Healthcare Data Analytics and Management (Advances in ubiquitous sensing applications for healthcare) [1 ed.] 0128153687, 9780128153680

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Healthcare Data Analytics and Management

Advances in Ubiquitous Sensing Applications for Healthcare

Healthcare Data Analytics and Management Volume 2 Series Editors Nilanjan Dey Amira S. Ashour Simon James Fong

Volume Editors Nilanjan Dey Techno India College of Technology, Kolkata, India

Amira S. Ashour Faculty of Engineering, Tanta University, Egypt

Chintan Bhatt Department of Computer Engineering, Charotar University of Science and Technology, Gujarat, India

Simon James Fong University of Macau, Taipa, Macau SAR

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom # 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-815368-0 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Mara Conner Acquisition Editor: Fiona Geraghty Editorial Project Manager: John Leonard Production Project Manager: Anitha Sivaraj Cover Designer: Matthew Limbert Typeset by SPi Global, India

Contributors Hameed Al-Qaheri Department of QM and IS, Kuwait University, Safat, Kuwait Soumya Banerjee Department of Computer Science, Birla Institute of Technology, Mesra, India Aadil Bashir Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Jai Prakash Bhati Noida International University, Greater Noida, India Chintan Bhatt Department of Computer Engineering, Charotar University of Science and Technology, Gujarat, India Tabish Digoo Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Ankur Dumka Graphic Era University, Dehradun, India Omid Mahdi Ebadati E. Department of Mathematics & Computer Science, Kharazmi University, Tehran, Iran Maria Firdous Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Galya Georgieva-Tsaneva Institute of Robotics, Bulgarian Academy of Sciences, Bulgaria Mitko Gospodinov Institute of Robotics, Bulgarian Academy of Sciences, Bulgaria Evgeniya Gospodinova Institute of Robotics, Bulgarian Academy of Sciences, Bulgaria Arifa Gulzar Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Gazanfar A. Hamdani Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India

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Contributors

Shirisha Kakarla Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India Gurjit Kaur Delhi Technological University, Delhi, India Irfan Khan Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Obaid Khan Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Nazir A. Loan Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Misbah Manzoor Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Azizollah Memariani Department of Financial Mathematics, Kharazmi University, Tehran, Iran Saurabh Pal Bengal Institute of Technology, Kolkata, India Tanmoy Pal Bengal Institute of Technology, Kolkata, India Shabir A. Parah Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Pijush Kanti Dutta Pramanik National Institute of Technology, Durgapur, India Anushree Sah University of Petroleum &Energy Studies, Dehradun, India Pinal Shah IT Department, CSPIT, Charotar University of Science And Technology (CHARUSAT), Changa, India Asif A. Shah Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Javaid A. Sheikh Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India

Contributors

Amit Thakkar IT Department, CSPIT, Charotar University of Science And Technology (CHARUSAT), Changa, India Dimpal Tomar Noida International University, Greater Noida, India Pradeep Tomar School of Information and Communication Technology, Gautam Buddha University, Greater Noida, India Bijoy Kumar Upadhyaya Tripura Institute of Technology, Agartala, India Mahdiyeh Yousefi Tabari Department of Industrial Engineering & Systems, Kharazmi University, Tehran, Iran

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Foreword When new technologies are gaining power, many things in the world are changed. Of course, healthcare should be one of the important subjects among those that benefit human beings. At the same time, big data has come of age and much undiscovered knowledge is being mined from big data, making data analytics for big data an important doctrine. There is no doubt that healthcare applications produce a kind of big data. Thus, technologies for data analytics play an important role in healthcare research. On the other hand, management skills related to healthcare data are also important. Therefore, healthcare data analytics and management are definitely crucial for future research. When I received the draft of this book, I understood that the authors are standing at the forefront of future research. Hence I am greatly honored to be writing this foreword. This book contains 11 chapters. Each chapter presents state-of-the-art technologies for developing healthcare systems and is written by well-studied researchers. For example, in Chapter 1 the challenges faced by today’s healthcare systems and different ways to overcome these with the help of pervasive healthcare are introduced. Chapter 2 addresses technology background, applications, and challenges in NoSQL cloud-based technology. In the remaining chapters, issues such as security, comparative analysis, smart ambulance systems, and so on, are discussed. This book is not only useful for constructing different healthcare systems, but also for studying theoretical analysis in heterogeneous healthcare data. I believe that this fruitful content is useful for researchers, educators, and professionals to explore future trends and applications in healthcare technologies. Alan C. Kay said that all understanding begins with our not accepting the world as it appears. I believe this is also true when we study the healthcare technologies for our future. I strongly recommend this book, written by well-known researchers like Nilanjan Dey, Amira Ashour, Chintan Bhatt and Simon Fong, for researchers and students who are interested in the topic of healthcare data analytics and management. I wish all the readers of this book great success in their healthcare study.

Sheng-Lung Peng Department of Computer Science and Information Engineering at National Dong Hwa University, Hualien, Taiwan

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Preface The healthcare industry is undergoing histrionic transformation in promoting patientcentered services based on reasoning and analysis for healthcare delivery and patient treatment. Such a transformation requires Big Data analysis, which is mainly associated with data analytics that has a significant role and impact on healthcare, social networks, and manufacturing. This Big Data arises from increased reliance on the digital technologies in healthcare systems, the clinical environment, and many other systems and applications. Healthcare providers are transforming their data mountains from raw information into actionable insights. In the healthcare sector, there is rising interest in effective healthcare data acquisition, analysis, and processing across different healthcare organizations. Efficient data management plays an imperative role in refining the performance of the healthcare systems in hospitals/clinics. Gathering, analyzing, interpreting, and evaluating healthcare data for specific performance measures assists healthcare professionals to make corrective adjustments, to identify accurate treatment plans, and to track outcomes. Several difficult puzzles face the providers in diagnostics and treatment, leading to the emergent need for advanced clinical decision support tools to leverage these new information resources. In addition, due to the central role of the electronic medical records in all aspects of healthcare, various organizations struggle with how to make their electronic medical records (EMRs) more secure, with easy access and sharing of the patient’s data and records. In order to solve such challenges, the oncology domain, which is undergoing vast changes due to precision medicine techniques, is considered an active tool in medical healthcare data analysis for easier access to large EHR data sets, for increasing the decision support system capabilities. This book contains 11 chapters covering the following studies. In Chapter 1 Dutta and Upadhyaya present the challenges faced by today’s healthcare systems and analyze how to overcome these with the help of pervasive healthcare. They present an assessment of the current and future IoT healthcare market along with a listing of the key players in the IoT healthcare market. In Chapter 2 the challenges and analysis of data migration techniques are introduced. Tomar et al. also present the technology background, applications, and challenges in NoSQL cloud-based technology. In Chapter 3 Tabari et al. establish a sophisticated platform in an attempt to develop a decision support system with a multiobjective programming model for an efficient allocation in the Iranian Ministry of Health and Medical Education. Shirisha developed a novel algorithm that uses a large key that offers higher security against attacks on data confidentiality with lower computational costs than the conventional ones, to provide security and privacy to large data sets at rest as well as during transmission, in Chapter 4. A large key bunch matrix is chosen with 2048 bits and the block cipher is developed, although the size of the key is further expandable with very trivial changes in the computational costs that would be incurred, in spite of using a lower configured computing system. The relaxation of the key-size constraints was realized, thus paving the way for using the keys with a large number of bits in the cryptosystem and enhancing the security, without overburdening the system with complex and time-consuming

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computations involved in the encryption and decryption procedures. Then, a comparative analysis of the semantic framework in healthcare is conducted by Shah and Thakkar. In Chapter 5 the authors analyze the data effectively for making an automatic decision regarding a patient’s health risk level, whether the patient is at low risk, moderate risk, or high risk. In Chapter 6 a smart ambulance system using the concept of big data and Internet of Things is proposed by Dumka and Sah. A novel approach is proposed with a smart ambulance equipped with IoT technology to disseminate the information to a main center or hospital, where doctors can treat a patient using equipped nurses in the ambulance through wireless body sensor technology (WBSN). In Chapter 7 Gospodinov et al. present the use of mathematical methods for the analysis of electrocardiographic data, specifically based on an essential diagnostic parameter, such as heart rate variability (HRV). In Chapter 8 Dumka et al. illustrate the role of body sensor networks in medicine and healthcare applications to collect information about the patients, generating their needful database, connecting payment gateways, and insurance providers along with a cadaver implementation system, including legal formalities within a single proposed platform. The proposed system uses an implantable wireless body sensor for transmission of information on the body part to the main station through a wireless sensor. The proposed system is integrated with wearable devices, wearable body area network (WBAN), near field communication (NFC), narrowband Internet of Things (NBIoT), and long range low power wireless platform (LoRa) integrated on a single monitoring system through a customized user interface. In Chapter 9, Banerjee et al. include a generic set of designs and libraries of data structure towards design methodologies. The features of sensing capacity of a sensor, preciseness, persistence, and data acquisition can be used as a core statistical model and thus an emerging machine-learning model is proposed to solicit a generic design methodology for IoT design. In Chapter 10, Parah et al. propose a reversible and secure Electronic Patient Record (EPR) embedding technique, using histogram bin shifting and RC6 encryption. Finally, in Chapter 11, Parah et al discuss a secure and reversible data hiding scheme for healthcare system using magic rectangle and a new interpolation technique. This volume is anticipated to disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques, and challenges to researchers, engineers, and developers for handling big data and data analytics and management in healthcare. We the editors have a great appreciation for the authors’ high-quality contributions as well as for the respected referees for their accurate, detailed, and timely review comments. Special thanks go to our publisher, Elsevier. We hope this book will stimulate further research in healthcare data analysis and management. Volume Editors Nilanjan Dey Techno India College of Technology, Kolkata, India Amira S. Ashour Faculty of Engineering, Tanta University, Egypt Chintan Bhatt Department of Computer Engineering, Charotar University of Science and Technology, Gujarat, India Simon James Fong University of Macau, Taipa, Macau SAR

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Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare

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Pijush Kanti Dutta Pramanik*, Bijoy Kumar Upadhyaya†, Saurabh Pal‡, Tanmoy Pal‡ National Institute of Technology, Durgapur, India* Tripura Institute of Technology, Agartala, India† Bengal Institute of Technology, Kolkata, India‡

CHAPTER OUTLINE 1 Introduction ......................................................................................................... 2 2 IoT, Smart Sensors, and Pervasive Computing ........................................................ 3 2.1 IoT ....................................................................................................... 3 2.2 Smart Sensors Augmenting the IoT ......................................................... 4 2.3 Pervasive Systems ................................................................................. 5 2.4 Difference Between Pervasive Systems and IoT ........................................ 6 2.5 IoT and Pervasive Systems: Complementing Each Other ............................ 6 3 Challenges in Traditional Healthcare Systems ........................................................ 8 4 Mobile and Pervasive Healthcare .......................................................................... 9 4.1 Context-Awareness in Healthcare .......................................................... 10 4.2 Connected Healthcare .......................................................................... 11 4.3 Pervasive Healthcare Vs Telemedicine ................................................... 11 5 Role of IoT in Healthcare .................................................................................... 14 5.1 Clinical Care ....................................................................................... 14 5.2 Remote Monitoring .............................................................................. 14 5.3 IoT and Medical Robotics ..................................................................... 14 6 Different Healthcare Sensors ............................................................................... 15 6.1 Basic Health Sensors ........................................................................... 15 6.2 Other Sensors Used in Medical Care Units ............................................. 25 6.3 Different Fitness Devices ...................................................................... 26 7 Benefits of Connected Healthcare ........................................................................ 28 8 Challenges in Connected Healthcare ................................................................... 31 Healthcare Data Analytics and Management. https://doi.org/10.1016/B978-0-12-815368-0.00001-4 # 2019 Elsevier Inc. All rights reserved.

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9 Healthcare Applications of Smart Sensors and IoT ............................................... 34 9.1 Smart Needle ...................................................................................... 36 9.2 iTBra .................................................................................................. 36 9.3 Coronary Artery Disease and IoT ............................................................ 37 9.4 Personalized Medical Care .................................................................... 38 9.5 Patient Monitoring ............................................................................... 38 9.6 Cardiac Rhythm Monitoring .................................................................. 39 9.7 Cardiac Rehabilitation .......................................................................... 39 9.8 Handling COPD Problems ..................................................................... 40 9.9 Smart Contact Lens for Diabetics .......................................................... 40 10 Use Cases ......................................................................................................... 40 10.1 Mississippi Blood Service: Maintaining Logistics Smartly ......................40 10.2 Finding Treatment for COPD ...............................................................41 10.3 Lahey Clinic Medical Center: Tracking Healthcare Equipment in Real-Time .....................................................................................41 10.4 Irin General Hospital: Improving Healthcare Quality ..............................43 10.5 Jefferson University Hospital: Providing Cognitive Environment of Care ..43 11 The IoT Healthcare Market: Present and Future .................................................... 44 12 Conclusion ......................................................................................................... 51 Acknowledgments ................................................................................................... 51 References ............................................................................................................. 51

1 INTRODUCTION Digitization of healthcare data over the past decade has brought revolutionary transformations to the healthcare industry. It has facilitated healthcare data to be more open and easily accessible. Not only have the private players bitten into a share of this apple, but also the government and the public stakeholders of the healthcare industry have progressed towards transparency by making the healthcare data generated and collected from different sources and stored at isolated data islands more usable, searchable, and actionable to all those concerned. Smartphones and tablets, convenient medical apps, wearable devices, and the development of a variety of wireless monitoring services have made healthcare services omnipresent. Medical devices are increasingly being connected to each other [1]. In fact, the trend in the adoption of connected medical devices is set to grow drastically in the coming years. And this rising number of connected medical devices, along with supported software and services, is turning connected healthcare into a proliferating platform for pervasive healthcare. The objective of an ideal healthcare system should be not only to provide effective healthcare services but also to support patients with quality of life by ensuring optimal functioning of overall health monitoring. This goal has led to the concept of a pervasive healthcare system that is able to monitor health status, provide medical facilities, and ensure sound health regardless of the location of the beneficiary. The traditional healthcare systems are highly concentrated in hospitals and clinics [2], but most people prefer to receive health services at their own residences as much

2 IoT, smart sensors, and pervasive computing

as possible. Even if they are required to use institutional medical facilities, they wish to minimize the time spent there. Typically, the direct clinical healthcare received by humans, on average, is negligible in comparison to the overall healthcare needed during a lifetime. Smart sensors [3], Internet of Things (IoT) [4, 5], and wearables [6, 7] have augmented the healthcare system, enabling remote monitoring and supporting the medical condition of patients in and out of clinics [8]. For instance, the blood glucose monitor may send a reminder to a diabetic patient to take insulin. If the patient is a pediatric diabetic, the system might suggest that parents recheck the diet plan if the sugar level continuously approaches higher levels. Similarly, a wearable sensor allows an orthopedic physician to monitor a patient as to whether the patient is doing prescribed exercises properly and regularly. Smart sensors and the IoT will allow clinicians to have passable and unified access to the details of their patients, including food habits and lifestyles. This means that collecting only the clinical data is insufficient. To get the real picture of the health status of an individual or the mass public, peoples’ health data need to be collected and analyzed on a regular basis, even if they are not a clinical patient [9]. Using current technology, patients can continuously be monitored even they are not under clinical care. The pervasive health applications have significantly increased health data liquidity, which has elevated the healthcare service industry as never before. Pervasive healthcare [10, 11] not only will allow healthcare service providers to monitor and manage patients remotely, but the patients also will be able to track their own medical records and status, perform basic analytics, and seek consultancy from doctors, pharmacists, and hospitals by referring to those documents. Easy-to-use app interfaces connected to the medical devices and databases have empowered users with easy and ubiquitous access to medical data through mobile devices. The rest of this chapter is organized as follows. Section 2 provides a brief overview of IoT and pervasive computing, pervasive systems, and their interrelationships. Section 3 highlights the challenges in traditional healthcare systems. Section 4 covers the basics of mobile and pervasive healthcare, including context-aware and connected healthcare, while distinguishing it from telemedicine. Section 5 emphasizes the role of IoT in healthcare. Various smart sensors related to healthcare, fitness and medical care units are discussed in Section 6. Section 7 covers the benefits of connected healthcare, while Section 8 points out the challenges in pervasive healthcare. Several real-life applications of IoT and smart sensors are presented in Section 9, showing the effectiveness of pervasive healthcare. Some use cases are discussed in Section 10. Section 11 presents an assessment of the current and future IoT healthcare market. And finally, Section 12 concludes the chapter.

