Safety (pp. 171–196). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2337-6.ch007 Gündüz, U., & Pembecioğlu, N. (2021). Case Study of Gender and Career Choices: Being and Becoming Medical People. In G. Sarı (Ed.), Handbook of Research on Representing Health and Medicine in Modern Media (pp. 269-307). IGI Global. https://doi.org/10.4018/978-1-7998-6825-5.ch018 225

Recommended Readings

Gupta, A. (2021). Artificial Intelligence Approaches to Detect Neurodegenerative Disease From Medical Records: A Perspective. In G. Rani & P. Tiwari (Eds.), Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning (pp. 254–267). IGI Global. https://doi.org/10.4018/978-1-7998-2742-9. ch013 Gürcü, M., & Tengilimoğlu, D. (2017). Health Tourism-Based Destination Marketing. In A. Bayraktar & C. Uslay (Eds.), Strategic Place Branding Methodologies and Theory for Tourist Attraction (pp. 308–331). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0579-2.ch015 Gürsel, G. (2017). For Better Healthcare Mining Health Data. In S. Bhattacharyya, S. De, I. Pan, & P. Dutta (Eds.), Intelligent Multidimensional Data Clustering and Analysis (pp. 135–158). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1776-4. ch006 Gyaase, P. O., Darko-Lartey, R., William, H., & Borkloe, F. (2017). Towards an Integrated Electronic Medical Records System for Quality Healthcare in Ghana: An Exploratory Factor Analysis. International Journal of Computers in Clinical Practice, 2(2), 38–55. doi:10.4018/IJCCP.2017070103 Hagle, H. N., Liu, Y., Murphy, D. M., & Krom, L. (2022). Innovative Adaptation of Training and Technical Assistance: Education for the Behavioral Health Workforce. In C. Ford & K. Garza (Eds.), Handbook of Research on Updating and Innovating Health Professions Education: Post-Pandemic Perspectives (pp. 239–264). IGI Global. https://doi.org/10.4018/978-1-7998-7623-6.ch011 Halis, M., & Halis, M. (2021). The Relationship Between Organizational Culture and Organizational Commitment: A Research in Private Health Institutions. In G. Sarı (Ed.), Handbook of Research on Representing Health and Medicine in Modern Media (pp. 308-329). IGI Global. https://doi.org/10.4018/978-1-7998-6825-5.ch019 Halis, M., Tepret, S., Çamlibel, Z., & Halis, M. (2021). Competitiveness in Medical Tourism: An Evaluation on Kocaeli Medical Tourism Market. In G. Sarı (Ed.),  Handbook of Research on Representing Health and Medicine in Modern Media (pp. 239-268). IGI Global. https://doi.org/10.4018/978-1-7998-6825-5.ch017 Hanel, P. (2017). Is China Catching Up?: Health-Related Applications of Biotechnology. In T. Bas & J. Zhao (Eds.), Comparative Approaches to Biotechnology Development and Use in Developed and Emerging Nations (pp. 465–520). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1040-6.ch016

226

Recommended Readings

Hartman, A., & Brown, S. (2017). Synergism through Therapeutic Visual Arts. In V. Bryan & J. Bird (Eds.), Healthcare Community Synergism between Patients, Practitioners, and Researchers (pp. 29–48). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0640-9.ch002 Heck, A. J., Cross, C. E., & Tatum, V. Y. (2020). Early Medical Education Readiness Interventions: Enhancing Undergraduate Preparedness. In R. Gotian, Y. Kang, & J. Safdieh (Eds.), Handbook of Research on the Efficacy of Training Programs and Systems in Medical Education (pp. 283–304). IGI Global. https://doi.org/10.4018/9781-7998-1468-9.ch015 Hemant, B., Arasappa, R. G. I., Udupa, K., & Varambally, S. (2021). Yoga for Mental Health Disorders: Research and Practice. In S. Telles & R. Gupta (Eds.), Handbook of Research on Evidence-Based Perspectives on the Psychophysiology of Yoga and Its Applications (pp. 179–198). IGI Global. https://doi.org/10.4018/9781-7998-3254-6.ch011 Herdeiro, M. T., Gouveia, N., & Roque, F. (2021). Clinical Research and Regulatory Affairs: Skills and Tools in Pharmacy Education. In I. Figueiredo & A. Cavaco (Eds.), Pedagogies for Pharmacy Curricula (pp. 160–184). IGI Global. https://doi. org/10.4018/978-1-7998-4486-0.ch008 Hosoda, M., & Hosoda, M. (2019). Supporting Patient-led Initiatives to Improve Healthcare: An Investigation of Cancer and ME/CFS Support Groups and Prefectural Medical Councils in Japan. International Journal of Patient-Centered Healthcare, 9(2), 44–56. https://doi.org/10.4018/IJPCH.20190701.oa2 Iqbal, S., Ahmad, S., & Willis, I. (2017). Influencing Factors for Adopting Technology Enhanced Learning in the Medical Schools of Punjab, Pakistan. International Journal of Information and Communication Technology Education, 13(3), 27–39. doi:10.4018/IJICTE.2017070103 Jagiello, K., Sosnowska, A., Mikolajczyk, A., & Puzyn, T. (2017). Nanomaterials in Medical Devices: Regulations’ Review and Future Perspectives. Journal of Nanotoxicology and Nanomedicine, 2(2), 1–11. doi:10.4018/JNN.2017070101 Jagtap, R., Phulare, K., Kurhade, M., & Gawande, K. S. (2021). Healthcare Conversational Chatbot for Medical Diagnosis. In B. Patil & M. Vohra (Eds.), Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics (pp. 401–415). IGI Global. https://doi.org/10.4018/9781-7998-3053-5.ch020

227

Recommended Readings

Janosek, D. M. (2022). What Is the “Public Good” in a Pandemic? Who Decides?: Policy Makers and the Need for Leadership in Society’s Perception of Medical Information. In H. Aker & M. Aiken (Eds.), Handbook of Research on Cyberchondria, Health Literacy, and the Role of Media in Society’s Perception of Medical Information (pp. 1–15). IGI Global. https://doi.org/10.4018/978-1-7998-8630-3.ch001 Jena, T. K. (2018). Skill Training Process in Medicine Through Distance Mode. In U. Pandey & V. Indrakanti (Eds.), Optimizing Open and Distance Learning in Higher Education Institutions (pp. 228–243). Hershey, PA: IGI Global. doi:10.4018/9781-5225-2624-7.ch010 Joseph, V., & Miller, J. M. (2018). Medical Students’ Perceived Stigma in Seeking Care: A Cultural Perspective. In C. Smith (Ed.), Exploring the Pressures of Medical Education From a Mental Health and Wellness Perspective (pp. 44–67). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2811-1.ch002 Joyce, B. L., & Swanberg, S. M. (2017). Using Backward Design for CompetencyBased Undergraduate Medical Education. In J. Stefaniak (Ed.), Advancing Medical Education Through Strategic Instructional Design (pp. 53–76). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2098-6.ch003 Kalder, M., & Kostev, K. (2021). Epidemiology of Gynaecological and Breast Cancers: Incidence, Survival, and Mental Health. In K. Dinas, S. Petousis, M. Kalder, & G. Mavromatidis (Eds.), Handbook of Research on Oncological and Endoscopical Dilemmas in Modern Gynecological Clinical Practice (pp. 1–21). IGI Global. https://doi.org/10.4018/978-1-7998-4213-2.ch001 Kaljo, K., & Jacques, L. (2018). Flipping the Medical School Classroom. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 5800-5809). Hershey, PA: IGI Global. doi:10.4018/978-1-52252255-3.ch504 Karanfiloglu, M. (2021). The Role of Social Media Use in Health Communication: Digitized Health Communication During COVID-19 Pandemic. In G. Sarı (Eds.), Handbook of Research on Representing Health and Medicine in Modern Media (pp. 84-104). IGI Global. https://doi.org/10.4018/978-1-7998-6825-5.ch006 Karthikeyan, P., Vasuki, S., & Karthik, K. (2019). Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images: Denoising of Medical Images Using Contourlet. In A. Swarnambiga (Ed.), Medical Image Processing for Improved Clinical Diagnosis (pp. 155–183). IGI Global. https://doi.org/10.4018/9781-5225-5876-7.ch008