2 IoT, SMART SENSORS, AND PERVASIVE COMPUTING 2.1 IOT IoT extends the Internet from connected computers to connected objects. The objects or “things” can be anything, provided they are connected to the Internet directly or indirectly [12]. The purpose of this huge-scale connectivity is to enable access to

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information pervasively and ubiquitously. This means information on any object can be accessed from anywhere. All the objects in the IoT have unique IDs making them identifiable on the Internet. In the Service Oriented Architecture (SOA) perspective, the objects have a digital representation on the Web for easy and flexible accessing through normal web portals. The basic components of IoT are [13]: • • • •

Sensors (e.g., temperature, light, motion, etc.) or actuators (e.g., displays, sound, motors, etc.) Computing resources (for processing sensor data) The communication medium (Bluetooth, ZigBee, RFID, etc., for short range and the Internet for long range) Application interface (for accessing IoT services)

In IoT, the “things” are typically embedded with some sort of sensors that sense the surrounding data and send them to a centralized housing, which is generally a private or public cloud. The sensed data are processed and analyzed here and, depending on the outcome, some events are triggered and notified to the subscribed application [14]. This whole concept allows remote monitoring of any object that is connectable. In IoT, the ability to identify an object globally, by other IoT devices, not only increases the utility of that object but also gives the ability to interact with other devices and communicate with the surrounding environment, making the things smarter and offering an overall intelligent environment surrounding that object.

2.2 SMART SENSORS AUGMENTING THE IOT The driving philosophy of IoT is to gain knowledge about an environment, state of things, user’s situation, and activities. This knowledge is made possible by sensors embedded in the things. The sensors, which act as the nervous system of IoT, consistently sense and capture the necessary data from the device itself, the environment, and users. In general, IoT devices are not intelligent but with the addition of smart sensors, these devices are transformed into smarter ones. The data thus gathered by sensors can be analyzed to find hidden patterns and to predict user need, device operability, etc. Learning from the procured data is what actually transforms IoT into an intelligent system. Based on the learning, appropriate services can be provided while peer devices interact or willing to interact. The application of sensors in devices has definitely made IoT an intelligent system. But the application of sensors has its own intrinsic issues related to sensor connectivity, integration and control, and data processing. Some of the issues associated with the usual sensors embedded in a device are: •

Sensors attached to devices produce an enormous amount of data to be streamed over the Internet. The sheer volume of data produced by sensors puts a huge burden on a network; due to network latency, data are often not processed synchronously or are lost. This creates a major setback for any real-time

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•

•

• •

applications that demand instant feedback, as late arrival of processed feed due to network latency causes a delay in real-time action. The other associated issue is the fact that sensors produce an enormous amount of noisy raw data and processing these data and filtering the noise is a CPUintensive job. Sensors produce data in a raw format. From the data integration and usability perspective, this raw data may not be acceptable to other heterogeneous systems. It is often necessary to abstract the data by extracting the needed information and representing it in a form acceptable to other heterogeneous systems. Sensors are prone to fault, producing erroneous or no data, and this raises a need for run-time diagnostics to determine the cause and magnitude of errors. Further, the usual sensors are separate entities that need to be connected separately to the interfacing circuit of the device. This causes difficulties related to embedding sensors into the device as well as their maintenance.

These issues have existed for some time in the industrial and research sectors, and to address them, sensors have improved over the years. In particular, they have evolved over several generations to become smart sensors, capable of performing a logic function, two-way communication, and decision making. A smart sensor consists of an actuator interfacing circuit, a sensing element, and a signal processing unit consisting of processor, memory, and software [15, 16]. A smart sensor can also be referred to as a basic sensing mechanism with embedded intelligence [17]. It can detect signals from multiple sensing elements, perform signal processing, data validation, interpretation and logging. Advances in sensing technologies and intelligent data processing have brought automation to the healthcare system. Smart sensors have truly taken IoT a step further by making it intelligent. Now instead of sensing and transmitting all the raw data, smart sensors can pick and transmit only the relevant data for further processing and storage, and hence reduce the network load. The embedded software program helps by filtering the noise from the raw data and then transforming it into a form that is portable and comprehensible to other systems. Further, self-calibration ability helps in self-diagnostics and making adjustments for error-free data sensing. In real-time systems, the embedded software in the smart sensor allows for self-decisions and thereby the actuator circuit can be instructed to perform necessary actions in real time. The use of smart sensors in IoT has transformed it into an accurate, fast, reliable and intelligent system. Smart sensor in IoT are often fused together, enabling context awareness and giving a holistic picture of the entire scene. Services can be invoked based on the context of what a user is doing, what action a machine is performing, and the state of the infrastructure and environment, or a combination of all [18].

2.3 PERVASIVE SYSTEMS The origin of the term pervasive is the Latin word pervadere, which means to spread or to go through. The word pervasive has been derived by blending the past participle

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stem of pervadere—or pervas—and the “ive” from English. The Cambridge Dictionary defines pervasive as “present or noticeable in every part of a thing or place.” Hence, a pervasive system refers to a distributed computing system that comprises computationally enabled devices and information systems that provide computation and, consequently, information anywhere and anytime. Pervasive computing enables small, low-powered, and embedded devices to compute the sensed or incoming data and disseminate the processed information to the desired sink wherever it is, with the help of ubiquitous communication networks. The idea of pervasive computing is to equip commonly used everyday objects with some sort of computing facility. Empowered by this embedded computing, these objects become digitally functional. The objects may also be connected to the network for remote accessing, thus adding more value to the system. In contrast to desktop computing, pervasive computing is done on any device, at any time and in any place, regardless of the data format. When integrated, the devices are capable of handing off tasks among each other, if required, for better task flow. The main objective of pervasive computing is to make the objects used in daily life interact with a human. Empowered by embedded computing abilities, these objects become more intelligent. The richer interaction between humans and intelligent physical objects creates ambient intelligence and thus improves the living experience of human beings.

2.4 DIFFERENCE BETWEEN PERVASIVE SYSTEMS AND IOT Pervasive computing and IoT are often confused, as if they were synonyms. The reason behind this confusion is that both computing paradigms have many similarities. For example, both try to infuse “life” into every physical object, making the objects “intelligent.” Devices in both systems are generally small—at the micro or nano level. Both aim to minimize human effort in everyday jobs. But despite the similar and overlapping properties, these terms differ in some parameters. Table 1 summarizes the differences between the two.

2.5 IOT AND PERVASIVE SYSTEMS: COMPLEMENTING EACH OTHER Even with the differences discussed here, pervasive computing and IoT seem to be inseparable. They need each other to realize their respective goals. Need for pervasive computing in IoT: Usually the IoT devices have limited or no computing capacity. To process the sensed data, they must depend on some local or remote processing units. The omnipresence of computationally enabled pervasive devices can support in this aspect. Need for IoT in pervasive systems: Due to the inherent ubiquity, IoT has become one of the key factors in realizing the full value of pervasive systems. In pervasive computing, the computing is dispersed throughout common objects used in daily life. IoT can extend this vision by knitting these disconnected and distributed objects together by connecting them to the Internet, making them not only

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Table 1 Difference between pervasive systems and IoT Era of origin Defining point Vision

Purpose

Goal

Focus Designing emphasis

Interaction with neighbors

Devices’ computing capability

Internet connection requirement

Pervasive system

IoT

The early 1990s All the physical objects should have computation capability The vision of pervasive computing was to make computing available anytime and anywhere To make the living experience of a human being much richer and more interesting by creating ambience intelligence among surrounding objects Provide a suitable platform for infusing intelligence into everyday objects by empowering them with computing facility and enabling users to interact with these objects Human-to-machine interaction The core effort of designing pervasive systems is to make the devices self-sufficient in terms of computing that is required for ambient intelligence The pervasive computing devices may be isolated or loosely coupled with other devices in the neighborhood. Hence, the degree of interaction with the neighbors is less The devices in a pervasive system necessarily have some computing power

The late 1990s All the physical objects should be connected The vision of IoT was to extend the principles of the Internet beyond the computers to physical things To automate many of the simple but essential daily chores without human intervention, thus relieving human beings to a significant extent Connecting all the physical objects, enabling them to interact among themselves, which will help in global automation

Philosophically, pervasive computing does not necessarily say that the objects should be connected to the Internet. It just says that every object should be able to compute. But without a global connection, the full benefits of pervasive computing cannot be achieved

Machine-to-machine interaction IoT is more about virtual representations of automatically identifiable objects

IoT devices are tightly coupled with other devices in the neighborhood. In most of the use cases, a number of devices work as a team for a particular purpose

It may not always be true for IoT devices. Many of the IoT devices are used only for sensing, with the computing job being offloaded to some other capable device The goal of the IoT vision is to access and control things remotely, for which the devices need to be connected to the Internet directly or indirectly

Continued

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Table 1 Difference between pervasive systems and IoT—cont’d Pervasive system

IoT

Network standards

For communication, pervasive systems follow the traditional communication standards, such as IP

Network permanency

If the devices are connected, a major possibility is that they are connected through an ad-hoc connection rather than a permanent connection All the computing points are basically vulnerable. Hence, the greater the span of pervasive systems, the more security concerns

For IoT, besides IP, some other specific standards are used, such as 6LoWPAN (IPv6 Low Power Wireless Personal Area Network), RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks), etc. As the IoT devices generate data continually and most often the data must be transported immediately, a permanent connection is required The widespread network with inclusive connectivity increases security vulnerability. The threat is greater than with pervasive systems

Security threat

computationally enabled but also globally connected. IoT makes pervasive systems more context-aware by providing a considerable amount of contextual information. This helps in improving the overall activity of the user. If the pervasive system is considered as the objective, then the IoT is one of the enablers to achieve that. With the help of IoT and its widespread adoption, pervasive computing can do wonders. The merger of IoT and pervasive computing, along with AI, gives us the foundation of cognitive IoT, which is going to change our lives in a major way [13]. Actually, both these concepts are being enhanced and fused and, subsequently, the differences between them have become blurred. That is why today these terms are often used interchangeably. In fact, the purely academic definition of these terms has insignificant value within the context of the rising popularity of their applications among the common people.

3 CHALLENGES IN TRADITIONAL HEALTHCARE SYSTEMS In spite of the significant advances in the medical sector, the potential benefits are not being passed on to the patients who need them, due to today’s inefficiently implemented healthcare systems. The existing healthcare systems face several challenges [19]: •

Thanks to the advances in medical science, related technologies, and healthcare delivery, the average lifespan of humans has increased by a significant margin. Hence the size of the senior population is growing considerably. Along with this aging population, the number of patients suffering from chronic conditions also

4 Mobile and pervasive healthcare

• • •

•

• •

increases. These chronic diseases require continuous observation and treatment, which places an additional burden on the existing inadequate healthcare infrastructure. Both public and private hospitals and clinics are experiencing shortages of qualified, experienced, and skilled medical professionals and experts. The accessing and delivering of healthcare services are becoming more costly as time goes on. Modern erratic and unhealthy lifestyles have caused different unforeseen disease patterns to emerge. Population health trends are shifting towards a challenging clinical disarray. Due to discrete and case-by-case diagnosis and treatments, traditional healthcare systems involve a high rate of hospital readmissions. This leads to further stress and strain along with repeated and intricate follow-up care. Lack of on-site medical facilities, experts, and global healthcare information systems increases the average length of stay in the hospital. The high rates and longer durations of hospitalization definitely make bigger holes in patients’ wallets, which is a real concern, especially for the lower socioeconomic groups.

The root cause of the incapability of our traditional healthcare systems to overcome these challenges is the discrete and isolated nature of the existing medical care facilities. The present healthcare system can hardly be called a system [20]. The hospitals and healthcare facility centers are working in silos. The infrastructures are built as isolated set-ups that do not interact among themselves. Also, a significant number of physicians practice solo, not connected to any healthcare system. They operate without any formal communications and collaborations. Although some systems are implemented by large companies with a long chain of hospitals across cities and even countries, they are not well designed. The lack of an integrated system directly affects the patient care services. The most critical, as discussed previously, is the coordination of continued care across different hospitals and for different diseases. Lack of coordination results in redundant and overlapping processes, resulting in delays in diagnosis and treatment, patient suffering, and increased medical bills [20]. The same applies when the patient is shifted to home for rehabilitation. Fragmented and ill-coordinated procedures affect the after-treatment care. To properly utilize the medical sector advances, we need an all-inclusive healthcare system that will integrate patients, different healthcare service providers, and other stakeholders.

4 MOBILE AND PERVASIVE HEALTHCARE The incorporation of IoT into healthcare services reasonably allows pervasiveness. The added pervasive features like mobility, adaptability, and context awareness provide the right information to the right person at the right place and time, adding great

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value to the modern health system. Pervasive healthcare envisions transforming the healthcare service focus from illness to wellbeing and staying healthy. For example, technology applications such as mobile devices, wireless links, and mobility enable trauma and emergency service personnel to send a patient’s medical details while in transit to the hospital. When a patient is in the close proximity to the doctor, the patient’s medical, health, and insurance data can be transferred from the patient’s mobile device to the physician, assuring that issues like allergies, blood pressure, glucose level, and other problems can be preassessed as a precautionary measure before starting the treatment. Similarly, doctors with smart devices have access to the patient’s vital status whether during an office visit or remotely from any other location. Besides the benefits to diagnosing, the mobility and pervasiveness of IoT devices allow monitoring of a patient’s vital statistics no matter their physical location. Wearables and health monitoring sensors continuously collect a patient’s physiology information, such as body temperature, heart rate, blood pressure, oxygen level, etc. A pervasive information system allows doctors and patients’ families to remotely access these health records. A close and precise observation can be made from anywhere for critical patients, whether they stay at home or in a hospital. Location-based services using GPS or RFID would enable tracking and supervision of patients suffering from mental illness, elderly persons, and disabled persons. A smart mobile application could remind the patient about routine check-ups and medication based on the patient’s current health context [21]. A wide range of mobile and pervasive healthcare applications like mobile telemedicine, personalized monitoring and assessment, location-based medical services, emergency response and management, and other context-based services are improving health services and patient satisfaction [21]. One of the significant characteristics of pervasive systems is adaptability. A patient’s contextual information like medical state and other situational information like the patient’s background and habits, environment, location, and social relationships are being used in making diagnostic decisions. These personalized diagnostics ensure treatment adapted to the patient, leading to more precise treatment [21]. Pervasive technology focuses on managing healthcare by looking forward, not only to the cure, but also to promoting health and wellbeing throughout a lifetime, as prevention is always better than cure. Pervasive technology can continually monitor and sense one’s health status before any medical event actually happens. Instead of monitoring for one medical parameter like ECG, the technology can look for multiple parameters that influence one’s health prospects. These data coming from various sensors are fused together to predict the present state and future medical complication [22]. An intelligent data processing platform and the natural interacting software can make decision-making more intelligent and context based.

4.1 CONTEXT-AWARENESS IN HEALTHCARE In evidence-based medical treatment, diagnosis is performed upon seeing the patient’s signs and symptoms. But for chronic and critical illness with a prolonged ailment history, these types of treatments are often not effective. For effective

4 Mobile and pervasive healthcare

treatment, it is necessary that the patients be monitored continually. In treating a medical event, it is important to understand patient context, which is the situational information that uniquely describes the patient’s medical condition. Patient context includes patient profile (e.g., name, age, weight, gender, and eating and sleeping habits), behavior and activity information, medical history, current medical condition, when medication is taken, etc. Analysis of patient context is very applicable to prescriptive decisions. Context-aware treatment would lead to personalized treatments that definitely prove effective for the particular patient. Sensor-based IoT devices provide an opportunity for context-aware health services. Sensor-based medical devices constantly sense the patient’s vital statistics such as glucose level, blood pressure, the oxygen level in blood, breathing, heart rate, brain activity, and chemical balance of the blood. These sensor devices, when connected to the internet, can record patients’ medical data over time. The patient contextual information, including present vital statistics and previous medical data, on analysis would enable the physician to access the complete medical situation of the patient, thus helping in proper diagnosis.

4.2 CONNECTED HEALTHCARE The other opportunity sensor-based IoT provides is connected healthcare. Sensors monitoring a patient connected over the Internet gather the patient’s critical data and transfer them to be stored. The data recorded can be shared with the physician to be analyzed further. This helps patients to get treatment from suitable doctors, clinics or hospitals across geographical locations, without worrying about carrying medical records or repetitive tests and diagnostic procedures. The connected device allows the physician to monitor a patient’s vitals remotely, literally from any location having an internet connection. The data exchanged between connected devices includes vital medical data like textual data, mostly numeric values, images, and video data as sonography, endoscopy, etc. For an automated remote monitoring system, patients are automatically monitored for any health complication. Any deviations in the data sensed are automatically analyzed, and the doctor and emergency services are notified. Further, the connected medical devices and medical services increase patient engagement. The patient is more informed and connected to physicians, medical staff, hospitals, clinics, and the insurance company. Though the potential of the connected devices and their pervasive applications is great, still deeper exploration is needed to deliver the best practical applications.

4.3 PERVASIVE HEALTHCARE VS TELEMEDICINE Since both involve remote operation, pervasive healthcare and telemedicine are often confused, though they are quite different approaches to healthcare. Telemedicine emphasizes clinical healthcare services such as diagnosis and recommendations from a distance using telecommunication as a medium of interaction [23].

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Much of the interaction between doctor and patient happens using telephone or Internet while consulting doctors are geographically a distance apart from the patient. In contrast, pervasive healthcare is an automated approach for remote health monitoring and prediction. The following brief description of the what and how of these techniques elaborates on the need for each. Telemedicine: Telemedicine is an approach to practicing medicine and clinical services from a distance, using telecommunication. It is used to overcome distance barriers and doctor shortages in rural or geographically remote areas. Telemedicine can be categorized into three types [23]: (i) Store-and-Forward (ii) Remote Monitoring (iii) Real-time Interaction In store-and-forward, the medical data of patients are sent to doctors for assessment. This is an asynchronous process and does not need both parties to be present at the same time; doctors at their convenience can assess the reports. Remote monitoring involves doctors or medical staff remotely monitoring a patient’s vital statistics in real time. Sessions are created where a patient at their own convenience at home or a nearby medical facility can interact with doctors using a telecommunication link [23, 24]. In general, practicing telemedicine over a distance consists of a doctor or group of doctors at one end with medical staff administering the patient or the patient alone at the other end, with the two ends communicating through the link. Doctors at a distance from the patient handle diagnosis, decision making, and recommendations using the link. Technologies like mobile communication, video conferencing, fax, scanners, and the Internet are widely used to communicate and exchange documents. The doctors assess a patient based on the video, X-ray and sonography images; infection photographs; and documents like prescriptions, pathological reports, ECG reports, etc., to make recommendations. Video conferencing and audio conversations are carried out for communication between doctors and patients. Telemedicine is a cost-effective way of bridging the gap between doctor and patient. Using the telecommunication advantages discussed, high-quality medical consultation can be provided to patients in almost any corner of the earth. This would help in increasing the availability of doctors to patients regardless of distance, caste, creed, and economic status [25]. Pervasive healthcare: In contrast to telemedicine, IoT is an approach for automated health monitoring. Precision in sensing and real-time analysis has given rise to applications like remote health monitoring and care of elderly or disabled persons. An IoT healthcare system is an amalgamation of different technologies, such as the Internet, Wi-Fi networks, sensors and embedded devices, and ubiquitous computing [26]. The objective of biosensors is physiological data collection, while IoT enables these devices (sensors) to interact with a data-processing platform over the Internet. Biosensors, embedded inside or attached outside to (the body of ) a patient, sense the different vital parameters. The physiological data are collected and subsequently transmitted to a local processing station using wireless links like Wi-Fi or Bluetooth.

4 Mobile and pervasive healthcare

The data collected at the local subsystem can be processed either locally or in the remote system. The data sent to remote systems are collected, stored, and analyzed for any anomalies. For any anomaly found in the data pattern, an alert is raised for urgent medical services. Often, for a quicker and more timely response, data are processed at a local subsystem, with the crucial/significant data being sent to the remote system for further analysis [27]. IoT devices capture health data over a continuum; the data recorded over the past can be analyzed for monitoring and predicting future health problems and patient progress in health fitness. Thus, IoT gives better opportunities to monitor and keep track of the patient remotely, with vital parameters such as temperature, heart rate readings, blood pressure, blood glucose, brainwaves, and oxygen level in the blood being captured through IoT devices and analyzed later on. Since health check-ups can be done at any convenient place, such as the patient’s home, routine visits to hospitals and other health service centers can be avoided. The advances in biosensors and the increasing popularity of wearable devices and health monitoring systems have led to numerous medical health applications [27]. Common sensors that have been widely used for monitoring patients are: • • • •

Inertial motion sensor (for monitoring human body posture) Bioelectrical sensors (ECG, EMG, EEG) Electrochemical sensors (for measuring glucose level, CO2 and oxygen level in blood, etc.) Temperature sensors

For the elderly and those suffering from chronic illness, wearable sensor devices enable constant monitoring of a person’s health. If any anomaly is found in the vital readings, the issues can be reported immediately. These wearable devices connect to the internet, collect the patients’ health data at a remote location, and send it to the hospital and to physicians, allowing for real-time monitoring of these patients. A fitness band wirelessly connected to tablets or mobile devices reports a person’s vital signs. Besides monitoring the health status, these devices also remind the patient to take medications, do exercises, and check blood pressure and cardio at a scheduled time. Different instruments like glucometers, cardio monitors, and blood pressure monitoring devices connected to IoT have been preferred by patients at home and also in hospitals [28]. The purpose of both the IoT and telemedicine is the betterment of peoples’ health, but in a different manner. Telemedicine incorporates doctors taking the lead role in assessment, decision-making, and recommendations, while IoT in healthcare tries to make monitoring, assessment, and predicting patient health more automated. The perspective of IoT in healthcare is improving a person’s health by monitoring and predicting health issues over a period and making health services available 24
7, while telemedicine is a process whereby physicians in a short session assess a patient’s current medical condition and make recommendations based on the signs/ symptoms. IoT in healthcare is a technologically intensive development. Automatic prediction and diagnosis need huge data processing and cognition; despite many pioneering advances, IoT still lacks the cognition for predicting and diagnosing a

13

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patient. The lack of true machine intelligence means that human expertise and cognition for patient treatment must still be sought. In contrast, the telemedicine process is a human/expert intensive process; doctors with their expertise make the diagnosis. It will be a long time before IoT will fully automate healthcare services (if ever); perhaps the real-time characteristics of IoT will be complementary to telemedicine. Physicians and other medical staff could have a real-time picture of a patient’s health status, and the past data patterns would enable them to analyze the patient issues more thoroughly and provide a correct diagnosis.

5 ROLE OF IoT IN HEALTHCARE 5.1 CLINICAL CARE Diagnosing a patient often requires various tests of blood, breathing, urine, cardiac status, etc. These tests, when carried out in a laboratory, take time to conclusively find the results. In an emergency, prescribing suitable medicine or treatment requires knowing the necessary vital statistics of a patient like glucose level, blood pressure, cardiac condition, etc. Sensor and sensor-based IoT for medical aids in this direction can provide a quick and pervasive way to test a patient’s vital statistics. In an emergency situation, such a device quickly assesses the patient anywhere and any time, without the patient waiting a long time for test results. The fast results can help doctors to correctly diagnose the patient, possibly saving a life. Further, using noninvasive IoT devices to monitor patients in hospitals, clinics, or at home can gather needed information about a patient over a long time, which can then be stored and analyzed. The analyzed data provides information over a continuum, which helps in yielding better clinical care at lower cost.

5.2 REMOTE MONITORING Sensor and sensor-based IoT devices for medical aid provide a unique opportunity for patient remote monitoring. Whether the patient is in hospital, at home or staying anywhere else, the use of IoT enabled medical aid and services would allow doctors to have patient information at their fingertips. A different electronic sensor connected to the patient could monitor vital statistics like chemical imbalances in the body, glucose level, nerve and brain activity, blood pressure, cardiac status, and further psychological and behavioral conditions. IoT devices connected wirelessly ensure that vital statistics data are available to doctors around the clock and from any location. Applying data analysis over the collected data would help physicians to make correct recommendations remotely.

5.3 IOT AND MEDICAL ROBOTICS Robots are machines programmed to perform tasks. Robots are quite useful in performing complex jobs that are time-consuming, laborious, and demand precision. Robotics have been used for decades in industrial applications, but in recent years

6 Different healthcare sensors

their application in the medical and healthcare sector has grown. Robots perform such integrated services as patient care and rehabilitation. The application of robotics has significantly improved areas such as patient monitoring, surgery, smart medical capsules, devices for leg and arm amputees, muscle disorders, and patients who have a cognitive or mental disability. Robots act in the real physical world, but prior to acting it is important to comprehend the context. IoT acting as the sensory level provides input to robots. The sensors and IoT devices detect the patient’s situational information and make meaningful contextual sense of the information by gathering real-world inputs. The data are processed intelligently to take appropriate real-world action. IoT sensors, besides informing robots, also send the data for storage and further analyses. The data analysis then helps in gaining better insight into the situation and further development of the robots. For a prosthetic robotic arm or leg, the sensors attached to the amputated region sense muscle pressure and the nerve system to identify the patient’s intention. Similarly, a camera providing visual sensory input analyzes the operating area and informs the robot to perform the necessary surgery. Similarly, sensors and IoT devices that continually monitor patients can inform a robot, based on patient situations or context, to attend the patient for medication or help the patient in rising from the bed or walking, etc. Medical robots are very useful, but to act appropriately they need IoT devices as sensory input to assess the situation [29].

6 DIFFERENT HEALTHCARE SENSORS 6.1 BASIC HEALTH SENSORS The health sensors are the most important components of the pervasive healthcare system. They are used to sense different health conditions and to keep track of health information. Since health sensors are implanted on or in the body to read the biological data, they are also referred to as biosensors. Different types of biosensors are deployed to measure various types of biological signals such as blood pressure, body temperature, oxygen saturation, heart rate, etc. in the human body under the aegis of a pervasive healthcare environment, using IoT. The working principle of these devices may vary depending on their applications. For example, the oxygen saturation biosensor, also known as a pulse oximeter, employs an optical sensing mechanism to determine light absorption at two different wavelengths. Fine wires blended in the jacket are used to intercept the electrical signals corresponding to the body temperature of a patient. However, in a broader perspective, these sensors may make use of some common characteristics in terms of their construction and functionality, as shown in Fig. 1. In general, the biosensors may be composed of the following four main parts [30, 31]: • • • •

Bioreceptor Transducer Signal processor Output/communication system

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CHAPTER 1 Pervasive healthcare

Bioreceptor

Transducer

Signal processor

FIG. 1 Schematic diagram of biosensors.

The bioreceptor senses a biological parameter in a human body. Bioreceptors are also referred to as recognition elements and may be of various types, such as antigen, antibody, enzyme, protein, etc. The parameter under consideration is translated into the equivalent electrical signal by the transducer block. A biosensor may use electromechanical, optical, mass change or calorimetric, etc., types of transducers, depending on the nature of the input. The signal so obtained may be contaminated with noise and need filtering. In addition, the strength of the signal needs to be enhanced so that it can be sent for display or transmission for storage and analysis purposes. The signal processing block performs the noise removal and amplification operation. Elimination of noise is usually done with the help of digital finite impulse response (FIR) filters. Operational amplifiers (op-amps) are the ideal choice to raise the strength of a weak signal. The local display block is optional. In a pervasive healthcare system with IoT, the biological parameter so obtained is transmitted to the server located in the healthcare center using various wireless protocols such as Wi-Fi, Bluetooth, ZigBee, and so forth. In this section, different health sensors supporting pervasive healthcare systems are discussed. Table 2 summarizes these devices, mentioning the associated challenges.

6.1.1 Blood pressure sensor Blood pressure measurement aims at determining pressure flowing through the blood vessels against the artery walls. The blood pressure is said to be normal if the flow of blood in the artery is normal. Due to some reason, if blood flow is restricted, then the blood pressure goes high. Increased blood pressure may cause severe medical problems [39]. The device used to measure blood pressure is called a sphygmomanometer. The blood pressure measurement process identifies two pressures inside the blood vessels: one, when the heart beats, is called the systolic pressure, and the other, when the heart is at rest between two heartbeats, is referred to as diastolic pressure. There are several conventional blood pressure measurement methods available today. A mercury sphygmomanometer is considered to be the “gold standard,” which consists of a straight glass tube in assembly with a reservoir containing mercury. An aneroid sphygmomanometer is another type that works on a similar principle to that of the mercury sphygmomanometer, but instead of mercury it uses a mechanical dial to display the blood pressure. The digital manometer is based on the oscillometric

Table 2 Different healthcare sensors supporting a pervasive healthcare system and their challenges Application area Health monitoring

Device Blood pressure sensor Body temperature sensor ECG sensor

EEG sensor

Acceleration sensor Pulse oximeter Heart rate monitor Cardiac rhythm monitor Pill camera

Sensor technology/ sensing mechanism

General purpose/ focus

Communication protocols used

Pulse transit time (PTT) [32]/pulse wave transit time (PWTT) [33] Thermistor/ thermoelectric/optical based Capacitively coupled [35]/insulated electrode [32] ECG Noncontact biopotential electrodes [36]

Detection of hypertension

Wi-Fi, Bluetooth, ZigBee

Detection of fever/ circulatory shock [34]

Bluetooth

Piezoresistive/ piezoelectric/differential capacitance types Infrared light based Infrared light based Capacitive electrode [37] Capsule camera

Identifies cardiac abnormalities

Bluetooth, ZigBee

Determines various electrical activity in the brain Monitoring human physical activity

Bluetooth

Determines oxygen level in blood Determines cardiovascular fitness Detection of irregular heart functioning

Bluetooth, ZigBee Bluetooth, ZigBee Bluetooth, ZigBee

Identification of various gastrointestinal tract disorder

ZigBee, Ant, Ultra-Wide Band (UWB), Bluetooth

Bluetooth, ZigBee

Communication challenges

• Signal

• • • •

• •

attenuation by body’s line of sight absorption Low power communication Radio Frequency Interference from multiple sources Handling of huge computational loads Availability of ubiquitous mobile networking Identification of each patient uniquely Prioritization of vital signals over others

Data processing challenges

• Confidentiality • • • •

•

of patients’ information Secure access control Privacy of patients Secure wireless networking Protection against malicious modification Distributed data access security

Continued

Table 2 Different healthcare sensors supporting a pervasive healthcare system and their challenges—cont’d Application area Medical care unit

Fitness

Device Room temperature sensor Barometric pressure sensor Light sensor

Motion and activity sensor Wristband

Sensor technology/ sensing mechanism

General purpose/ focus

Communication protocols used

Communication challenges

Data processing challenges

Thermistor/resistive temperature detector (RTD) Capacitive sensor/ microelectromechanical systems (MEMS) Light dependent resistor (LDR)/photo diode/ photo transistor MEMS sensor

Monitoring and control of room temperature

Wi-Fi

Monitoring of room pressure and proper airflow Illumination control

Wi-Fi

Wireless networking with reliability, dependability, suitability for healthcare providing infrastructure

Relatively lower level of data security requirement

Detection of mobility

Bluetooth

Counting of heart rate, steps, calories consumed, etc. Monitoring of pace, heart rate, cadence, and power, etc. during physical activities like swimming, cycling, etc. Keep track of gait and physical fitness

Bluetooth

• Low power

• Confidentiality

Bluetooth

communication • Availability of ubiquitous mobile networking

of user information • Secure access control • Privacy of user

Optical sensor

Eye gear

Digital sensor technology

Smart shoe

Ground contact Force (GCF) sensor [38]

Wi-Fi

Bluetooth

6 Different healthcare sensors

principle of measuring blood pressure and uses an electronic pressure sensor for measuring the blood pressure; the readings are given out digitally on a display. The various methods described so far use a cuff, which is not suitable for continuous monitoring in the pervasive environment, especially while sleeping. One of the viable methods for ambulatory blood pressure monitoring in the pervasive scenario is pulse transit time (PTT) [32] or pulse wave transit time (PWTT) [33]. PTT refers to the time delay between two arterial sites. PTT can be estimated from the time gap between the proximal and distal arterial waveforms. Blood pressure is inversely related to the PTT. Finally, the PTT values in a millisecond are calibrated into blood pressure in millimeters of mercury. This method of blood pressure measurement is cuffless and can be interfaced with a smart hub, such as a smartphone, to support a pervasive healthcare system.