228

Recommended Readings

Kashyap, R., & Rahamatkar, S. (2019). Medical Image Segmentation: An Advanced Approach. In S. Paul, P. Bhattacharya, & A. Bit (Eds.), Early Detection of Neurological Disorders Using Machine Learning Systems (pp. 292–321). IGI Global. https://doi. org/10.4018/978-1-5225-8567-1.ch015 Kasina, H., Bahubalendruni, M. V., & Botcha, R. (2017). Robots in Medicine: Past, Present and Future. International Journal of Manufacturing, Materials, and Mechanical Engineering, 7(4), 44–64. doi:10.4018/IJMMME.2017100104 Katehakis, D. G. (2018). Electronic Medical Record Implementation Challenges for the National Health System in Greece. International Journal of Reliable and Quality E-Healthcare, 7(1), 16–30. doi:10.4018/IJRQEH.2018010102 Katehakis, D. G., & Kouroubali, A. (2022). Digital Transformation Challenges for the Development of Quality Electronic Medical Records in Greece. In A. Moumtzoglou (Ed.), Quality of Healthcare in the Aftermath of the COVID-19 Pandemic (pp. 112–134). IGI Global. https://doi.org/10.4018/978-1-7998-9198-7.ch007 Kaur, P. D., & Sharma, P. (2017). Success Dimensions of ICTs in Healthcare. In B. Singh (Ed.), Computational Tools and Techniques for Biomedical Signal Processing (pp. 149–173). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0660-7.ch007 Kaushik, P. (2018). Comorbidity of Medical and Psychiatric Disorders in Geriatric Population: Treatment and Care. In B. Prasad & S. Akbar (Eds.), Handbook of Research on Geriatric Health, Treatment, and Care (pp. 448–474). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3480-8.ch025 Khachane, M. Y. (2017). Organ-Based Medical Image Classification Using Support Vector Machine. International Journal of Synthetic Emotions, 8(1), 18–30. doi:10.4018/IJSE.2017010102 Kharbanda, V., Madaan, R., & Bhatia, K. K. (2021). Incautious Usage of Social Media: Impact on Emotional Intelligence and Health Concerns. In D. Yadav, A. Bansal, M. Bhatia, M. Hooda, & J. Morato (Eds.), Diagnostic Applications of Health Intelligence and Surveillance Systems (pp. 172–186). IGI Global. https:// doi.org/10.4018/978-1-7998-6527-8.ch008 Kirci, P. (2018). Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records. In N. Shah & M. Mittal (Eds.), Handbook of Research on Promoting Business Process Improvement Through Inventory Control Techniques (pp. 559–582). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3232-3.ch029

229

Recommended Readings

Kldiashvili, E. (2018). Cloud Approach for the Medical Information System: MIS on Cloud. In M. Khosrow-Pour (Ed.), Incorporating Nature-Inspired Paradigms in Computational Applications (pp. 238–261). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-5020-4.ch008 Ko, H., Mesicek, L., Choi, J., Choi, J., & Hwang, S. (2018). A Study on Secure Contents Strategies for Applications With DRM on Cloud Computing. International Journal of Cloud Applications and Computing, 8(1), 143–153. doi:10.4018/ IJCAC.2018010107 Komendziński, T., Mikołajewska, E., & Mikołajewski, D. (2018). Cross-Cultural Decision-Making in Healthcare: Theory and Practical Application in Real Clinical Conditions. In A. Rosiek-Kryszewska & K. Leksowski (Eds.), Healthcare Administration for Patient Safety and Engagement (pp. 276–298). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3946-9.ch015 Konecny, L. T. (2018). Medical School Wellness Initiatives. In C. Smith (Ed.), Exploring the Pressures of Medical Education From a Mental Health and Wellness Perspective (pp. 209–228). Hershey, PA: IGI Global. doi:10.4018/978-1-52252811-1.ch009 Kromrei, H., Solomonson, W. L., & Juzych, M. S. (2017). Teaching Residents How to Teach. In J. Stefaniak (Ed.), Advancing Medical Education Through Strategic Instructional Design (pp. 164–185). Hershey, PA: IGI Global. doi:10.4018/978-15225-2098-6.ch008 Kulkarni, S., Savyanavar, A., Kulkarni, P., Stranieri, A., & Ghorpade, V. (2018). Framework for Integration of Medical Image and Text-Based Report Retrieval to Support Radiological Diagnosis. In M. Kolekar & V. Kumar (Eds.), Biomedical Signal and Image Processing in Patient Care (pp. 86–122). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2829-6.ch006 Kumar, A., & Sarkar, B. K. (2018). Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets. In U. Singh, A. Tiwari, & R. Singh (Eds.), Soft-Computing-Based Nonlinear Control Systems Design (pp. 134–155). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3531-7.ch007 Labbadi, W., & Akaichi, J. (2017). Efficient Algorithm for Answering Fuzzy Medical Requests in Pervasive Healthcare Information Systems. International Journal of Healthcare Information Systems and Informatics, 12(2), 46–64. doi:10.4018/ IJHISI.2017040103