6.1.2 Body temperature sensor Body temperature is one of the important signs to provide insight into the physiological state of a person [40]. The normal core body temperature is approximately 37°C. An abnormal body temperature may be considered as an important indicator that the person is suffering from an infection, fever, or low blood flow due to circulatory shock [41]. The body temperature of a healthy person may also vary marginally depending on the time of measurement during the day and the location of the measurement on the body. Therefore, while measuring the temperature, care must be taken to calibrate the temperature readings appropriately. In the pervasive healthcare environment, wearable temperature sensors are often used and are placed on the wrist, the arm, or the chest. The temperature of these body parts is lower by approximately 5°C than the body-core temperature. In wearable and other noninvasive technology, thermistor-based sensors are preferred over thermoelectric or optical-based sensors for body temperature measurement [40] due to their improved sensitivity and accuracy. The resistance of a thermistor varies with respect to change in temperature. In one class of thermistor, called positive temperature coefficient (PTC) type, the resistance increases with increase in temperature. In the other category, the resistance decreases with a decrease in temperature and these are referred to as a negative temperature coefficient (NTC) type thermistor. Conductive textile wires are used as the NTC type of sensor in the wearable smart jacket for neonatal patients [42]. In addition, nickel and tungsten wires are popularly used as fabric sensors due to their high reference resistance, sensitivity, and availability for such wearable applications. Temperature sensors based on integrated circuits (ICs) like the LM35 may also be directly placed on the skin of a patient requiring continuous temperature monitoring.

6.1.3 ECG sensor Electrocardiography (ECG) is one of the oldest and simplest tests used to determine vital information about the cardiovascular system of a patient [35]. An ECG shows electrical activity of the heart muscles in response to electrical depolarization, which is traced on graph paper. An ECG can be performed using various techniques.

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One conventional method, namely wet ECG, in which 12 or 15 Ag-AgCl electrodes are fitted to the chest, arms, hands, and legs, uses a special type of conductive gel that works as a conducting medium for electrical signals from the body to the electrodes. Wet ECG suffers from drawbacks with long-term use, such as patient allergies due to contact with metal electrodes and the gel, or surface degradation of electrodes leading to deterioration of signal quality. In the past few decades, there has been a sharp increase in coronary diseases, especially for the elderly. In the case of high-risk patients, continuous monitoring of the ECG signal may be very helpful in immediately detecting pathological signatures and arrhythmias [35]. In such situations, deviations from normal ECG readings can be dynamically identified and the patient can be sent immediately to the healthcare center for preventive actions. In the pervasive healthcare environment, such an arrangement is possible wherein patients can engage freely in their daily routine activities and also be monitored for ECG signals continuously. Capacitively coupled ECG (CC-ECG) is an alternative method in which an ECG signal is obtained without conductive contact with the patient’s body [43]. In this method, a thin layer of insulator isolates the human body from a metal plate electrode and thereby forms a capacitor. The electrodes can be applied to a cloth that can be worn by the person requiring continuous monitoring of ECG signals. The CC-ECG sensor-based ECG can be designed to be portable and small and can work wirelessly to support a pervasive healthcare environment. To enhance battery life, use of lowpower components including techniques like idle mode, low power wireless protocol, etc., are adopted.

6.1.4 EEG sensor Electroencephalogram (EEG) is a technique to measure the electrical activity of the brain of a person using small electrodes at multiple locations on the scalp [44]. It is a noninvasive method that can be applied repeatedly to patients, normal adults, and children without any risks. The electrical impulses are generated by nerves firing in the brain, having an amplitude in the range of microvolts (μV) and frequency between 8 and 50 Hz. EEG signals can be classified into the five bands of electromagnetic waves [45] shown in Table 3. EEG is sensitive to a variety of states ranging from stress state, alertness to resting state, hypnosis, and sleep [46]. Beta activities dominate when a person is in the normal state of wakefulness with open eyes. In a relaxation or drowsiness state, alpha activities are predominant. If a person feels sleepy, the power of lower frequency electromagnetic waves increases. EEG is used in many medical and nonmedical applications. Some of the medical applications are monitoring alertness, coma and brain death; locating areas of damage following head injury, stroke, tumor, etc.; investigating epilepsy and locating seizure origin; analyzing sleep disorders and physiology [46], and so forth. Among the nonmedical applications, EEG is used for the psychological training of sports persons, helping them to enhance focus and to have effective management of stress or fatigue. EEG can also be used for the study of cognitive processes, decision making, driver alertness, etc.

6 Different healthcare sensors

Table 3 Frequency bands, corresponding activities and state of the brain with the strength of EEG signal Activity

Frequency (Hz)

Strength (μV)

δ

0.5–3

100–200

θ

4–7

8) are considered for the generation of the second decimal number. On following the same procedure, the succeeding bits of this column under consideration can be expressed as the decimal numbers. Later, the (m + 1)th column of the matrix is considered to generate the decimal integers by following the same as in the 1st column. The 2nd column of this matrix is then selected for the conversions in the same way. After this, (m + 2)th column bits, which are the 2nd column bits of the latter half of the matrix, are taken. All these numbers obtained are arranged in a matrix, one after another in a row-wise manner. However, if the dataset contains less than eight rows, then for generating the decimal integer, the bits of the first column of the first half and the first column in the second half of the matrix are considered. These decimal numbers are inserted in the matrix of size n  n, one after the other in a row-wise manner. In this approach, the dataset contents are mixed very thoroughly. The function inverseMixBits(), used in the pseudocode of the decryption procedure, perform the reverse process of the mixBits().

5 ILLUSTRATION AND THE OUTCOMES For a practical implementation, a dataset with a total of 237 entries and 46 different attributes in [34] is considered. The first two records, the attributes, and the corresponding values of the dataset are shown here for reference: JURISDICTION NAME,COUNT PARTICIPANTS,COUNT FEMALE,PERCENT FEMALE,COUNT MALE,PERCENT MALE,COUNT GENDER UNKNOWN,PERCENT GENDER UNKNOWN,COUNT GENDER TOTAL,PERCENT GENDER TOTAL,COUNT PACIFIC ISLANDER,PERCENT PACIFIC ISLANDER,COUNT HISPANIC LATINO,PERCENT HISPANIC LATINO, COUNT AMERICAN INDIAN,PERCENT AMERICAN INDIAN,COUNT ASIAN NON HISPANIC,PERCENT ASIAN NON HISPANIC,COUNT WHITE NON HISPANIC,PERCENT WHITE NON HISPANIC,COUNT BLACK NON HISPANIC,PERCENT BLACK NON HISPANIC,COUNT OTHER ETHNICITY, PERCENT OTHER ETHNICITY,COUNT ETHNICITY UNKNOWN,PERCENT ETHNICITY UNKNOWN,COUNT ETHNICITY TOTAL,PERCENT ETHNICITY TOTAL,COUNT PERMANENT RESIDENT ALIEN,PERCENT PERMANENT RESIDENT ALIEN,COUNT US CITIZEN,PERCENT US CITIZEN, COUNT OTHER CITIZEN STATUS,PERCENT OTHER CITIZEN STATUS, COUNT CITIZEN STATUS UNKNOWN,PERCENT CITIZEN STATUS

5 Illustration and the outcomes

UNKNOWN,COUNT CITIZEN STATUS TOTAL,PERCENT CITIZEN STATUS TOTAL,COUNT RECEIVES PUBLIC ASSISTANCE,PERCENT RECEIVES PUBLIC ASSISTANCE,COUNT NRECEIVES PUBLIC ASSISTANCE,PERCENT NRECEIVES PUBLIC ASSISTANCE,COUNT PUBLIC ASSISTANCE UNKNOWN,PERCENT PUBLIC ASSISTANCE UNKNOWN,COUNT PUBLIC ASSISTANCE TOTAL,PERCENT PUBLIC ASSISTANCE TOTAL 10001, 44,22, 0:5, 22,0:5, 0,0,44, 100,0, 0,16, 0:36,0, 0,3,0:07, 1,0:02, 21,0:48,3, 0:07,0, 0,44, 100,2, 0:05,42, 0:95,0, 0,0,0, 44,100, 20,0:45, 24,0:55,0, 0,44, 100 (12)

After converting the dataset into the EBCDIC, the initial 256 characters, as shown in Eq. (13), are taken into account for constructing the matrix P. JURISDICTION NAME, COUNT PARTICIPANTS, COUNT FEMALE, PERCENT FEMALE,COUNT MALE,PERCENT MALE, COUNT GENDER UNKNOWN, PERCENT GENDER UNKNOWN, COUNT GENDER TOTAL, PERCENT GENDER TOTAL, COUNT PACIFIC ISLANDER, PERCENT PACIFIC ISLANDER, COUNT HISPANIC LATINO, PERCENT HISPA

(13)

For the illustration, the same P is considered here as the initial raw data to be secured, and it can be written as 2

209 6 197 6 6 193 6 6 197 6 6 107 6 6 197 6 6 197 6 6 217 P¼6 6 213 6 6 217 6 6 199 6 6 213 6 6 196 6 6 198 6 4 213 213

228 107 213 107 195 213 213 195 214 64 197 227 197 201 227 214

217 195 227 215 214 227 196 197 230 227 213 64 217 195 64 107

201 214 226 197 228 64 197 213 213 214 196 215 107 64 200 215

226 228 107 217 213 212 217 227 107 227 197 193 215 201 201 197

196 213 195 195 227 193 64 64 195 193 217 195 197 226 226 217

201 227 214 197 64 211 228 199 214 211 64 201 217 211 215 195

195 64 228 213 212 197 213 197 228 107 227 198 195 193 193 197

227 215 213 227 193 107 210 213 213 215 214 201 197 213 213 213

201 193 227 64 211 195 213 196 227 197 227 195 213 196 201 227

214 217 64 198 197 214 214 197 64 217 193 64 227 197 195 64

213 227 198 197 107 228 230 217 199 195 211 201 64 217 64 200

64 201 197 212 215 213 213 64 197 197 107 226 215 107 211 201

The encryption key bunch matrix, of size 16, may be taken as

213 195 212 193 197 227 107 228 213 213 195 211 193 195 193 226

193 201 193 211 217 64 215 213 196 227 214 193 195 214 227 215

3 212 215 7 7 211 7 7 197 7 7 195 7 7 199 7 7 197 7 7 210 7 7 197 7 7 64 7 7 228 7 7 213 7 7 201 7 7 228 7 7 201 5

193 (14)

123

124

CHAPTER 4 Fast performing key bunch matrix block cipher for large datasets 2

19 6 149 6 6 211 6 6 161 6 6 65 6 6 179 6 6 7 6 6 153 Enc Key ¼6 6 181 6 6 187 6 6 239 6 6 17 6 6 107 6 6 247 6 4 157 141

173 35 213 147 221 119 45 103 157 203 115 197 105 221 89 191

1 205 207 77 195 53 71 119 185 159 233 27 245 111 199 103

247 117 29 53 171 45 177 209 11 107 187 45 75 231 121 107

187 177 91 67 145 11 57 43 153 91 227 141 99 135 193 221

205 15 237 169 253 205 203 189 127 197 71 117 185 209 23 251

221 161 159 203 65 97 145 149 55 229 85 161 97 181 47 79

157 173 9 189 115 145 189 67 241 37 249 91 211 251 115 147

129 51 49 159 229 223 221 243 73 177 175 191 151 85 159 249

15 185 29 113 173 135 191 155 205 23 77 145 239 37 127 41

249 203 69 185 147 239 197 95 255 205 29 45 229 119 203 91

125 61 35 181 63 21 109 39 227 153 245 229 105 91 167 225

69 79 113 59 181 155 227 117 229 177 69 49 233 93 3 177

127 93 49 19 147 83 131 67 149 93 179 145 155 93 239 85

3 193 245 239 33 7 7 179 119 7 7 117 43 7 7 11 109 7 7 133 183 7 7 1 75 7 7 251 135 7 7 9 21 7 7 253 241 7 7 189 249 7 7 191 77 7 7 179 213 7 7 15 221 7 7 249 47 5 5 155 (15)

Implementing the multInverse(), using the basic concept of multiplicative inverse, for the given Enc_Key shown previously, the corresponding Dec_Key, i.e., decryption key bunch matrix, is derived and written as 2

58 6 190 6 6 141 6 6 26 6 6 189 6 6 110 6 6 1 6 6 100 Dec Key56 6 92 6 6 70 6 6 85 6 6 212 6 6 233 6 6 88 6 4 197 253

125 223 52 0 148 197 146 50 10 15 8 70 62 135 205 35

140 33 198 245 30 159 84 54 136 26 25 239 187 212 217 224

75 102 148 81 85 67 215 3 150 201 80 99 61 153 159 212

9 237 83 199 174 112 77 76 207 69 129 44 221 82 69 105

209 11 159 230 52 191 151 29 134 242 120 204 230 54 217 100

148 93 15 79 195 126 44 103 188 229 182 49 87 35 54 184

230 234 128 190 76 234 141 143 135 42 205 3 203 220 143 216

62 147 0 222 33 66 238 241 231 43 135 38 71 49 232 31

52 163 169 197 100 138 148 174 109 19 249 173 39 185 27 40

94 125 193 202 35 239 120 1 108 55 68 243 16 13 19 93

184 171 116 169 141 108 182 75 134 129 12 228 160 214 252 125

76 56 114 8 109 98 208 240 103 178 131 111 139 97 202 38

195 7 232 10 73 148 20 32 115 47 41 252 105 120 238 127

3 213 28 47 123 7 7 167 32 7 7 241 47 7 7 205 244 7 7 188 40 7 7 182 5 7 7 70 187 7 7 153 188 7 7 255 96 7 7 98 95 7 7 32 174 7 7 232 41 7 7 251 155 7 7 96 166 5 145 244 (16)

On application of the encryption procedure, as discussed in Section 4, on the input matrices (14, 15), the corresponding enciphered data is obtained as follows:

5 Illustration and the outcomes

2

38 6 118 6 6 151 6 6 99 6 6 43 6 6 113 6 6 11 6 6 48 C56 6 207 6 6 39 6 6 98 6 6 123 6 6 113 6 6 137 6 4 202 246

146 192 91 145 211 199 66 132 198 129 138 19 177 168 92 192

39 219 204 138 22 206 37 16 140 13 180 153 222 147 174 61

107 29 237 206 16 238 8 139 47 217 57 198 168 238 141 47

57 81 2 88 234 84 49 130 173 198 38 147 198 54 59 25

225 123 84 150 101 235 218 237 128 55 52 159 131 137 252 34

208 211 215 165 49 157 194 202 182 121 121 191 169 196 188 223

66 228 120 4 143 66 237 43 12 87 86 41 127 253 210 80

187 4 57 3 84 110 206 245 102 28 2 26 208 173 159 117

165 66 195 163 248 96 243 118 228 150 77 109 29 69 44 6

144 77 141 13 217 220 240 28 125 109 116 114 164 245 78 67

204 48 95 208 178 162 79 92 162 159 131 220 101 99 224 198

0 92 180 58 115 30 28 190 209 187 145 242 88 216 219 55

149 237 36 218 226 94 112 19 173 65 121 143 19 19 120 173

132 180 169 248 147 222 23 209 102 50 68 115 216 61 122 43

3 139 223 7 7 150 7 7 77 7 7 50 7 7 155 7 7 74 7 7 123 7 7 71 7 7 78 7 7 6 7 7 217 7 7 95 7 7 20 7 7 57 5 135 (17)

For the decryption procedure, discussed in Section 4, the inputs: Dec_Key of (16) and enciphered data of (17) are considered and the original P, shown in Eq. (13), is obtained again. In the block ciphers, one of the desirable properties of the algorithms involved in the cryptosystem is the large-scale change caused due to slight changes in the inputs. This is termed the “Avalanche effect.” It’s the process to study the impact of the flip of one bit in either the Enc_Key or the raw CSV formatted text. For examining this effect, the 15th row, 6th column element of matrix (15) is changed from 23 to 24. Thus, there is a one-bit flip in the Enc_Key. With the use of the modified Enc_Key and the same raw CSV input of 256 characters, given by matrix (14), and the encryption procedure discussed in Section 4, the corresponding outcome C is acquired in the form 2