230

Recommended Readings

Lagumdzija, A., & Swing, V. K. (2017). Health, Digitalization, and Individual Empowerment. In F. Topor (Ed.), Handbook of Research on Individualism and Identity in the Globalized Digital Age (pp. 380–402). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0522-8.ch017 Lamey, T. W., & Davidson-Shivers, G. V. (2017). Instructional Strategies and Sequencing. In J. Stefaniak (Ed.), Advancing Medical Education Through Strategic Instructional Design (pp. 30–52). Hershey, PA: IGI Global. doi:10.4018/978-15225-2098-6.ch002 Lee, D. C., & Gefen, D. (2020). Promises and Challenges of Medical Patient Healthcare Portals in Underserved Communities: The Case of Einstein Medical Center Philadelphia (EMCP). In R. McHaney, I. Reychev, J. Azuri, M. McHaney, & R. Moshonov (Eds.), Impacts of Information Technology on Patient Care and Empowerment (pp. 219–251). IGI Global. https://doi.org/10.4018/978-1-79980047-7.ch012 Leon, G. (2017). The Role of Forensic Medicine in Medical Errors. In M. Riga (Ed.), Impact of Medical Errors and Malpractice on Health Economics, Quality, and Patient Safety (pp. 144–170). Hershey, PA: IGI Global. doi:10.4018/978-15225-2337-6.ch006 Leung, L. Y. (2020). Representation of Gender Equality From the Perspective of the Medical Trainee and its Ripple Effect: Highlighting Gender Inequality in Medical Student Experiences. In M. Bellini & V. Papalois (Eds.), Gender Equity in the Medical Profession (pp. 29–50). IGI Global. https://doi.org/10.4018/978-15225-9599-1.ch003 Lopes, M. J., Fonseca, C., & Barbosa, P. (2020). The Individual Care Plan as Electronic Health Record: A Tool for Management, Integration of Care, and Better Health Results. In D. Mendes, C. Fonseca, M. Lopes, J. García-Alonso, & J. Murillo (Eds.), Exploring the Role of ICTs in Healthy Aging (pp. 1–12). IGI Global. https:// doi.org/10.4018/978-1-7998-1937-0.ch001 Love, L., & McDowelle, D. (2018). Developing a Comprehensive Wellness Program for Medical Students. In C. Smith (Ed.), Exploring the Pressures of Medical Education From a Mental Health and Wellness Perspective (pp. 190–208). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2811-1.ch008

231

Recommended Readings

Lovell, K. L. (2017). Development and Evaluation of Neuroscience Computer-Based Modules for Medical Students: Instructional Design Principles and Effectiveness. In J. Stefaniak (Ed.), Advancing Medical Education Through Strategic Instructional Design (pp. 262–276). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2098-6. ch013 Lubin, R., & Hamlin, M. D. (2018). Medical Student Burnout: A Social Cognitive Learning Perspective on Medical Student Mental Health and Wellness. In C. Smith (Ed.), Exploring the Pressures of Medical Education From a Mental Health and Wellness Perspective (pp. 92–121). Hershey, PA: IGI Global. doi:10.4018/978-15225-2811-1.ch004 Luk, C. Y. (2018). Moving Towards Universal Health Coverage: Challenges for the Present and Future in China. In B. Fong, A. Ng, & P. Yuen (Eds.), Sustainable Health and Long-Term Care Solutions for an Aging Population (pp. 19–45). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2633-9.ch002 Mackintosh, S. E., & Katsaros, E. (2020). Building Interprofessional Competencies Into Medical Education and Assessment. In S. Waldman & S. Bowlin (Eds.), Building a Patient-Centered Interprofessional Education Program (pp. 84–112). IGI Global. https://doi.org/10.4018/978-1-7998-3066-5.ch005 Magalhães, J. L., Quoniam, L., Hartz, Z., Silveira, H., & Rito, P. D. (2022). Knowledge Management in Big Data Times for Global Health: Challenges for Quality in One Health. In J. Lima de Magalhães, Z. Hartz, G. Jamil, H. Silveira, & L. Jamil (Eds.), Handbook of Research on Essential Information Approaches to Aiding Global Health in the One Health Context (pp. 149–163). IGI Global. https://doi.org/10.4018/9781-7998-8011-0.ch008 Mahat, M., & Pettigrew, A. (2017). The Regulatory Environment of Non-Profit Higher Education and Research Institutions and Its Implications for Managerial Strategy: The Case of Medical Education and Research. In L. West & A. Worthington (Eds.), Handbook of Research on Emerging Business Models and Managerial Strategies in the Nonprofit Sector (pp. 336–351). Hershey, PA: IGI Global. doi:10.4018/9781-5225-2537-0.ch017 Maitra, I. K., & Bandhyopadhyaay, S. K. (2017). Adaptive Edge Detection Method towards Features Extraction from Diverse Medical Imaging Technologies. In S. Bhattacharyya, S. De, I. Pan, & P. Dutta (Eds.), Intelligent Multidimensional Data Clustering and Analysis (pp. 159–192). Hershey, PA: IGI Global. doi:10.4018/9781-5225-1776-4.ch007

232

Recommended Readings

Mane, V. M., Patki, S., Dhumma, A. V., & Apte, K. J. (2022). Telehealth System for Effective Treatment in COVID-19 Pandemics. In A. Thakare, S. Wagh, M. Bhende, A. Anter, & X. Gao (Eds.), Handbook of Research on Applied Intelligence for Health and Clinical Informatics (pp. 328–339). IGI Global. https://doi.org/10.4018/9781-7998-7709-7.ch019 Mangu, V. P. (2017). Mobile Health Care: A Technology View. In C. Bhatt & S. Peddoju (Eds.), Cloud Computing Systems and Applications in Healthcare (pp. 1–18). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1002-4.ch001 Manirabona, A., Fourati, L. C., & Boudjit, S. (2017). Investigation on Healthcare Monitoring Systems: Innovative Services and Applications. International Journal of E-Health and Medical Communications, 8(1), 1–18. doi:10.4018/ IJEHMC.2017010101 Martins, C. L., Martinho, D., Marreiros, G., Conceição, L., Faria, L., & Simões de Almeida, R. (2022). Artificial Intelligence in Digital Mental Health. In A. Marques & R. Queirós (Eds.), Digital Therapies in Psychosocial Rehabilitation and Mental Health (pp. 201–225). IGI Global. https://doi.org/10.4018/978-1-7998-8634-1.ch010 Marwan, M., Kartit, A., & Ouahmane, H. (2018). A Framework to Secure Medical Image Storage in Cloud Computing Environment. Journal of Electronic Commerce in Organizations, 16(1), 1–16. doi:10.4018/JECO.2018010101 Mason, A., Bhati, S., Jiang, R., & Spencer, E. A. (2020). Medical Tourism Patient Mortality: Considerations From a 10-Year Review of Global News Media Representations. In M. Merviö (Ed.), Global Issues and Innovative Solutions in Healthcare, Culture, and the Environment (pp. 206–225). IGI Global. https://doi. org/10.4018/978-1-7998-3576-9.ch011 Masoud, M. P., Nejad, M. K., Darebaghi, H., Chavoshi, M., & Farahani, M. (2018). The Decision Support System and Conventional Method of Telephone Triage by Nurses in Emergency Medical Services: A Comparative Investigation. International Journal of E-Business Research, 14(1), 77–88. doi:10.4018/IJEBR.2018010105 Mathew, N. E., Walter, A., & Eapen, V. (2020). Mental Health Challenges in Children With Intellectual Disabilities. In R. Gopalan (Ed.), Developmental Challenges and Societal Issues for Individuals With Intellectual Disabilities (pp. 13–39). IGI Global. https://doi.org/10.4018/978-1-7998-1223-4.ch002