212 6 93 6 6 26 6 6 100 6 6 225 6 6 168 6 6 53 6 6 119 C56 6 90 6 6 93 6 6 172 6 6 171 6 6 187 6 6 87 6 4 207 253

102 210 180 162 44 127 128 94 161 145 180 209 134 195 234 193

79 246 19 137 30 76 238 61 41 9 170 126 54 137 243 213

50 55 3 24 39 27 91 210 156 236 83 162 201 173 21 41

246 172 188 112 103 163 231 22 85 99 156 58 181 77 58 42

85 132 23 32 17 230 233 183 8 174 210 36 79 73 113 56

191 71 47 146 163 199 61 143 33 174 126 223 51 200 117 117

243 153 165 29 200 187 7 121 235 234 83 61 56 1 22 16

240 98 141 5 130 103 206 9 168 107 178 58 37 130 46 238

98 123 244 3 78 55 182 176 2 179 254 140 18 108 73 164

224 160 240 250 14 187 120 180 221 83 197 26 133 145 190 251

9 130 115 237 113 13 118 215 82 3 183 160 149 219 72 130

45 107 128 104 252 225 236 224 164 101 203 59 33 245 119 89

73 159 112 193 54 151 164 95 101 45 56 65 6 124 18 229

125 199 38 214 246 45 94 183 250 231 121 79 142 199 240 3

3 24 41 7 7 253 7 7 248 7 7 251 7 7 232 7 7 203 7 7 243 7 7 199 7 7 183 7 7 204 7 7 145 7 7 44 7 7 118 7 7 31 5 135 (18)

125

126

CHAPTER 4 Fast performing key bunch matrix block cipher for large datasets

The two outcomes shown in matrices (17, 18) vary by 1045 bits (out of total 2048), when compared in their binary forms. The total number of element changes is 254 out of 256, in EBCDIC form. This indicates that the cipher is predictably very good. Instead of change in the Enc_Key, if on changing the 4th row, 12th column element in the CSV formatted data of (14), from 197 to 213, one binary bit is changed. With the altered raw input, the Enc_Key of (15), and the encryption algorithm, the ciphertext C is acquired in the form 2

217 6 185 6 6 219 6 6 168 6 6 75 6 6 161 6 6 222 6 6 44 C56 6 237 6 6 53 6 6 140 6 6 72 6 6 202 6 6 17 6 4 75 238

62 220 176 243 39 153 70 223 142 2 143 188 164 23 220 244

115 12 220 1 59 34 33 30 204 137 122 226 119 215 32 64

254 193 23 117 171 128 211 1 167 105 120 173 122 107 103 195

231 135 119 206 108 72 52 32 116 94 122 80 134 69 164 225

110 41 45 103 179 37 186 149 41 88 68 149 199 91 74 130

11 238 62 19 121 147 196 87 109 91 248 155 165 152 225 57

29 66 138 89 90 40 147 9 181 166 233 43 235 127 164 56

131 116 181 127 155 243 135 161 178 44 137 23 172 86 108 137

70 114 22 199 176 131 121 154 175 217 206 83 234 19 7 201

161 93 126 166 3 172 172 185 49 112 15 134 123 106 231 69

187 76 84 220 232 236 14 244 197 148 155 26 40 65 226 2

142 30 56 226 201 114 243 0 161 239 25 98 54 147 177 160

31 37 170 71 202 239 188 155 204 197 22 57 214 218 204 45

229 225 1 110 161 33 154 147 129 234 85 79 142 235 5 235

3 200 60 7 7 226 7 7 54 7 7 195 7 7 130 7 7 204 7 7 243 7 7 60 7 7 11 7 7 192 7 7 121 7 7 22 7 7 246 7 7 117 5 135 (19)

Comparing matrices (17, 19) in their binary forms, the two ciphertexts under consideration show a variance of 1000 bits out of 2048 bits. In EBCDIC forms, they differ by 255 elements. This implies that the cipher is a strong one.

6 SIMULATION SET-UP AND PERFORMANCE ANALYSIS The simulations of the key generation, encryption, and decryption are conducted on the desktop with an Intel i3-6100 CPU, 3.70 GHz, 4.00 GB RAM, and 32-bit Windows 7 operating system. The block size considered for the illustration is the 16  16 Decimal integers, thus containing 2048 binary bits. The number of iterations chosen in the encryption as well as decryption is 16, in order to enhance the confusion and diffusion thoroughly within a block. The algorithms are implemented in Java 1.5.0_05 (32-bit), using the ECB mode. The key generation program took 0.16  104 seconds for outputting the 16  16 odd integers random key, thus composed of 2048 binary bits. The programs are executed on a couple of datasets of varying sizes for comparing the time elapsed in the encryption and decryption processes, keeping the block size the same 2048 bits. The elapsed time durations are presented in Table 2 and Fig. 3 for the fast dataset block cipher.

6 Simulation Set-Up and performance analysis

Table 2 Time elapsed in the encryption and decryption programs of fast dataset block cipher (with 2048 bit block size, 2048 bit key size 16 rounds for each block) Dataset size

Encryption time (in seconds)

Decryption time (in seconds)

1.02 MB 2 MB 5 MB 10 MB 20 MB

1.71 3.04 6.02 9.48 17.8

1.91 3.81 6.90 10.20 18.12

20 18 16

Time elaspsed in seconds

14 12

Encryption time (in seconds)

10

Decryption time (in seconds)

8 6 4 2 0 1.02 MB

2 MB

5 MB

10 MB

20 MB

Dataset size

FIG. 3 Graph for time elapsed for encryption and decryption for the fast dataset block cipher (with 2048 bit block size, 2048 bit key size 16 rounds for each block).

The well-known algorithms of symmetric key cryptosystems, namely DES, Triple DES, and AES Blowfish, are also implemented on the previously described system set-up to provide more perspective on the performance of the key bunch block cipher using the results obtained. Table 3 indicates the simulation parameters of the block ciphers used in the programs executed in Java, and the time expended in generating the key and the encryption for one round of one block are also shown. With the scalar matrix multiplications involving corresponding modular multiplications between the raw input elements and the key bunch matrix elements, as shown in Eqs. (13), (15), respectively, the time required to encrypt a block of 2048 bits with the key of 2048 bits is 0.0012 s. This significantly plays a pivotal role in expanding the key size and the block size further, and impressively, with trivial increase in computational cost in implementing the cryptographic procedures.

127

128

CHAPTER 4 Fast performing key bunch matrix block cipher for large datasets Table 3 Comparison of block ciphers in ECB mode

Parameters

DES

Triple DES

AES

Blowfish

Fast dataset block cipher (proposed cipher)

Key size (binary bits)

56

128

64

2048

Block size (in binary bits) Operations used in the encryption

64

168 (for 3 different keys) 64

64

64

2048

S-box generation, subkey generation

S-box generation, subkey generation

Key expansion, addition, xor

Scalar matrix multiplications, mixbits

Number of rounds for each block Key generation time (in seconds) Time elapsed for 1 round of encryption of 1 data block (in seconds)

16

16

mix columns, shift rows, substitute bytes, add round key 10

16

16

2.4

7.2

3.71

2.41

0.6  104

1.58

3.8

6.73

0.84

0.0012

The last block of the dataset contains fewer bits than are considered block size, and then additional zeroes are added to make it a complete block. As the encrypted elements of all the blocks are out of the scope of this chapter, the first enciphered block is presented in Eq. (17).

7 CRYPTANALYSIS Cryptanalysis infers the strength of a cryptosystem to withstand attacks and plays a vital role in the development and sustainability of encryption techniques and standards. The types of attacks that are widely put into operation by attackers and observed in the literature of cryptography are the following: • • • •

Ciphertext only attack (Brute force attack) Known plaintext attack Chosen plaintext attack Chosen ciphertext attack.

7 Cryptanalysis

The block ciphers developed in this context are meant to preserve the confidentiality of the dataset contents at rest as well as during transmission over public networks. As the data is principally converted to the CSV format and then enciphered, the two attacks, known plaintext as well as chosen ciphertext, can be leveled to the encrypted contents. Mainly, the cipher needs to be designed to sustain the first two attacks. The theoretical proofs for the ciphertext only and known plaintext attacks are presented in [35]. However, intuitive analysis can be presented for the latter two attacks. For enforcing a ciphertext only attack, the attacker holds the encrypted data and tries to decode it. In addition, the enciphering procedure is also known. To speculate on the original dataset or a part of it, the brute force technique is the widely accepted one to ascertain the key. In this analysis, the size of the Enc_Key is the square matrix of size n with each of its elements lying in the interval [1–255]. The number of ways for each integer chosen would then be 128. Thus the space of the Enc_Key matrix remains as follows:  0:7n2 2 2 2 128n ¼ 27n ¼ 210  102:1n

On assuming the time required to execute the cipher as 107 seconds for each value of the Enc_Key matrix, the total time needed for the computation of all keys is the key space range, which would be approximately and unassumingly equal to 102:1n  107 2 ¼ 3:12  102:1n 15 years: 365  24  60  60 2

With the n value considered in the illustration as lower than 4, the preceding expression converges to 3.12  1018.6 years. As the time required is convincingly large, a ciphertext-only attack using the brute force approach is not viable. A further increase in the value of n would escalate the time required to crack the cryptosystem. In the known-plaintext only attack, the attacker knows both the original dataset contents (called a crib), although limitedly, and its corresponding enciphered data. The task remains to uncover the entire original dataset, which can be derived only after identifying the secret key. For analyzing the strength of the developed cipher to counter the known-plaintext attack, it’s assumed that the attacker is holding many pairs of dataset records and the corresponding enciphered data, either at rest or during the transmission. On confinement to one round of iteration in the encryption procedure for the analysis, the sequence of subtasks manipulating the input elements and the binary bits can be listed as follows:
 P ¼ eij  pij mod 256, i ¼ 1 to n, j ¼ 1 to n,

(20)

P ¼ mixBitsðPÞ, and

(21)

C¼P

(22)

The elements of the matrix P, appearing on the right side in Eq. (20), are known to an extent of n2. The corresponding fully encrypted data C in Eq. (22) is also known.

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CHAPTER 4 Fast performing key bunch matrix block cipher for large datasets Given the context that the algorithmic procedures are publicly known, the individual elements of the matrix P, on the right side of Eq. (21), are obtained from the inverseMixBits(). After mapping the P elements on the left side and the raw P elements of the right side in Eq. (20), one can deduce the individual keys in the Enc_Key matrix using the multiplicative inverses. Thus the cipher is breakable in 1 round. However, if the number of rounds considered is 2, then the following equations hold true:
 P ¼ eij  pij mod 256 i ¼ 1 to n, j ¼ 1 to n,

(23)

P ¼ mixBitsðPÞ,

(24)


 P ¼ eij xpij mod 256 i ¼ 1 to n, j ¼ 1 to n,

(25)

P ¼ mixBitsðPÞ,and

(26)

C¼P

(27)

In this case, the elements of the C matrix, occurring on the left side of Eq. (27), are known, and so are those of the P matrix, on the right side of Eq. (26). Using inverseMixBits() on the binary form of P, the elements of the P matrix, present on the right of Eq. (26), can be obtained. Although the P matrix on the right side of Eq. (23) is known, the P matrix of the left side cannot be computed as the Enc_Key elements are unknown. One cannot proceed further beyond this and this results in nonconvergence from Eqs. (23) to (25). Thus the cipher is not breakable with 2 rounds. In the present analysis, the itr value adopted is 16, and consequently the cipher can withstand the known-plaintext attack. In either of the chosen text attack models, the attacker is assumed to choose a few random elements of either plaintext or ciphertext and their corresponding data after encryption/decryption procedures. The goal is to obtain further insight into the confidential information shared across. In the present analysis of the dataset block cipher, even after fully using intuition, there is no scope for generating correct dataset/encrypted data. Conclusively, the cipher under consideration is unbreakable by any of the previously listed attacks.

8 CONCLUSIONS AND FUTURE SCOPE In this chapter, development of a block cipher is discussed for securing large datasets by means of a bunch of keys along with their corresponding multiplicative inverses. The cipher performs faster, even on legacy machines with lower configurations. The block and key size can be further expanded with trivial changes in the computational cost. In the cryptanalysis, it is observed that this fast-performing block cipher is stronger, as the dataset elements are thoroughly influenced in every cycle of the iterative method by the keys.

References

In this analysis, each character of the dataset is modified using modular multiplication with a key and undergoes several transformations in the iteration process, resulting in enciphered elements that cannot be broken in any cryptanalytic attack. Analogous to the Hill Cipher [36], this cryptosystem can be efficiently used for securing large datasets, embedded with images as well, during transmission as well as at rest, with fast-performing key bunch matrix modular operations. Beforehand, the images must be digitized in appropriate formats.

REFERENCES [1] The Codasyl Model, J.S. Knowles, D.M.R. Bell, P.M. Stocker, P.M.D. Gray, M.P. Atkinson (Eds.), Databases—Role and Structure, CUP, University of Minnesota, Minneapolis, 1984. [2] E.F. Codd, A relational model of data for large shared data banks, Commun. ACM 13 (6) (1970) 377–387. [3] D.D. Chamberlin, A history and evaluation of system R, Commun. ACM 24 (10) (1981) 632–646. [4] E.F. Codd, in: A data base sublanguage founded on the relational calculus, Proceeding SIGFIDET ’71 proceedings of the ACM SIGFIDET (now SIGMOD) workshop on data description, Access and Control, 1971, pp. 35–68. [5] E.F. Codd, in: A data base sublanguage founded on the relational calculus, SIGFIDET ’71 Proceedings of the ACM SIGFIDET, Workshop on Data Description, Access and Control, 1971. [6] How to hack unbreakable Oracle. The Register. February 7, 2002 [7] G.G. Henry, Introduction to IBM System/38 Architecture, in: IBM System/38 Technical Developments (PDF). IBM Product Design and Development, General Systems Division, 1978, pp. 3–6. [8] AOL Employee Arrested and Charged with Stealing List—June 23, 2004. http://cnn.com [9] Ex-AOL Worker Who Stole e-Mail List Sentenced, http://msnbc.com [10] LulzSec on Hacks: We Find it Entertaining. PCMAG [11] Vijayan, Jaikumar “Programmer who stole drive containing 1 million bank records gets 42 months”, 26 March 2008, Computerworld [12] Arthur, Charles. Security Leak Leaves US Apple iPad Owners at Risk. The Guardian [13] NHS Researchers Lose Laptop with 8m Patient Records. TechWeekEurope UK [14] “50,000,000 Usernames and Passwords Lost As LivingSocial “Special Offers” Site Hacked—Naked Security”. Naked Security [15] 8 million Leaked Passwords Connected to LinkedIn, Dating Website. Ars Technica [16] Arthur, Charles. Apple Developer Site Hack: Turkish Security Researcher Claims Responsibility. (The Guardian) [17] 91,000 state Medicaid clients warned of data breach, February 9 2016, The Seattle Times [18] J.P. Morgan Chase Probing Possible Data Breach, 1 May 2007, PCWorld [19] http://www.anthemfacts.com/faq. [20] K. Tumulty, T. Hamburger, WikiLeaks Releases Thousands of Documents about Clinton and Internal Deliberations, Washington Post, July 22, 2016. [21] 2017 Cost of Data Breach Study: Global Overview Ponemon Institute, June 2017

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CHAPTER 4 Fast performing key bunch matrix block cipher for large datasets [22] Inmon, Bill, Encryption at rest—information management magazine article. InformationWeek Retrieved 26 December 2012 [23] Differences between Whole Database and Column Encryption. www.netlib.com. Retrieved 02 November 2015 [24] D. Suciu, Technical perspective: SQL on an encrypted database, 2012. Association for Computing Machinery. Commun. ACM. [25] D.L. Spooner, E. Gudes, A unifying approach to the design of a secure database operating system, IEEE Trans. Softw. Eng. 10 (3) (1984) 310–319. [26] Database Encryption in SQL Server 2008 Enterprise Edition. Technet.microsoft.com. Retrieved 03 November 2015 [27] Application Encryption from Thales e-Security. www.thales-esecurity.com. Retrieved 25 October 2015 [28] SANS Institute InfoSec Whitepaper, Regulations and Standards: Where Encryption Applies, November, 2007. [29] “Transparent Data Encryption with Azure SQL Database”. msdn.microsoft.com. Retrieved 2015-11-04 [30] Advanced encryption standard (AES). Federal Information Processing Standards Publication 197. United States National Institute of Standards and Technology (NIST). 26 November 2001 [31] W. Diffie, M.E. Hellman, Exhaustive cryptanalysis of the NBS data encryption standard, Computer 10 (6) (1977) 74–84. [32] Triple DES Encryption. IBM. Retrieved 17 May 2010. [33] D.V.U.K. Sastry, K. Shirisha, A novel block cipher involving a key bunch matrix, in: International Journal of Computer Applications (IJCA) (0975–8887), Vol. 55, Foundation of Computer Science, New York, 2012, pp. 1–6 No. 16, October. [34] https://catalog.data.gov/dataset/demographic-statistics-by-zip-code-acfc9/resource/ e43f1938-3c4a-4501-9aaf-46891bb21553. [35] William Stallings, Cryptography and network security: Principle and practices, third ed., 2003, 29 (Chapter 2). [36] L. Hill, Cryptography in an algebraic alphabet, Am. Math. Mon. 36 (6) (1929) 306–312.