233

Recommended Readings

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234

Recommended Readings

Meyer, T. M., & Simen, J. H. (2020). Geriatric Interprofessional Education: A Practical Guide for Health Professions Educators. In S. Waldman & S. Bowlin (Eds.), Building a Patient-Centered Interprofessional Education Program (pp. 120–136). IGI Global. https://doi.org/10.4018/978-1-7998-3066-5.ch007 Mi, M. (2017). Informal Learning in Medical Education. In J. Stefaniak (Ed.), Advancing Medical Education Through Strategic Instructional Design (pp. 225–244). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2098-6.ch011 Mishra, K., & Thomas, R. (2022). Low Cost, User-Controlled Peroneal Stimulator for Foot Drop in Patients With Stroke: An Experiment in Indian Rehabilitation Set-Up. In F. Stasolla (Ed.), Assistive Technologies for Assessment and Recovery of Neurological Impairments (pp. 279–303). IGI Global. https://doi.org/10.4018/9781-7998-7430-0.ch014 Mishra, S., & Panda, M. (2019). Artificial Intelligence in Medical Science. In N. Bouchemal (Ed.), Intelligent Systems for Healthcare Management and Delivery (pp. 306–330). IGI Global. https://doi.org/10.4018/978-1-5225-7071-4.ch014 Mosadeghrad, A. M., & Woldemichael, A. (2017). Application of Quality Management in Promoting Patient Safety and Preventing Medical Errors. In M. Riga (Ed.), Impact of Medical Errors and Malpractice on Health Economics, Quality, and Patient Safety (pp. 91–112). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2337-6.ch004 Moumtzoglou, A. (2017). Digital Medicine: The Quality Standpoint. In A. Moumtzoglou (Ed.), Design, Development, and Integration of Reliable Electronic Healthcare Platforms (pp. 179–195). Hershey, PA: IGI Global. doi:10.4018/9781-5225-1724-5.ch011 Moumtzoglou, A., & Pouliakis, A. (2018). Population Health Management and the Science of Individuality. International Journal of Reliable and Quality E-Healthcare, 7(2), 1–26. doi:10.4018/IJRQEH.2018040101 Mukhtar, W. F., & Abuelyaman, E. S. (2017). Opportunities and Challenges of Big Data in Healthcare. In N. Wickramasinghe (Ed.), Handbook of Research on Healthcare Administration and Management (pp. 47–58). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0920-2.ch004 Munugala, S., Brar, G. K., Syed, A., Mohammad, A., & Halgamuge, M. N. (2018). The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage. In M. Lytras & P. Papadopoulou (Eds.), Applying Big Data Analytics in Bioinformatics and Medicine (pp. 99–113). Hershey, PA: IGI Global. doi:10.4018/9781-5225-2607-0.ch005

235

Recommended Readings

Naha, N., & Manickavachagam, G. (2021). Textile Industry and Health Hazards: Impact of Climate Change Issues and Fertility Potential. In K. Wani, & N. Naha (Ed.), Climate Change and Its Impact on Fertility (pp. 42-69). IGI Global. https:// doi.org/10.4018/978-1-7998-4480-8.ch003 Nair, L. D. (2019). Childhood Neurodegenerative Disorders. In M. Uddin & M. Amran (Eds.), Handbook of Research on Critical Examinations of Neurodegenerative Disorders (pp. 385–412). IGI Global. https://doi.org/10.4018/978-1-5225-5282-6. ch018 Nayak, S. R., & Mishra, J. (2019). Analysis of Medical Images Using Fractal Geometry. In N. Dey, A. Ashour, H. Kalia, R. Goswami, & H. Das (Eds.), Histopathological Image Analysis in Medical Decision Making (pp. 181–201). IGI Global. https://doi. org/10.4018/978-1-5225-6316-7.ch008 Netto, J. T., Hartz, Z., & de Magalhães, J. L. (2022). One Health and Information Management Using Big Data in Health: A Brazilian Case Study for COVID-19. In J. Lima de Magalhães, Z. Hartz, G. Jamil, H. Silveira, & L. Jamil (Eds.), Handbook of Research on Essential Information Approaches to Aiding Global Health in the One Health Context (pp. 36–51). IGI Global. https://doi.org/10.4018/978-1-79988011-0.ch003 Ngara, R. (2017). Multiple Voices, Multiple Paths: Towards Dialogue between Western and Indigenous Medical Knowledge Systems. In P. Ngulube (Ed.), Handbook of Research on Theoretical Perspectives on Indigenous Knowledge Systems in Developing Countries (pp. 332–358). Hershey, PA: IGI Global. doi:10.4018/9781-5225-0833-5.ch015 Nikumbh, D. D., Sayyad, S., Joshi, R. R., Dubey, K. S., Mehta, D. V., & Matta, D. K. (2022). Applied Intelligence for Medical Diagnosing. In A. Thakare, S. Wagh, M. Bhende, A. Anter, & X. Gao (Eds.), Handbook of Research on Applied Intelligence for Health and Clinical Informatics (pp. 44–79). IGI Global. https:// doi.org/10.4018/978-1-7998-7709-7.ch004 O’Connor, Y., & Heavin, C. (2018). Defining and Characterising the Landscape of eHealth. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 5864-5875). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2255-3.ch510 Ochonogor, W. C., & Okite-Amughoro, F. A. (2018). Building an Effective Digital Library in a University Teaching Hospital (UTH) in Nigeria. In A. Tella & T. Kwanya (Eds.), Handbook of Research on Managing Intellectual Property in Digital Libraries (pp. 184–204). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3093-0.ch010

236

Recommended Readings

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237

Recommended Readings

Poduval, J. (2017). Medical Errors: Impact on Health Care Quality. In M. Riga (Ed.), Impact of Medical Errors and Malpractice on Health Economics, Quality, and Patient Safety (pp. 33–60). Hershey, PA: IGI Global. doi:10.4018/978-1-52252337-6.ch002 Politis, D., Stagiopoulos, P., Aidona, S., Kyriafinis, G., & Constantinidis, I. (2018). Autonomous Learning and Skill Accreditation: A Paradigm for Medical Studies. In A. Kumar (Ed.), Optimizing Student Engagement in Online Learning Environments (pp. 266–296). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3634-5.ch012 Pomares-Quimbaya, A., González, R. A., Sierra, A., Daza, J. C., Muñoz, O., García, A., ... Bohórquez, W. R. (2017). ICT for Enabling the Quality Evaluation of Health Care Services: A Case Study in a General Hospital. In A. Moumtzoglou (Ed.), Design, Development, and Integration of Reliable Electronic Healthcare Platforms (pp. 196–210). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1724-5.ch012 Ponnuswamy, P., Paul, A. S., & Jose, A. B. (2021). Pre-, Peri-, and Post-Natal Risk Factors in ADHD. In R. Gopalan (Ed.), New Developments in Diagnosing, Assessing, and Treating ADHD (pp. 43–54). IGI Global. https://doi.org/10.4018/978-1-79985495-1.ch003 Pouliakis, A., Margari, N., Karakitsou, E., Archondakis, S., & Karakitsos, P. (2018). Emerging Technologies Serving Cytopathology: Big Data, the Cloud, and Mobile Computing. In I. El Naqa (Ed.), Emerging Developments and Practices in Oncology (pp. 114–152). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3085-5.ch005 Praveenkumar, P., Santhiyadevi, R., & Amirtharajan, R. (2019). Medical Data Are Safe: An Encrypted Quantum Approach. In B. Singh, B. Saini, D. Singh, & A. Pandey (Eds.), Medical Data Security for Bioengineers (pp. 142–165). IGI Global. https://doi.org/10.4018/978-1-5225-7952-6.ch008 Punhani, R., Saini, S., Varun, N., & Rustagi, R. (2021). mHealth: A Resolution in Improving Global Health. In D. Yadav, A. Bansal, M. Bhatia, M. Hooda, & J. Morato (Ed.), Diagnostic Applications of Health Intelligence and Surveillance Systems (pp. 86-105). IGI Global. https://doi.org/10.4018/978-1-7998-6527-8.ch004 Quintela, A., & Veiga, A. (2022). An Integrated Approach (Social and Health) in the Elderly With Diabetes: An Increase in Health Literacy. In C. Vaz de Almeida & S. Ramos (Eds.), Handbook of Research on Assertiveness, Clarity, and Positivity in Health Literacy (pp. 134–142). IGI Global. https://doi.org/10.4018/978-1-79988824-6.ch008