FURTHER READING [37] Eoin Higgins, https://www.pastemagazine.com/articles/2016/12/the-clinton-campaignsvoter-demographic-data-serve.html, December 15, 2016.

CHAPTER

5

Comparative analysis of semantic frameworks in healthcare

Pinal Shah, Amit Thakkar IT Department, CSPIT, Charotar University of Science And Technology (CHARUSAT), Changa, India

CHAPTER OUTLINE 1 Introduction .......................................................................................................134 2 Background Work ...............................................................................................136 2.1 Data, Information, and Knowledge .........................................................136 2.2 Semantic Web Overview .......................................................................137 2.3 Linked Data Principles .........................................................................138 2.4 RDF: Healthcare Information Representation ..........................................138 2.5 SPARQL (SPARQL Protocol and RDF Query Language) ............................139 2.6 Role of RDF and SPARQL in Semantic Healthcare ..................................140 2.7 Ontology .............................................................................................140 2.8 Introduction to Multiagent Systems in Semantic Healthcare ....................141 3 Healthcare Semantic Frameworks and Software ...................................................142 3.1 Healthcare Semantic Frameworks .........................................................142 3.2 Role of Existing Semantic Software in Healthcare ...................................146 4 Research Issues .................................................................................................147 4.1 Current Research Challenges ................................................................147 4.2 Existing Information Retrieval Methods in Semantic Web ........................148 4.3 Interoperability in Healthcare ................................................................149 4.4 Comparison of Existing Frameworks .......................................................149 4.5 Proposed Framework ............................................................................149 4.6 Implementation of Multiagent System ...................................................151 5 Conclusion ........................................................................................................152 References ...........................................................................................................152 Further Reading ....................................................................................................154

Healthcare Data Analytics and Management. https://doi.org/10.1016/B978-0-12-815368-0.00005-1 # 2019 Elsevier Inc. All rights reserved.

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Scope of Chapter: In the recent years many users have surfed the online web documents for the health related information. This indicates the high demand of putting updated and high quality data of healthcare which can be accessible to patients as well as many healthcare stack holders. This chapter seeks to help satisfy that demand by analyzing health data and designing framework based on semantic methods powered by open source technologies JADE, WADE, RDF and ontologies. Framework discussed in this chapter accept the data from the various healthcare standards like HL7 (Healthcare Level 7) and FHIR (Fast Healthcare Interoperability Resource). Initially framework convert the data to the standard format RDF (Resource Description Framework) using syntactic transformation methods and finally by applying semantic transformation you can analyze and find the accurate meaning of data. Apart from frameworks, this chapter also discusses the various software tools like PACS (picture archiving and communication system), DICOM (Digital Imaging Communicating Medicine) and RIS (Radiology Information system). These tools are used to change the traditional healthcare practice of maintaining all the records within file and some hard storage with the high risk of stolen data or crashing of hard data by converting and managing everything digitally. So the main aim of this chapter is to analyze the data effectively for taking automatic decision regarding patient’s health risk level whether the patient is at low risk, moderate risk or high risk.

1 INTRODUCTION More than seven thousand languages are spoken around the globe [1]. Despite the fact that individuals speak distinctive languages, they find a way to communicate by either agreeing on the same language or by utilizing a translator. This basic concept is known as interoperability. Broadly speaking, we must have a common platform or system through which every individual or organization is able to work together to achieve a common objective [2]. This concept plays a very important role in our daily lives as well. It was initially defined in the information technology (IT) field for exchanging or sharing information [3]. We can take the example of the World Wide Web, which is a large interoperable network of documents whose standards are defined by the World Wide Web Consortium (W3C) [4]. Similarly this concept is applicable in healthcare, where a need exists to develop a semantic-based framework and a multiagent system to retrieve and exchange information among the different hospitals and medical institutions. According to a survey made by the World Health Organization (WHO) in India, only 5% of individual doctors and 12% of hospitals are participating in the exchange of health information (Fig. 1). These numbers are very small compared to other countries like the United States and the United Kingdom. To achieve interoperability is the biggest challenge in healthcare, because every hospital stores data in its own format and to communicate between each electronic health record (EHR), we must have a common system or framework that allows us to retrieve meaningful patient information in a secure

1 Introduction

Disease burden

20 %

Health workers

9%

Doctors

8%

8%

Nurses

6%

Beds

1%

Lab technician

FIG. 1 Healthcare industries in India—2017.

manner. Meaningful use of healthcare data is achieved from three components [5]: (1) using a licensed EHR; (2) using certified EHR technology for exchange of health information; (3) using certified EHR technology to submit clinical quality measures. Here components 2 and 3 require interoperability between EHRs and that is where the multiagent system plays a pivotal role. System and device interoperability in healthcare could save over Rs. 30,000 crore in India ($4.49 billion US). But achieving interoperability in healthcare is a significantly challenging process. The current healthcare system in India contains complex data, either in structured or unstructured formats, because medical data are available in many layers and may be generated from diverse devices. This causes inefficiencies in data interoperability, so sometimes physicians are not able to interpret the exchanged data. The same holds true for computer software. The demographic dividend in India, which we all hailed as a great opportunity for development, soon vanished because of a simple fact: a majority of Indians who are 40 + years old suffer from cardiac diseases, diabetes, communicable diseases, and cancer. Our tradition does not emphasize nor encourage active physical exercise and healthy lifestyles. The following section discusses the digitization changes in healthcare and how semantics plays an important role in healthcare. The emergence of advanced imaging in the previous 25 years has changed medical services. Advances, for example, in computerized tomography (CT) scans and magnetic resonance imaging (MRI) have drastically changed the way doctors analyze and treat illnesses [6]. Innovative developments in picture archiving and communication systems (PACS) allow most radiological information to be stored safely and then retrieved within the following couple of years [7]. Likewise, we are seeing the development of advanced imaging archives in numerous specialties, for example, pathology,

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cardiology, and dermatology. The later advancements in useful imaging modalities, for example, functional MRI and positron emission tomography (PET), combined with programming to create three-dimensional reproductions instead of the common two-dimensional image collections, means that these advances will likely turn into fundamental clinical diagnosis tools. Future clinical data frameworks should be able to deal with these heterogeneous imaging informational indexes, as they will probably become part of continuing care. Overseeing extensive amounts of imaging information is a real difficulty that confronts the creators of mixed media medical record frameworks. Database models must be created that can flawlessly incorporate both the textual and imaging parts of medical records. Digital Imaging and Communications in Medicine (DICOM) is an initial move towards digitization and institutionalization of medical imaging information configurations and it has been received as the data interchange standard for biomedical work [8]. After gathering information from the DICOM system, we can apply different semantic-based methods to fetch meaningful data on patients and provide better care for society [9]. According to startup data tracker, India’s highest funded healthcare startups in 2017 were focused mainly on appointment booking and cloud-based healthcare information systems (like PACS and DICOM), with very few startups focusing on cutting-edge research, such as remote patient monitoring and semantic information retrieval. This chapter provides an overview of semantic frameworks, multiagent systems, and different information retrieving methods and how they all fit into the Indian healthcare industry. This is just the beginning; with thoughtful innovations and correct use of technology, the healthcare industry is set to be a major source of growth for the Indian economy.

2 BACKGROUND WORK 2.1 DATA, INFORMATION, AND KNOWLEDGE In Ref. [10] the author described three keywords: (1) Data: the facts about and description of the real world; (2) Information: captured data in any format; and (3) Knowledge: a personal map or model of the world information (see Fig. 2). In this sense, in a healthcare domain, data are the facts about the patient’s personal situation or health-related information. For instance, patient personal information like gender, name, and date of birth represents data. They are true, whether or not they are recorded somewhere. When this data is captured in some format, it becomes information that people or machines can understand and access at different times. For example, when the nurse records the patient’s data during a hospital visit, it becomes information. Therefore any sort of database (a storage-related concept) is information that can be accessible and understood. In this case, the birthday record of a patient is information, but the actual birthdate of the patient is data/fact.

2 Background Work

Information Capture data and knowledge

Data

Knowledge Our map of the world

FIG. 2 Data, information, and knowledge.

The patient record (information) could be transferred to other data sources, or it could be modified. But modifying the patient information does not change the original data. The knowledge is generated based on the data, information, past experience, and creative mind-mapping model. Based on knowledge, our processing engine, the human brain, is able to make decisions by connecting various information together.

2.2 SEMANTIC WEB OVERVIEW Going back in history, back in 1945, Vannevar Bush proposed a device called the Memex, for memory extension, that would allow us to do external management of links and to assist us in recalling the documents and the links we made. Twenty years later, Ted Nelson proposes to use computers to implement that idea, using hypertext and hypermedia structures, linking documents and parts of documents together. At the end of the 1980s, Tim Berners-Lee reused that idea and extended it, merging it with the Internet, and the Web was born, mainly with three components: (I) a URL that allows us to identify and locate the document on the Web; (II) HTTP (Communication Protocol) that allows the client to connect to the server; (III) HTML that describes the web pages on the web. There was a change at the end of the 1990s, instigated by Tim Berners-Lee, in the way we use and perceive addresses on the Web (Fig. 3). Instead of a URL he introduced the concept of URI and IRI to locate and identify anything on the Web. Thus the Web is transforming from pages to the resource. Only one thing remains unchanged in URL, URI, and IRI, and that is the R (for resource). The resource is any real-world entity that can be described on the Web (Fig. 4).

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CHAPTER 5 Comparative analysis of semantic frameworks in healthcare

HTTP

HTML

RDF

URL

SPARQL

URI

FIG. 3 Evolution of the Web.

URL URI IRI

• IDENTIFY WHAT EXIST ON WEB • IDENTIFY ON THE WEB, WHAT EXIST • IDENTIFY ON WEB IN ANY LANGUAGE

FIG. 4 From pages to resource.

2.3 LINKED DATA PRINCIPLES Tim Berners-Lee has suggested following five main principles for linked open data [11]: • • • • •

Data must be available on Web in any format. Data must be in machine-readable format (Scanned images are not allowed). Only nonproprietary format allowed for the data. Data must be published using RDF and SPARQL (Standard defined by W3C). All data should have proper link to get connection with other linked data.

2.4 RDF: HEALTHCARE INFORMATION REPRESENTATION To implement the concept of the Semantic Web, the Resource Description Framework (RDF) standard was born at the end of the last century. At that time, the URI standard existed to identify resources on the Web, and the HTTP standard to access resources. The only standard that was missing was that to describe resources for machines, and it was the invention of the RDF data and graph model that allowed the creation of the Web of Data. RDF follows a triple format for storing data and facts as shown in Fig. 5. Composition rules for defining RDF files are: • • •

Subject must be resource (not literals) Property must be identified by URI Object may be a literals or resource.

2 Background Work

FIG. 5 RDF triple data model.

First, a triple is a statement in “subject/predicate/object” form. All the objects are connected to another object or literals through the predicate. For example, a relation is a collection of triples: if a column in your table has the value “ABC” and a row has the value “has Sister” and the value in the cell is, for example, “XYZ,” then here you have a triple: ABC (subject) has Sister (predicate) XYZ (literal/object). RDF triples are special, as every part of the triple has a URI associated with it, so an everyday statement like “Pinal Shah knows Amit Thakkar” might be represented in RDF as: uri://people#pinalshah12 http://xmlns.com/foaf/0.1/knows uri://people#amitthakkar45. The analogy to the spreadsheet is that by giving every part of the URI a unique address, you could in principle put every cell (if expressed in RDF triples) in the spreadsheet into a different document on a different server, etc., and reconstitute the spreadsheet through a single query. RDF 1.1 comes with seven syntaxes: N-Triples, Turtle, RDF/XML, RDFa, JSON-LD, TriG, N-Quads, and out of all these the Turtle syntax is the most widely used to represent and store data using RDF. Is it possible to depict healthcare data entities having diverse formats in a single flexible form? In this sense, RDF [12] has gained notoriety in recent years, because it is schema-less and is a commonly acknowledged framework of the Semantic Web [13] community. It is also proposed as a universal healthcare information representation in Ref. [14]. RDF makes statements about resources in the form of (subject, predicate, object) expressions, which are known as triples. In this work we utilize RDF representation of healthcare data. Both instance data and data models used to organize the instance data are represented in RDF. Healthcare data in different systems are linked through the relations of RDF triples, ending up with an extensively connected graph.

2.5 SPARQL (SPARQL PROTOCOL AND RDF QUERY LANGUAGE) The SPARQL query language is part of a set of recommendation frameworks of the Web of Linked Data and it enables us to query RDF triple sources published on the Web. It is totally different from SQL, which enables users to query a relational database. In Fig. 6 you can see the standard syntax and format to write a SPARQL query.

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FIG. 6 SPARQL query format.

SPARQL has a select/from/where syntax, that looks like the SQL language. In the select clause we can specify what we want to get for the result. In the “from” part, which is optional, we specify the focus graph that we want to query, and the “where” part is the body of the query; this is where we write the graph pattern of the SPARQL query. The basic statements in SPARQL are triple patterns in the Turtle syntax.

2.6 ROLE OF RDF AND SPARQL IN SEMANTIC HEALTHCARE Syntax and semantics are autonomous, independent systems. Syntactical processing is independent of semantic (and pragmatic) processing (Fig. 7). For example, say we have two different healthcare systems, HL7 (Healthcare Level 7) and FHIR (Fast Healthcare Interoperability Resource), and data are coming from them both. The first step is to convert their own data formats to the RDF format (Standard Healthcare Format) using the syntactic transformation method, which extracts various grammars and words from the language and information. The next step is to convert that to RDF format using semantic transformation methods, which require identifying and finding the meaningful information from the language found through syntactic transformation. Once you have all the data within the receiver, you can fire any SPARQL-based query to fetch information or knowledge. Thus in Semantic Web healthcare, RDF and SPARQL play an important role for conversion of data and fetching of meaningful information. Within the hospitals and medical institutions, enormous amounts of data are generated from heterogeneous devices, so the first step is to provide better methods for researchers, through access to this data available on the Web. The next step is to use powerful applications to effectively query the data and there SPARQL comes into the picture. SPARQL is used to bridge between the healthcare communities and the Semantic Web communities for handling the challenges of accessing the data from the data source [15].

2.7 ONTOLOGY The history of computer science shows that information is crucial for intelligent systems. In several cases, higher information is more necessary for determination of a task than higher algorithms. To achieve truly intelligent systems, information must

2 Background Work

FIG. 7 Syntactic transformation vs semantic transformation.

be captured, processed, reused, and communicated. Ontologies support all these tasks. The ontology describes knowledge, and each agent has its own ontology (knowledge), so when an agent wants to communicate to another agent, ontologies must be shared between agents. Ontologies play an important role in achieving interoperability across different healthcare standards of organizations. Because it provides a common vocabulary and grammar for publishing data, it can also be used to establish a semantic description of the data. Ontology is considered the backbone of the Semantic Web [16].

2.8 INTRODUCTION TO MULTIAGENT SYSTEMS IN SEMANTIC HEALTHCARE Fig. 8 portrays a little bit of this ontological Web [17]. The little boxes speak to agents or other Web assets that utilize the terms in Web ontologies, spoken to by the bigger boxes. The arrows speak to any instrument that gives a mapping (full or incomplete), starting with one ontology then onto the next. The figure demonstrates one DAG (directed acyclic graph) that could be taken from the substantially bigger Web of ontologies. Accepting agents speak with each other, utilizing the terms in these ontologies. In the case where all agents are associated, and if the ontology determines that a specific class has a specific property and that the property has

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FIG. 8 Concept of multiagent system.

some confinement, at that point every specialist can expect that alternate has lawful esteems for that property keeping up that limitation. What is all the more fascinating, agents that are not utilizing similar ontologies may in any case have the capacity to impart. On account of this, we should rethink the DAG in Fig. 8. Unmistakably, huge numbers of these agents might discover at any rate a few terms that they could impart to others. For example, for those pointing at ontologies C and E, the terms they offer may be a type of subset. For this situation the agent at E may have the capacity to utilize just a portion of the terms in C (those that were not essentially changed when E was characterized). Different agents, for example the ones pointing at F and G, may share incomplete terms from another ontology that they both changed (D in this case). This is how mapping works in a Semantic Web environment multiagent-based system. The possibilities are endless for researchers to fetch and extract meaningful information while bringing agents to the Semantic Web [18].

3 HEALTHCARE SEMANTIC FRAMEWORKS AND SOFTWARE 3.1 HEALTHCARE SEMANTIC FRAMEWORKS 3.1.1 Agent and ontology-based information sharing Agent and Ontology-based Information Sharing (AOIS) attempts to couple the features of the multiagent system and peer-to-peer system. Authors have proposed this framework by considering BitTorrent as a peer-to-peer system that supports the sharing of information among different users connected through the Internet. They have used semantic-based ontology search techniques to fetch and retrieve the data instead of normal search techniques. [19] AOIS is the extended version of many systems like DIAMS, ACP2P, and CinemaScreen. DIAMS is a multiagent system that allows the user to work collaboratively with the Web to share, manage, and access information. It supports the

3 Healthcare semantic frameworks and software

PA—Personal assistant - receives the user's query -forward to available information finder -maintain updated information record IF—Information finder -searches information in repository -finds the appropriate results and filter them based on user's demand and policy

RM—repository manager -maintain index for searching information -each time RM update the index when user remove or add new inforamtion

IP—Information pusher - monitors the changes in repository -pushes the new information to PA

DF—Directory facilitator -register new agent platform in AOIS network -PA can ask about active agent's address in the network

FIG. 9 Different agents of AOIS system.

searching and retrieving operation on a local repository as well as on remote repositories. ACP2P (Agent community based peer to peer) is an information retrieval system that supports the community in identifying the right agent based on user queries and the agent’s past experience. CinemaScreen is a recommender system with two types of filtering functionality, collaborative filtering and content-based filtering. Collaborative filtering is used to match the users based on similar kinds of behavior, while content-based filtering is used to match the user’s characteristics. AOIS is known as a multiagent system because it is composed of five different agents connected through the internet (Fig. 9): (1) Personal Assistant (PA); (2) Repository Manager (RM); (3) Information Finder (IF); (4) Information Pusher (IP); and (5) Directory Facilitator (DF). The AOIS system is driven by creating a search index and creating an ontology for each topic presented during the exchange of information among the users. Creation of a search index and the ontology are both tasks performed by the RM, automatically based on the information stored in the local repository. The search index allows the ranking of information on the basis of the terms contained in a query. The ontology allows identifying additional information on the basis of the terms contained in the ontology that have semantic relationships (i.e., synonyms) with the terms contained in the query. The AOIS system has been implemented using five components: JADE [20], BitTorrent DHT [21], Nutch [22], WordNet [23], and JAWS [24].

3.1.2 Statistics and collaborative knowledge exchange Statistics and Collaborative Knowledge Exchange (SCKE) is a lightweight and easyto-use system implemented in the JADE environment that focuses on patients and diseases, allowing information to be exchanged with and accessed by other hospitals in the loop via a secure connection. While the information is accessed, it assumes that each agent represents a user in the system and provides information using semantic

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Agent 1 Hospital 1

Agent 2 Hospital 2

Agent 3 Hospital 3

Agent n Hospital n Database

Role of each agent in the system: 1. Each agent can interact with others using Message Passing 2. To search data first agent fires query in local hospital database. 3. If data is not available then search data in another hospitals database.

Ontology for the disease

FIG. 10 SCKE framework/multiagent system.

search. The system’s main components are Protege for ontology development and the Nutch plug-in for interconnecting users through a secure connection [25]. The author introduces a framework (Fig. 10) that allows for exchange of knowledge and information between hospitals and uses semantic search methods to make the search results more accurate and meaningful. The implemented framework consists of the different hospital agents and a local hospital database, in which each agent represents a hospital. The communication between two hospitals is accomplished by a message-passing system using the Java runtime environment [26]. Hospital agents are responsible for three main functions: • • •

Interaction with other agents. Accepting the hospital agent’s retrieval query. Searching for the query in the database; and then returning the retrieval results to the hospital’s agent.

Searching for queries in this type of system is semantic in nature. So it deals with linked data and knowledge representation by using ontology and helps to provide more meaningful information and the most relevant results from a database. But the main issue with this framework is not supporting a file-sharing application among each agent. This system is capable of transferring messages only in an asynchronous manner.

3.1.3 Multiagent semantic-driven framework Epidemiological applications are very complex in today’s world, as data are coming from diversified fields, and they also involve different types of diseases and different types of publishers and formats. This will result in inability of one dataset to interconnect with another dataset, because they rarely have a single standard format. To solve this problem authors have presented the multiagent-based semantic driven (MASE) approach [27]. Every year government announces a budget for rural healthcare development based on the area and the people living there. In deciding the budget amount, they

3 Healthcare semantic frameworks and software

have to rely on manual data collection systems coming from various government offices. Instead of a manual process, MASE provides automation in deciding the approximate budget based on the location and groups of people. For example, if a government officer wants to analyze last year’s outbreak of dengue or swine flu fever, then MASE will help decision makers allocate funds and resources for the coming years. This system has five agents: User, User Interface, MASE Agent, Service Discovery agent, and Composition planner agent. The general workflow of this system involves various sequential events: • • •

• •

All the agents are created automatically by the system. A subset of expert’s knowledge was already fed into the system. When the system receives a message about a user specification, the system will locate the semantic service and prepare the discovered service for service composition. When the service discovers agent finishes the work, it updates the user interface with available services. The composition planner agent is responsible for checking the consistency of data exchange in the system.

3.1.4 MET4 Most healthcare organizations, to maintain their workflow, have appointed an interdisciplinary healthcare team (IHT). To provide support to that team, the author has suggested the MET4 framework, or multiagent system. This framework is mostly used to select the most responsible practitioners or physicians for the assigned workflow task to the IHT. The system is implemented using mainly two components: WADE, a multiagent platform, and Z3 solver, a theorem prover/model finder. The MET4 framework is mostly focused on chronic kidney disease [28]. The suggested MET4 system requires new semantic components and the author has presented all semantic components on first-order logic (FOL). This allows for scalable modeling and is used to control the clinical workflow. Following are the semantic components introduced by the author: (1) a revised ontology describing concepts and relations to manage patients and (2) rules for governance of IHT. One of the important concepts the authors have touched on is role blurring, which means exchanging the responsibility and professional roles of team members due to shared knowledge and skills. This results in both positive and negative outcomes, a positive outcome being for example shared workload and a negative outcome could be anxiety of team members. In this chapter all semantic components are presented in FOL. For instance, to decide the best physician they have defined two rules in FOL. The main contribution of this chapter is to introduce new semantic components in the existing MET4 framework for managing clinical workflow in a smooth manner.

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3.2 ROLE OF EXISTING SEMANTIC SOFTWARE IN HEALTHCARE We are mainly focusing on interoperability issues, where data are coming from heterogeneous devices and in enormous amounts, making it difficult to achieve interoperability. Let’s narrow down our discussion to one of the core fields of healthcare, e.g., radiology, where large amounts of data are generated in different formats and where maximum digitization is required for storing patient records efficiently (Fig. 11). We are lucky enough to find some good software to help the doctor and his team manage everything digitally. In the following section we discuss some of the important tools [29].

3.2.1 PACS As described earlier, a PACS is used to store high-definition 2D images along with 3D graphics. Radiologists use this system to store all diagnostic images like X-rays, CAT scans, and MRI images, using local servers or cloud platforms, which provide an even more convenient way for sharing images on the network [29]. The main purpose of a PACS is to make it much easier to manage all the images that are needed to monitor the status of a patient going through treatment and recovery. In the traditional system of storing hard copies of images, retrieval by healthcare employees takes too much time and also there is a possibility that images will be

Patient registration

Review by doctor

FIG. 11 Radiology workflow [29].

Imaging order

Storage archiving

Order scheduling

Examine

4 Research issues

misfiled or misplaced. The new system is better as it provides improved and faster service to the patients.

3.2.2 Radiology information system A Radiology Information System (RIS) maintains all the documents and appointment-related activities at the fingertips of the healthcare personnel. Without such a system, radiology team members spend time keeping track of patients’ progress reports, finding historical patient data, and managing records of diagnostic images on file. RIS is a software solution for hospital staff members for managing all the operations mentioned. Once all the data is fed into the RIS system, it becomes very easy to share this information among doctors and consultants. Another benefit that many people have not considered is that you can use an RIS to see if a patient is due to come in for an appointment, as this affects the patient’s ongoing recovery [30].

3.2.3 DICOM DICOM is the system used by most radiologists. As previously discussed, this system helps hospital teams to perform routine tasks easily, such as comparing the latest patient’s report with a previous report. Without needing to worry about data and image standards, this system shares the information with many stakeholders. In basic streamlining of the hospital workflow, DICOM plays an important role [31]. Key points regarding PACS and DICOM: •

You can use PACS to manage and store different images like X-rays and CAT scans, while DICOM provides a standard for medical professionals to share and exchange information between different systems made by different manufacturers.

4 RESEARCH ISSUES 4.1 CURRENT RESEARCH CHALLENGES Semantic data can provide benefits to health organizations, clinics, medical institutions, and society in general, but surveys reveal certain loopholes and challenges in the use of Semantic Web technologies in the healthcare domain. In order to achieve maximum productivity in the healthcare domain from Semantic Web technologies, a number of barriers need to be addressed: •

•

A large amount of data is being produced by health organizations on a daily basis and all that data continue to be generated in proprietary formats that may be accurate and complete, or inaccurate and inconsistent. There are some strong mapping tools required to convert such data from local formats to a standard format, to achieve the best and most relevant result. The biggest problem in the healthcare domain is the availability of multiple ontologies for the same domain. Reaching a common and standard ontology is the

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•

•

challenge for any developer. For example, several ontologies are available for the cancer domain, but it is very difficult to choose a standardized and accurate one. Efforts are required to standardize vocabularies within specific domains. Data will be updated and described continuously using different vocabularies. No single ontology is enough to describe heterogeneous data. Various strategies and tools are required to integrate different ontologies. Integration of data mining techniques with Semantic Web technologies will bring many opportunities for developers to discover hidden patterns and knowledge in large healthcare datasets. The performance of mining techniques depends on the format of the underlying data generated from semantic frameworks; therefore the quality of the data is a serious challenge, because it can be an issue of a patient’s life or death.

4.2 EXISTING INFORMATION RETRIEVAL METHODS IN SEMANTIC WEB To address research challenges in the Semantic Web, we have various information retrieval methods available. Conventional data recovery for the most part is based on the recurrence of words in records (content mining), while semantic inquiry can be characterized as a search procedure that endeavors to achieve area learning and uses formal semantics in any of the three phases of a pursuit procedure: query, search process, or information presentation [32, 33]. Using area information and semantics helps in being focused on results from a user’s particular query. There are various approaches available for information retrieval in order to exploit domain knowledge and some of the approaches are discussed in this section. 1. Graph traversal approach This approach describes a process to use various search algorithms for traversing the graph. A graph can be used to express the domain knowledge. In this method we need to calculate the weight of the graph edge, which describes the relevance to a specific query or domain. The shortest path algorithm performs the best to identify the relation between two nodes in the graph. “MinG” and “sRelation” are the two most recent research graph traversal approaches to extract the correlation between entities in the semantic network. 2. Query expansion As the name suggests, this technique is used to manipulate and rewrite the extended version of a user query by adding synonyms or hyponyms. Different procedures are stemming and spelling remedy. An example investigative approach is [25], where query keywords are stretched out by connecting Linked Open Data. With regards to the semantic structure, query expansion can fulfill the prerequisites for mining heterogeneous records by settling spelling mistakes in client queries and by including equivalent words.

4 Research issues

3. Spread activation Spread activation broadens the graph traversal approach. In this algorithm approach, do not just think about weights on the edges but also the number of incoming connections, so as to discover relationships among documents. In Ref. [34] for instance, the authors propose an ontology-based vector space model that demonstrates the spread activation technique with specific end goal to expand the user queries. Inside the semantic system, a spread activation approach can be used to find similarities among documents of terms used in the queries.

4.3 INTEROPERABILITY IN HEALTHCARE Interoperability can be of two types: standard interoperability and translation interoperability. Many different standards exist in healthcare; each is developed to fulfill a different purpose. In Ref. [35], the authors discuss the challenges in implementing universal standards, the complexity of the standards, diverse use cases, and evolution of the standards. These hurdles cause standardization efforts to consume significant amounts of time and the systems relying on the standards also need to be updated along with the standardization. Consequently, a proficient route for translation between different data models is more practical for information interoperability. Given the fact that it is unrealistic to have one universal standard that fits all the use cases, is it conceivable to implement standard information translation? We think the answer is “yes.” Our work is based on the technical side of healthcare information translation. We propose translation-oriented methods, metrics, algorithms, and frameworks that assist in healthcare information interoperability.

4.4 COMPARISON OF EXISTING FRAMEWORKS See Table 1.

4.5 PROPOSED FRAMEWORK Fig. 12 shows the proposed framework to deal with ubiquitous healthcare (uhealthcare) services. This framework is used to measure the patient risk level, whether the patient is low risk, moderate risk, or high risk [36]. In the following the important parts of the framework are discussed: [36] •

Context manager: Receives the data from the various hospitals and medical institutions in different data formats. Then it is the responsibility of the context manager to convert data into high-level context by matching different syntactic transformation rules with predictive semantic ontologies. Thus the role of the context manager is to convert any healthcare data format to standard healthcare data format, i.e., RDF. Once the context manager finds the data in standard RDF format, the second step is to convert it into a meaningful format (RDF) by adding semantic transformation methods. (See Fig. 7 for syntactic and semantic transformation.)

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Table 1 Comparison of existing frameworks System

Platform

Framework

Singlemulti

AIOS

Java

Jade

Multi

SCKE

Java

Jade

Multi

MASE

Java

Jade

Multi

MET4

Java

Wade

Multi

Cancer Search Engine

RDF

HTML

Single

Issue Use of BitTorrent platform for communication, which has legal issue in many countries The system does not support file sharing among the agents Mostly focuses on geographical data for making decisions regarding patient’s health It provides support for kidney disease only It takes more time for generating responses regarding patient’s health as the system depends on neural networks

Proposed framework 1

2

3

4

5

6

Repository system

1

Context manager

3

RDF 2

Property matching

Targeted hospital ontology mapping

Standard interoperability

4 Semantic measure

5

7

RDF 6

Process parser Measure patient’s risk level

Process executor

Translation engine 9 RDF

Process manager

FIG. 12 Proposed semantic framework.

8

4 Research issues

•

•

Process manager: This module is used to select the appropriate u-healthcare services from the repository system based on the patient risk level. Once the service has been identified, the executor executes it for the patient. Repository system: Contains the complete information regarding the process, such as input and output variables, preconditions and effects variables, functional definition, URIs, and QoS parameters.

The authors have used a low-level dataset (captured from biosensors) for the experiments in this case, but they have not used any high-level context information coming from the different hospitals as an input to this system, so accuracy may differ, or there could be a problem of interoperability when selecting input parameters from a different system. Following are various data mining techniques used in healthcare for the solution of interoperability [37]: • •

• • •

K-NN Classification. Used to diagnosis heart disease on Cleveland Heart Disease Dataset with and without voting. K-NN achieves better accuracy without voting. Improved fuzzy k-nearest classifier using particle swarm optimization. For diagnosing thyroid disease. The author claims that this is the best approach among its competitive approaches. Integrated decision tree model. For skin disease and is also used for predicting breast cancer in patients. Neural Network Classification. Used for lung cancer, asthma, and chest disease. Bayesian classification. It is widely used for analyzing health risks.

4.6 IMPLEMENTATION OF MULTIAGENT SYSTEM Ideally, to implement any multiagent or semantic-based framework three components are required: (1) Protege for ontologies; (2) JADE or WADE Multiagent Platform; (3) Apache Nutch plug-in for retrieving data efficiently. Protege: Protege is an open source framework to build semantic-based intelligent systems that are freely available. Protege has strong community support from researchers and corporate users developing knowledge-based solutions in the areas of healthcare and e-commerce [38]. JADE: JADE works as a middleware for developing multiagent systems. It contains various libraries for programmers and graphics tools for administration, and monitors the activities of running agents. Besides the basic features illustrated in this tutorial, JADE provides a number of advanced features such as support for complex interaction protocols and usage of user-defined ontologies [39]. You can also use an extended version of JADE, i.e., WADE (Workflow and Agent Development Environment) platform, with some advanced features.