238

Recommended Readings

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239

Recommended Readings

Ros, M., Weaver, L., & Neuwirth, L. S. (2020). Virtual Reality Stereoscopic 180-Degree Video-Based Immersive Environments: Applications for Training Surgeons and Other Medical Professionals. In J. Stefaniak (Ed.), Cases on Instructional Design and Performance Outcomes in Medical Education (pp. 92–119). IGI Global. https://doi.org/10.4018/978-1-7998-5092-2.ch005 Rosiek, A. (2018). The Assessment of Actions of the Environment and the Impact of Preventive Medicine for Public Health in Poland. In A. Rosiek-Kryszewska & K. Leksowski (Eds.), Healthcare Administration for Patient Safety and Engagement (pp. 106–119). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3946-9.ch006 Rosiek, A., & Rosiek-Kryszewska, A. (2018). Managed Healthcare: Doctor Life Satisfaction and Its Impact on the Process of Communicating With the Patient. In A. Rosiek-Kryszewska & K. Leksowski (Eds.), Healthcare Administration for Patient Safety and Engagement (pp. 244–261). Hershey, PA: IGI Global. doi:10.4018/9781-5225-3946-9.ch013 Rosiek-Kryszewska, A., & Rosiek, A. (2018). The Involvement of the Patient and his Perspective Evaluation of the Quality of Healthcare. In A. Rosiek-Kryszewska & K. Leksowski (Eds.), Healthcare Administration for Patient Safety and Engagement (pp. 121–144). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3946-9.ch007 Rosiek-Kryszewska, A., & Rosiek, A. (2018). The Impact of Management and Leadership Roles in Building Competitive Healthcare Units. In A. Rosiek-Kryszewska & K. Leksowski (Eds.), Healthcare Administration for Patient Safety and Engagement (pp. 13–30). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3946-9.ch002 Rouzbehani, K. (2017). Health Policy Implementation: Moving Beyond Its Barriers in United States. In N. Wickramasinghe (Ed.), Handbook of Research on Healthcare Administration and Management (pp. 541–552). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0920-2.ch032 Sajjad, N., Hassan, S., Qadir, J., Ali, R., & Shah, D. (2020). Analyses of the Recycling Potential of Medical Plastic Wastes. In K. Wani, L. Ariana, & S. Zuber (Eds.), Handbook of Research on Environmental and Human Health Impacts of Plastic Pollution (pp. 178–199). IGI Global. https://doi.org/10.4018/978-1-52259452-9.ch010 Sam, S. (2017). Mobile Phones and Expanding Human Capabilities in Plural Health Systems. In K. Moahi, K. Bwalya, & P. Sebina (Eds.), Health Information Systems and the Advancement of Medical Practice in Developing Countries (pp. 93–114). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2262-1.ch006

240

Recommended Readings

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241

Recommended Readings

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Recommended Readings

Singh, A., & Dutta, M. K. (2017). A Reversible Data Hiding Scheme for Efficient Management of Tele-Ophthalmological Data. International Journal of E-Health and Medical Communications, 8(3), 38–54. doi:10.4018/IJEHMC.2017070103 Skourti, P. K., & Pavlakis, A. (2017). The Second Victim Phenomenon: The Way Out. In M. Riga (Ed.), Impact of Medical Errors and Malpractice on Health Economics, Quality, and Patient Safety (pp. 197–222). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2337-6.ch008 Smith, C. R. (2018). Medical Students’ Quest Towards the Long White Coat: Impact on Mental Health and Well-Being. In C. Smith (Ed.), Exploring the Pressures of Medical Education From a Mental Health and Wellness Perspective (pp. 1–42). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2811-1.ch001 Smith, S. I., & Dandignac, M. (2018). Perfectionism: Addressing Lofty Expectations in Medical School. In C. Smith (Ed.), Exploring the Pressures of Medical Education From a Mental Health and Wellness Perspective (pp. 68–91). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2811-1.ch003 Soczywko, J., & Rutkowska, D. (2018). The Patient/Provider Relationship in Emergency Medicine: Organization, Communication, and Understanding. In A. Rosiek-Kryszewska & K. Leksowski (Eds.), Healthcare Administration for Patient Safety and Engagement (pp. 74–105). Hershey, PA: IGI Global. doi:10.4018/9781-5225-3946-9.ch005 Soni, P. (2018). Implications of HIPAA and Subsequent Regulations on Information Technology. In M. Gupta, R. Sharman, J. Walp, & P. Mulgund (Eds.), Information Technology Risk Management and Compliance in Modern Organizations (pp. 71–98). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2604-9.ch004 Souza, T. de V. I. E. (2020). Certifications for Medical Interpreters: A Comparative Analysis. In I. Souza, & E. Fragkou (Eds.),  Handbook of Research on Medical Interpreting (pp. 26-53). IGI Global. https://doi.org/10.4018/978-1-5225-9308-9. ch002 Srivastava, A., & Aggarwal, A. K. (2018). Medical Image Fusion in Spatial and Transform Domain: A Comparative Analysis. In M. Anwar, A. Khosla, & R. Kapoor (Eds.), Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing (pp. 281–300). Hershey, PA: IGI Global. doi:10.4018/9781-5225-2848-7.ch011

243

Recommended Readings

Srivastava, S. K., & Roy, S. N. (2018). Recommendation System: A Potential Tool for Achieving Pervasive Health Care. In J. Machado, A. Abelha, M. Santos, & F. Portela (Eds.), Next-Generation Mobile and Pervasive Healthcare Solutions (pp. 111–127). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2851-7.ch008 Stanimirovic, D. (2017). Digitalization of Death Certification Model: Transformation Issues and Implementation Concerns. In S. Saeed, Y. Bamarouf, T. Ramayah, & S. Iqbal (Eds.), Design Solutions for User-Centric Information Systems (pp. 22–43). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1944-7.ch002 Subbaraman, K., Singh, M., & Johar, I. P. (2021). Medical Tourism: History, Global Scenario, and Indian Perspectives. In M. Singh & S. Kumaran (Eds.), Growth of the Medical Tourism Industry and Its Impact on Society: Emerging Research and Opportunities (pp. 1–18). IGI Global. https://doi.org/10.4018/978-1-7998-3427-4. ch001 Sukkird, V., & Shirahada, K. (2018). E-Health Service Model for Asian Developing Countries: A Case of Emergency Medical Service for Elderly People in Thailand. In M. Khosrow-Pour (Ed.), Optimizing Current Practices in E-Services and Mobile Applications (pp. 214–232). Hershey, PA: IGI Global. doi:10.4018/978-1-52255026-6.ch011 Talbot, T. B. (2017). Making Lifelike Medical Games in the Age of Virtual Reality: An Update on “Playing Games with Biology” from 2013. In B. Dubbels (Ed.), Transforming Gaming and Computer Simulation Technologies across Industries (pp. 103–119). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1817-4.ch006 Tarver, E. (2022). Virtual Simulation: A Flipped Classroom Teaching Tool for Healthcare Education. In L. Coelho, R. Queirós, & S. Reis (Eds.), Emerging Advancements for Virtual and Augmented Reality in Healthcare (pp. 65–81). IGI Global. https://doi.org/10.4018/978-1-7998-8371-5.ch005 Tripathi, S., & Musiolik, T. H. (2022). Fairness and Ethics in Artificial IntelligenceBased Medical Imaging. In T. Musiolik & A. Dingli (Eds.), Ethical Implications of Reshaping Healthcare With Emerging Technologies (pp. 71–85). IGI Global. https:// doi.org/10.4018/978-1-7998-7888-9.ch004 Turcu, C. E., & Turcu, C. O. (2017). Social Internet of Things in Healthcare: From Things to Social Things in Internet of Things. In C. Reis & M. Maximiano (Eds.), Internet of Things and Advanced Application in Healthcare (pp. 266–295). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1820-4.ch010

244

Recommended Readings

Tutgun Ünal, A., Ekinci, Y., & Tarhan, N. (2022). Health Literacy and Cyberchondria. In H. Aker & M. Aiken (Eds.), Handbook of Research on Cyberchondria, Health Literacy, and the Role of Media in Society’s Perception of Medical Information (pp. 276–297). IGI Global. https://doi.org/10.4018/978-1-7998-8630-3.ch015 Unwin, D. W., Sanzogni, L., & Sandhu, K. (2017). Developing and Measuring the Business Case for Health Information Technology. In K. Moahi, K. Bwalya, & P. Sebina (Eds.), Health Information Systems and the Advancement of Medical Practice in Developing Countries (pp. 262–290). Hershey, PA: IGI Global. doi:10.4018/9781-5225-2262-1.ch015 Ustun, A. B., Yilmaz, R., & Yilmaz, F. G. (2020). Virtual Reality in Medical Education. In S. Umair (Ed.), Mobile Devices and Smart Gadgets in Medical Sciences (pp. 56–73). IGI Global. https://doi.org/10.4018/978-1-7998-2521-0.ch004 Vamsi, D., & Reddy, P. (2020). Electronic Health Record Security in Cloud: Medical Data Protection Using Homomorphic Encryption Schemes. In C. Chakraborty (Ed.), Smart Medical Data Sensing and IoT Systems Design in Healthcare (pp. 22–47). IGI Global. https://doi.org/10.4018/978-1-7998-0261-7.ch002 Vartholomaios, P., Ramdani, N., Christophorou, C., Georgiadis, D., Guilcher, T., Blouin, M., Rebiai, M., Panayides, A. S., Pattichis, C. S., Sarafidis, M., Costarides, V., Vellidou, E., & Koutsouris, D. (2019). ENDORSE Concept: An Integrated Indoor Mobile Robotic System for Medical Diagnostic Support. International Journal of Reliable and Quality E-Healthcare, 8(3), 47–59. https://doi.org/10.4018/ IJRQEH.2019070104 Vasant, P. (2018). A General Medical Diagnosis System Formed by Artificial Neural Networks and Swarm Intelligence Techniques. In U. Kose, G. Guraksin, & O. Deperlioglu (Eds.), Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems (pp. 130–145). Hershey, PA: IGI Global. doi:10.4018/9781-5225-4769-3.ch006 Vaz de Almeida, C. (2022). ACP Model-Assertiveness, Clarity, and Positivity: A Communication and Health Literacy Model for Health Professionals. In C. Vaz de Almeida & S. Ramos (Eds.), Handbook of Research on Assertiveness, Clarity, and Positivity in Health Literacy (pp. 1–22). IGI Global. https://doi.org/10.4018/9781-7998-8824-6.ch001 Verma, K. (2022). Modeling Digital Healthcare Services Using NLP and IoT in Smart Cities. In J. Thomas, V. Geropanta, A. Karagianni, V. Panchenko, & P. Vasant (Eds.), Smart Cities and Machine Learning in Urban Health (pp. 138–155). IGI Global. https://doi.org/10.4018/978-1-7998-7176-7.ch007

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Recommended Readings

Viswanathan, J., Saranya, N., & Inbamani, A. (2021). Deep Learning Applications in Medical Imaging: Introduction to Deep Learning-Based Intelligent Systems for Medical Applications. In S. Saxena & S. Paul (Eds.), Deep Learning Applications in Medical Imaging (pp. 156–177). IGI Global. https://doi.org/10.4018/978-1-79985071-7.ch007 Wang, X., Su, K., & Su, L. (2019). Research on Improved Apriori Algorithm Based on Data Mining in Electronic Cases. International Journal of Healthcare Information Systems and Informatics, 14(3), 16–28. https://doi.org/10.4018/IJHISI.2019070102 Wickramasinghe, N. (2020). Minority Health and Wellness: A Digital Health Opportunity. In N. Wickramasinghe (Ed.), Handbook of Research on Optimizing Healthcare Management Techniques (pp. 116–126). IGI Global. https://doi. org/10.4018/978-1-7998-1371-2.ch008 Wickramasinghe, N., Geholt, V., Sloane, E., Smart, P. J., & Schaffer, J. L. (2021). Using Health 4.0 to Enable Post-Operative Wellness Monitoring: The Case of Colorectal Surgery. In N. Wickramasinghe (Ed.), Optimizing Health Monitoring Systems With Wireless Technology (pp. 233–247). IGI Global. https://doi.org/10.4018/978-15225-6067-8.ch016 Wilcox, S., Huzo, O., Minhas, A., Walters, N., Adada, J. E., Pennington, M., Roseme, L., Mohammed, D., Dusic, A., & Zeine, R. (2022). The Impact of Medical or HealthRelated Internet Searches on Patient Compliance: The Dr. Net Study. In H. Aker & M. Aiken (Eds.), Handbook of Research on Cyberchondria, Health Literacy, and the Role of Media in Society’s Perception of Medical Information (pp. 72–97). IGI Global. https://doi.org/10.4018/978-1-7998-8630-3.ch005 Witzke, K., & Specht, O. (2017). M-Health Telemedicine and Telepresence in Oral and Maxillofacial Surgery: An Innovative Prehospital Healthcare Concept in Structurally Weak Areas. International Journal of Reliable and Quality E-Healthcare, 6(4), 37–48. doi:10.4018/IJRQEH.2017100105 Wong, A. K., & Lo, M. F. (2018). Using Pervasive Computing for Sustainable Healthcare in an Aging Population. In B. Fong, A. Ng, & P. Yuen (Eds.), Sustainable Health and Long-Term Care Solutions for an Aging Population (pp. 187–202). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2633-9.ch010 Wu, W., Martin, B. C., & Ni, C. (2017). A Systematic Review of CompetencyBased Education Effort in the Health Professions: Seeking Order Out of Chaos. In K. Rasmussen, P. Northrup, & R. Colson (Eds.), Handbook of Research on Competency-Based Education in University Settings (pp. 352–378). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0932-5.ch018

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Recommended Readings

Yadav, S., Ekbal, A., Saha, S., Pathak, P. S., & Bhattacharyya, P. (2017). Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach. In S. Saha, A. Mandal, A. Narasimhamurthy, S. V, & S. Sangam (Eds.), Handbook of Research on Applied Cybernetics and Systems Science (pp. 234-253). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2498-4.ch011 Yu, B., Wijesekera, D., & Costa, P. C. (2017). Informed Consent in Healthcare: A Study Case of Genetic Services. In A. Moumtzoglou (Ed.), Design, Development, and Integration of Reliable Electronic Healthcare Platforms (pp. 211–242). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1724-5.ch013 Yu, B., Wijesekera, D., & Costa, P. C. (2017). Informed Consent in Electronic Medical Record Systems. In I. Management Association (Ed.), Healthcare Ethics and Training: Concepts, Methodologies, Tools, and Applications (pp. 1029-1049). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2237-9.ch049 Yu, M., Li, J., & Wang, W. (2017). Creative Life Experience among Students in Medical Education. In C. Zhou (Ed.), Handbook of Research on Creative ProblemSolving Skill Development in Higher Education (pp. 158–184). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-0643-0.ch008 Zanetti, C. A., George, A., Stiegmann, R. A., & Phelan, D. (2020). Digital Health. In R. Gotian, Y. Kang, & J. Safdieh (Eds.), Handbook of Research on the Efficacy of Training Programs and Systems in Medical Education (pp. 404–426). IGI Global. https://doi.org/10.4018/978-1-7998-1468-9.ch021 Zangão, M. O. (2020). Self-Perceived Health Status. In C. Fonseca, M. Lopes, D. Mendes, F. Mendes, & J. García-Alonso (Eds.), Handbook of Research on Health Systems and Organizations for an Aging Society (pp. 1–11). IGI Global. doi:10.4018/978-1-5225-9818-3.ch001 Zarour, K. (2017). Towards a Telehomecare in Algeria: Case of Diabetes Measurement and Remote Monitoring. International Journal of E-Health and Medical Communications, 8(4), 61–80. doi:10.4018/IJEHMC.2017100104 Zavyalova, Y. V., Korzun, D. G., Meigal, A. Y., & Borodin, A. V. (2017). Towards the Development of Smart Spaces-Based Socio-Cyber-Medicine Systems. International Journal of Embedded and Real-Time Communication Systems, 8(1), 45–63. doi:10.4018/IJERTCS.2017010104 Zeinali, A. A. (2018). Word Formation Study in Developing Naming Guidelines in the Translation of English Medical Terms Into Persian. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 5136-5147). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2255-3.ch446 247

Recommended Readings

Zineldin, M., & Vasicheva, V. (2018). Reducing Medical Errors and Increasing Patient Safety: TRM and 5 Q’s Approaches for Better Quality of Life. In Technological Tools for Value-Based Sustainable Relationships in Health: Emerging Research and Opportunities (pp. 87–115). Hershey, PA: IGI Global. doi:10.4018/978-15225-4091-5.ch005

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About the Contributors

Parijat Bhowmick is presently an Assistant Professor at the Department of Electronics and Electrical Engineering of IIT Guwahati. Prior to joining this position, he worked as a Post-doctoral Research Associate for three years at the Control Systems Centre, University of Manchester, UK. He completed his PhD degree in Control Engineering from IIT Kharagpur in the year 2018. He did his Master’s from Jadavpur University in 2012 with the same specialisation. He was a recipient of the University Gold Medal in ME and was also a recipient of the Institute Silver Medal during his B. Tech. He is an active researcher in the horizon of Robust control of uncertain systems, Negative Imaginary Systems theory, Vibration control of mechatronic systems, Cooperative Control of Multi-agent Systems (including Multi-Robot Systems) and Control of Smart/Micro-grid Systems. He has published around forty papers so far in Tier-I international journals and flagship conferences. He has also guided five PhD students till date in the domain of Control and Robotics. *** Moumita Chatterjee is currently associated with Aliah University, Kolkata as an Assistant Professor in the Department of Computer Science & Engineering. Pritam Chatterjee is currently working in Infosys Limited. Siddhartha Chatterjee completed his B.Tech & M.Tech degree from West Bengal University of Technology. His total work experience is 13 years. He has published 26 research paper in various national and international conferences and journals. His area of research work is wireless communication, graph theory & optimization techniques. He is the reviewer of many national & international conferences. Currently he is the Assistant Professor of NSHM Knowledge Campus, Durgapur in the department of Computer Science and Engineering.

About the Contributors

Ahona Ghosh is a B.Tech., M.Tech. in Computer Science and Engineering and presently is an AICTE Doctoral Fellow in the Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, West Bengal (In house). She has qualified NTA NET twice. Before joining her Ph.D., she was an Assistant Professor in the Department of Computational Science at Brainware University, Barasat, Kolkata. She has one patent, one authored book and published more than 40 papers in international conference proceedings, peer-reviewed international journals, and books with IEEE, Springer, Elsevier, IGI Global, CRC Press, etc. She is also a reviewer of different international journals and associated with different international conferences as National/International Advisory board member, technical committee member and reviewer. She is an active member and holds professional membership in different national and international organizations related to development of Engineering & Technological Research. Her research interests include Machine Learning, Human Computer Interaction, and the Internet of Things. Anup Kumar Ghosh completed his B.Tech & M.Tech degree from West Bengal University of Technology in the year of 2007 & 2014. His total work experience is 14 years. He has published multiple research paper in various national and international conferences and journals. His area of research work is Machine Learning & Deep Learning. He act as the reviewer of many conferences. Currently he is the Assistant Professor of NSHM Knowledge Campus, Durgapur in the department of Computer Science and Engineering. Rajesh Ghosh received the Master in Computer Application (MCA) from Silicon Institute of Technology, Bhubaneswar, India in 2009, the M.tech from C V Raman College of Engineering, Bhubaneswar, in 2013, and Currently Pursuing the Ph.D. degree at the CSE department, Institute of Technical Education and Research, Siksha O Anusandhan (SOA), Deemed to be University Bhubaneswar, worked as an Assistant Professor with Maryland Institute of Technology and Management, Jamshedpur. Research interests include machine learning, deep learning and stock price prediction. Nimay Chandra Giri () () received B. Tech. in Electronics and Communication Engineering (ECE) at BPUT, Odisha, India, in 2010 and M. Tech. in Communication Systems Engineering from CUTM, Odisha, India in 2014. He has pursuing He is pursuing a Ph.D. in Agrivoltaic system (AVS). He is currently serving as Assistant Professor, Dept. of ECE and Center for Renewable Energy & Environment at CUTM, Odisha, India. He has 11.5 years of teaching, training, and skill experience. His research and skill areas include solar PV systems, energy conversion, livelihood projects, protected cultivation, and AVS. 250

About the Contributors

Anudeepa Gon is an Assistant Professor of Computational Science Department of Brainware University, Kolkata, West Bengal, India. She has 1.8 years of teaching experience. Ms. Gon received M.Tech degree in Computer Science and Engineering in 2022 from Maulana Abul Kalam Azad University of Technology, West Bengal (Formerly known as WBUT) and awarded 1st class 3rd position, MCA from SMIT with 1st class and currently pursuing P.hD from National InstituteofTechnology Durgapur. She has published more than 7 peer reviewed journals, International Conferences, and received an Best paper Award from JIS Group.She has also authored 3 book chapters. She received PGET in 2020 Her main areas of research interests are Machine Learning, Cyber Security, Big Data, Network Analysis, Data Science. Sudipta Hazra is an Assistant Professor of Department of Computer Science and Engineering of NSHM Knowledge Campus, Durgapur, West Bengal, India. Mr. Hazra completed his B.E. degree from University Institute of Technology, Burdwan, West Bengal, India in the year of 2007 and completed his M. Tech. degree from National Institute of Technology, Durgapur, West Bengal, India in the year of 2012. He has published several research paper in various national and international conferences and journals. His area of research work is Cyber Security, Big Data, Machine Learning etc. He act as the reviewer of many national & international conferences. He has 2 years of IT industry experience and more than 14 years of teaching experience. Subrata Modak is currently working as an Assistant Professor of Maryland Institute of Technology and Management, Galudih, Jamshedpur, India in the department of Computer Science and Engineering. He has completed his MTech in CSE from Maulana Abul Kalam Azad University of Technology, West Bengal, India. His research interest includes AI, ML, Cryptography and Network Security, etc. Chandrasekaran R. completed his B.E. Biomedical Engineering in Jerusalem College of Engineering, M.E. Biomedical Engineering in GKMCET, currently he is pursuing his Ph.D. in Vels University in the field of Biomedical Signal Processing. Sriparna Saha received her M.E. and Ph.D. degrees from Electronics and TeleCommunication Engineering department of Jadavpur University, Kolkata, India. She is currently an Assistant Professor in the Department of Computer Science and Engineering of Maulana Abul Kalam Azad University of Technology, West Bengal, India. Prior to that, she was associated as a faculty with Jadavpur University and two other institutions. She has more than 8 years of experience in teaching and research. Her research area includes artificial intelligence, image processing, machine learning and recently she is giving more focus on deep learning. She has over 70 251

About the Contributors

publications in IEEE, Elsevier, Springer, etc. She is the author of a book on Gesture Recognition published by Studies in Computational Intelligence, Springer. She is also the reviewer for many international journals. Her major research proposal is accepted for Start Up Grant under UGC Basic Scientific Research Grant. Not only that, she also got University seed money for setting up her laboratory. Dhrubasish Sarkar is currently working with Supreme Knowledge Foundation, Mankundu, Hooghly, West Bengal as a Professor of Computer Science and Information Technology at Supreme Institute of Management & Technology. He has more than 26 years of experience in teaching, corporate training, academic administration, and institutional development. He holds PGCACS, MCA, M.Tech (IT) and PhD (CSE). He has more than 40 publications including reputed international journals, conference proceedings and book chapters to his credit. Currently he is serving as editorial review board member for few reputed international journals. His research interest includes social network analysis, recommendation system and data mining. He is a professional member of ACM.

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Index

A

D

Accuracy 21, 46, 52, 55-56, 71, 73, 79-82, 84-87, 91, 93-94, 98-100, 102, 106107, 123, 146, 151, 153-155, 157, 159-161, 163, 166-167, 169-170, 179, 184-186, 191 ADTree 78, 81, 87 AI Tools 1-4, 189-191 Ant Colony Optimization 151, 157, 163 Anxiety 44-45, 47, 56, 65, 77-82, 84, 86, 89, 113, 115-116, 124, 128 Artificial Neural Network 151, 154 Atheromatous Plaque 125

Decision Tree 28, 79, 84, 101, 158, 167169, 180, 182 Depression 29, 44-52, 56-66, 73, 75, 77-87, 89-90, 94, 103-105, 107-113, 115-116, 128, 138

E Electroencephalogram 13, 17, 37, 41, 74 Electroencephalography 14, 37, 40, 67-68, 70, 72-73 Emotion Detection 11, 67, 73, 76 Emotional behavior 67-71, 74-75

B F Brain-Computer Interfacing 14

C Cardiac 1-4, 113-123, 126-134, 136, 140141, 147-151, 153-155, 162, 167-168, 170-171, 185, 189-191 Cardiac Rehabilitation 1-4, 113-122, 140141, 189-191 Cardiovascular Disease 1, 113, 120, 131, 136, 138-139, 148, 155, 162, 169170, 191 Cardiovascular Fitness 113-114 Cognitive 1-11, 13, 15-16, 23, 28-29, 39, 43, 68, 75-76, 80, 86, 111, 189-191 Cognitive Rehabilitation 5-11, 13, 15, 39, 76, 111

Feature Selection 27, 44, 49, 54, 76, 156, 159, 180, 183, 186, 188

G Gradient Boosting Regression 101-102

H Healthcare 1-4, 7, 11, 13, 28, 41, 76, 105106, 110, 113, 115, 118, 120, 123, 128, 130, 133-136, 146-147, 149, 151, 154, 159, 162, 167-168, 186, 190-191 Heart Disease 114-121, 130, 135-136, 138-139, 141, 146-147, 149-156, 159-160, 162-164, 166-170, 173, 183-184, 186-188

Index

I Internet of Things (IoT) 1-4, 37, 87, 103, 122, 130, 133-135, 146-148, 150, 189-191

K k-Nearest Neighbor 91, 157

81, 85, 87, 89, 93-94, 120, 130, 150155, 162-164, 167-171, 173-174, 178, 182-183, 185-188

R Random Forest (RF) 82, 98, 158 Rehabilitation 2-11, 15-16, 21-22, 28-30, 34-35, 37-43, 76, 111, 113-122, 140141, 189-191

M S Machine Learning (ML) 14, 77, 91-92, 101, 105, 130, 155 Motor rehabilitation 15-16, 21, 37-38, 41-42 Myocardial Infarction 118, 122-123, 125133, 135-136, 145-150, 152-153, 187

Social Networks 44, 57, 61 Support Vector Classifier 91, 102 Support Vector Machine (SVM) 27, 79, 93, 158, 175

T N Natural Language Processing 46, 59-60, 85, 107

Thromboembolism 125 Twitter 46-48, 57-59, 61-65, 78

V P Virtual Reality (VR) 105 Particle Swarm Optimization 76, 151, 160, 163-164 Prediction 28, 49, 54, 57, 61, 65, 78-79,

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