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5 CONCLUSION This chapter has presented various healthcare semantic-based frameworks and multiagent systems for sharing and accessing information between users, doctors, and hospitals. We first discussed the two types of interoperability, standard and translation interoperability. Given the fact that it is next to impossible to impose a standard framework around the globe for the healthcare industry as a whole and to serve all the use cases, we suggest going with the translation type of interoperability between different models. In this regard, we have discussed the semantic approach and standard format to represent healthcare information in RDF. RDF is the most flexible data model for healthcare, to perform translations between different data elements. We then presented background information on the Semantic Web, SPARQL, and Linked Data related to healthcare, and on several multiagent systems, namely, AOIS, SCKE, and MET4. The framework includes a metadata repository, a user-friendly interface, and a translation engine. The metadata repository stores the data dictionaries, which include data model details for classes, concepts, concept values, and their associations to each other. The interface allows data managers to view and manage the data dictionary elements and the mappings between them. The defined translation framework includes a number of steps required to perform translations to support heterogeneous data interoperability. However, a manual approach may be timeconsuming and costly due to the size of healthcare data; therefore we have introduced the concept of using mining techniques along with the Semantic Web concept to bring automation to the process and improve the quality of data. Sometimes the medical industry has been slow to adopt technology because it requires precise information and they do not want to take any chances regarding the lives of their patients. In order to achieve a solution, the different semantic frameworks have been discussed in this chapter. Out of this entire framework and system discussion, the proposed framework (extended version of SCKE), the multiagent system, performed best on medical cognate data to provide more precise and germane results.

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[26] S. Li, W.A. Mackaness, A multi-agent-based, semantic-driven system for decision support in epidemic management, Health Informatics J. 21 (3) (2015) 195–208. [27] S. Li, W.A. Mackaness, A multi-agent-based, semantic-driven system for decision support in epidemic management, Health Informatics J. 21 (3) (2014) 195–208. [28] S. Wilk, et al., Using semantic components to represent dynamics of an Interdisciplinary healthcare team in a multi-agent decision support system, J. Med. Syst. 40 (2) (2015). [29] E.L. Siegel, B. Reiner, Work flow redesign: the key to success when using PACS, J. Digit. Imaging 16 (1) (2003) 164–168. [30] S.S. Boochever, HIS/RIS/PACS integration: getting to the gold standard, Radiol Manage 26 (3) (2004) 16–24. [31] P. Mildenberger, M. Eichelberg, E. Martin, Introduction to the DICOM standard, Eur. Radiol. 12 (4) (2002) 920–927. [32] M. Hildebrand, J.R. van Ossenbruggen, L. Hardman, An Analysis of Search-Based User Interaction on the Semantic Web, 2007. [33] C. Mangold, A survey and classification of semantic search approaches, Int. J. Metadata Semant. Ontol. 2 (1) (2007) 23–34. [34] V.M. Ngo, T.H. Cao, T.M. Le, in: Combining named entities with wordnet and using query-oriented spreading activation for semantic text search, 2010 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2010, pp. 1–6. IEEE. [35] D. Booth, C. Dowling, D. Michel, J. Mandel, C. Nanjo, R. Richards, The Yosemite project. A roadmap for healthcare information interoperability, Semantic Technology and Business Conference. San Jose, California USA, 2014. [36] M. Echeverrı´a, J.-M. Angel, S.A. Rı´os, A semantic framework for continuous u-health services provisioning, Proc. Comput. Sci. 60 (2015) 603–612. [37] X. Zenuni, et al., State of the art of semantic web for healthcare, Proc. Social Behavioral Sci. 195 (2015) 1990–1998. [38] Protege, Free open source ontology editor, http://protege.stanford.edu/. [39] G. Caire, Jade Tutorial, (2002) (Application-Defined Content Languages and Ontologies).

FURTHER READING [40] Siren, S. (9 2009). "Using Nutch with Solr". Lucidworks.com. Retrieved 18 January 2016.

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Smart ambulance system using concept of big data and internet of things

6

Ankur Dumka*, Anushree Sah† Graphic Era University, Dehradun, India* University of Petroleum & Energy Studies, Dehradun, India†

CHAPTER OUTLINE 1 Introduction .......................................................................................................155 1.1 Remote Health Care .............................................................................156 1.2 Medical History Data ............................................................................157 1.3 Telemedicine ......................................................................................157 2 Techniques and Technologies .............................................................................159 2.1 Internet of Things ................................................................................159 2.2 Big Data .............................................................................................160 2.3 Cloud Computing .................................................................................161 2.4 Wireless Body Access Network ..............................................................163 2.5 Case Study ..........................................................................................165 3 Proposed Design ................................................................................................166 3.1 Technicalities of Smart Ambulance .......................................................168 3.2 Conclusions ........................................................................................172 4 Results ..............................................................................................................174 References ............................................................................................................175 Further Reading .....................................................................................................176

1 INTRODUCTION Information technology (IT) is used for storing, retrieving, transmitting, and manipulating data by means of computers and telecommunication systems. Information technology in today’s world is used extensively in a wide range of fields, including medical science, where it is termed health information technology (HIT). Inclusion of HIT in ambulances is one of the technological achievements that changes the way in which patients are treated within the ambulance. Healthcare Data Analytics and Management. https://doi.org/10.1016/B978-0-12-815368-0.00006-3 # 2019 Elsevier Inc. All rights reserved.

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The need and demand for emergency medical response systems has created a push to develop more technology-based smart and efficient solutions for these systems. In view of this, we are proposing a technology-based smart ambulance system. The proposed design uses technology such as wireless body sensor networks (WBANs), Internet of Things (IoT), big data analytics, and artificial intelligence (AI) to provide: 1. Remote healthcare 2. Medical history data 3. Telemedicine

1.1 REMOTE HEALTH CARE With the growing demand for healthcare services, the traditional diagnosis services are becoming insufficient to meet the needs. E-health is one of the services that provides cost-efficient and 24  7 diagnosis tools. Remote mobile health monitoring is one of the innovations towards these initiatives. Monitoring health records on a realtime basis provides continuous detection of any patient issues. Remote healthcare can be used to improve a patient’s quality of life by reducing the burden of the medical system and the cost of public health, through reduced travel, physician and laboratory time, and greater patient peace of mind [1]. Jovanov [2] in his research implemented a remote healthcare system using sensor technology and short-distance personal area networks (PANs). Mamaghanian et al. [3] in their research have proposed the acquisition of MEMS-based gyroscopes, integrated front-end for electrocardiogram (ECG), and accelerometers. These are sensorbased systems used to provide vital signs and physiologic activities. Spinsante and Gambi [4] in their research focused on wireless and home-centered health monitoring systems, used for efficient management of medical devices. This research was based on the Open Services Gateway initiative (OSGi) framework. Kulkarni and € urk [5] provided a software framework and a common protocol for wireless infraOzt€ structure for disaster scenarios. The proposed system will provide wireless monitoring and tracking of patients. There is another project termed as Mobi Health [6], which is used for continuous monitoring of patient outside the hospital and for improving quality of life with services like the diagnosis of disease, remote assistance, monitoring of physical state and clinical research. In recent years there are a variety of health care monitoring systems that have been proposed. A remote human pulse monitoring web-based system with intelligent data analytics is presented by Chen [7] in his research. This system adopts human pulse, PDA, wireless communication and World Wide Web for daily home health care. The system provides a remote healthcare treatment system for observing pulse rate and heart rate of the patient using a convenient web-based user interface. Redondi et al. [8] presented a research idea for an indoor patient and provided a device for tracking and monitoring of patients using wireless sensor networks.

1 Introduction

1.2 MEDICAL HISTORY DATA The concept of computerization of patient medical records has existed for many years, but only in the past few decades has it become widely adopted. The computerization of patient medical records is called electronic health record (EHR), and it is used for keeping up with patient data digitally rather than in the traditional way with handwritten notes and typed reports in a paper file system. The EHR has been accepted in many countries in varying proportions. Canada in 2001 started an initiative for modernization of healthcare facilities through information and communications technology (ICT). Canada created electronic health records (EHRs) for 91% of the Canadian people in 2015. In 2014, the number of clinics using EHR services was 62,000, which rose to 91,000 in 2015. Thus within a year, a large increase in EHRs was accomplished using ICT technology in health services. England in the year 2001 started a national initiative termed the “National Plan for IT” (NPf1T) for modernization of the healthcare system. This plan aimed to create summary health records (SHRs) for nearly 54 million persons, which amounts to nearly 96% of the total population of England. In Germany, around 90% of the physicians in private practice are using an EHR system for their healthcare records. Security concerns have been emphasized, while giving patients the ability to block or hide any information in their health records. New Zealand adopted an EHR system for 97% of its total population. For accessing EHR systems from any entity across New Zealand, a distributed system is currently used rather than a centralized EHR system, with the near-term goal of installing a centralized EHR system. In India, the level of IT usage in health services is minimal as compared to other countries. Private hospitals are implementing integrated ICT-based technology for better health management. Max healthcare hospital in India began to implement an EHR system for patients from 2009 onwards and achieved level 6 of the EMR adoption model used by HIMSS for assessment of the level of adoption of EMR systems in any hospital [1]. The Apollo group has also implemented level 6 of EMR adoption for four of its hospitals located in Chennai, Nandanam, Aynambakkam, and Jublie Hills. Sankara Nethralaya (SN) implemented EMR services in its hospitals and satellite clinic in Chennai [3]. This hospital uses the Tata consultancy for implementation of this service. Together with TCS, SN provides EMR services to other hospitals as well.

1.3 TELEMEDICINE Telemedicine uses IT for health applications and services to bridge the communication gap between doctor and patient. Telemedicine uses several information technologies for better application of traditional health services [9]. Telemedicine can be used in services such as teleradiology, teleconsultation, tele-ICU, telenursing,

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telesurgery, etc. Each of these services has its own advantages and challenges regarding real-time applications, in order to operate efficiently and legally. Taking the example of clinical medicine, telemedicine is one of the rapidly developing applications in this area. Telemedicine can be used to perform examinations and remote medical procedures [10], and it uses advanced communication technology to provide assistance in techniques of dialogue, correspondence, etc. The areas of patient care, public health diagnosis, training, research, health information sending and receiving [11], X-ray analysis, and health professional education [12] can all fall under the telemedicine heading.Criteria for evaluation of internet health records are as follows: • • • • • •

•

Reliability—the source of information and its relevance is subject to editorial review. Content—the data should be accurate and complete in itself. Disclosure—to make the user aware of the purpose of a website, or the collection of information associated with the website. Links—includes evaluation based on selection, content, and architecture and back linkages. Design—encompasses accessibility, navigability, and searching of internet capability. Interactivity—focuses on how interactive the data is. By exchanging information between users and the feedback process, data can be made more and more interactive. Caveats—meant to clarify whether the website function is to market products and services or whether it is a primary information provider.

The advantages of the system described in this chapter are: 1. Real-time monitoring of patients in the ambulance: Monitoring of patients during ambulance travel can be done easily with the smart ambulance. Doctors will be prepared in advance for the patient, as all the important measures like blood sugar, ECG report, blood group, other blood reports, heart rate, etc., can be checked and sent to the doctor before reaching the hospital. 2. Easy access to patient information: Patient records are easily stored, managed, and accessed over the internet from the database in the cloud. 3. Processed information for a patient anywhere and any time for future requirements. 4. Secure information: Information present and uploaded is saved in a secure environment with proper security protocol and requiring proper credentials to access it. 5. Easy and stable accessibility of stretchers and instruments: Loading and unloading of patients will be safer, easier, and more comfortable. 6. Specific segmentation: Segmentation of patients allows for efficient administrative decisions based on locality, type of disease, and gender.

2 Techniques and technologies

2 TECHNIQUES AND TECHNOLOGIES The proposed design discussed in this chapter uses multiple ICT-based technologies to create a smart ambulance, which will provide a real-time rapid response system for the patient. The techniques and technologies used in the smart ambulance system are as follows: 1. 2. 3. 4. 5.

Internet of Things Big data technology The Cloud Wireless body access networks (WBANs) Artificial intelligence

2.1 INTERNET OF THINGS The IoT has brought about a revolution in IT and in the way we all see science, and IT has changed drastically with the evolution of the IoT. Various technologies like WSN, microservices, microelectromechanical systems (MEMS), frameworks, World Wide Web, the internet, etc. have allowed the IoT to evolve. IT and operational technology have helped in analyzing data and hence are improving predictions in many fields. IoT can be said to be the network of devices (physical), home appliances, smart cities, smart grids, agriculture, medical appliances, intelligent transport systems, virtual power plants, or any other thing that is embedded or enabled with sensors, electronic devices, software, actuators, a network, or the internet, and which aid these devices or objects to share or connect or exchange information. All of these “things” are embedded with a unique device having a unique identity and which can communicate over the network with other devices. These IoT-enabled devices reduce human interaction and increase the effectiveness of the system.

2.1.1 Roadmap of IoT in technology In early 2000, RFID tags were used for intelligent transportation and routing and are often considered a precursor of the IoT. The IoT was used for loss detection and prevention in its early stages; then with its acceptance and increased usage it began to be applied into various other fields such as healthcare, food safety, security management, etc. Nowadays, IoT is further used in locating everyday objects and people, and with more and more advances in technology and acceptance of IoT, it will be used in monitoring and controlling distant objects; i.e., it can be used for teleoperations or telepresence (also called ubiquitous positioning). Further, the future of IoT is bright; it will eventually be used in almost all aspects of our life to make our lives easier. In recent years, the growth of IoT has significantly increased and with the amount of money being spent on this technology, innovations in this space are bound to happen.

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2.1.2 Applications of IoT • • • • • • •

Smart healthcare management Smart home automation Energy management Agriculture In search and rescue operations (used by defense or firefighters for their field operations) Media infrastructure management Environmental monitoring

2.1.3 Challenges of IoT Over the next few years, IoT will reduce dependency on monitoring devices by humans and hence reduce the chances of human error. IoT devices will provide early diagnosis and prevention of any problem that was difficult to find with only human interaction. IoT devices are continuously improving the IT industry, but still there are IoT adoption barriers, such as: • • • • • • • •

Privacy and security concerns Lack of interoperability and unclear value propositions Healthcare privacy IT integration and cybersecurity Connectivity challenges Compatibility and longevity challenges Adoption of intelligent analytics within the IoT Policy enforcement

The IoT will be used in the proposed system for information transfer from/to the ambulance to/from the main station for quick response time for the patient.

2.2 BIG DATA Big data, as its name indicates, is a collection of large and complex data or datasets, which are very difficult to handle using traditional data processing systems or traditional database management tools. These large and complex data suffer from the challenges of capturing, curation, searching, sharing, transferring, analysis, and visualization. Large amounts of patient data have been collected, from X-ray to blood testing reports. Electronic storage and processing of these data can make patient healthcare easier and more efficient. With the introduction of genomics and personalized medicine as part of the future of healthcare services, the quantity of data continues to increase manyfold and more insights from this data will be needed. Keeping in view the large amounts of data and the data processing requirements, big data will be an emerging technology that can serve this issue effectively and provide required results cost-effectively. Once the initial set-up has been completed for big data technology to store and process large amounts of patient data, then the cost for adding more patients will be

2 Techniques and technologies

substantially lower and thus the economies of scale will drive further technical developments. This technology will be very beneficial in the long run, contributing to the progress of medical science. Hadoop is a framework using simple programming models that allow the users to store and process a large amount of data in a distributed environment across a cluster of commodity computers. Hadoop uses a cluster of nodes for processing of big data in a distributed environment; it is an open source data management tool having scaleout storage and distributed processing. The simple programming model for Hadoop is called MapReduce. When working with large datasets, Hadoop reduces costly transmission steps by using distributed storing and transferring code. Hadoop provides redundancy, thus allowing recovery from a situation in case of failure of a single node. Programming in Hadoop is also easier, as it uses the easy to program MapReduce framework. Within Hadoop, the partitioning of data and allocation of the tasks to nodes, and also communication between the nodes, is done automatically. There is no need for manual allocation of these tasks, leaving the programmer free to focus on the data and the logic that needs to be implemented. The core of Hadoop consists of: (1) (2)

Hadoop Distribution File System (HDFS) Processing portion, MapReduce

The HDFS is used for data storage. It consists of a cluster of machines made up of two nodes called Namenode and Datanode, where Namenode acts as the administration node and Datanode acts as the data storage. The HDFS is natively redundant, which means that redundancy is built in, as all the data of one node is replicated to another node to avoid failures. Thus, data is stored at multiple locations to prevent data failure. MapReduce, used for data processing, is a programming model that splits tasks across processors. Hadoop breaks the files into smaller blocks of variable defined sizes and distributes the data among nodes of the cluster, where the data is parallel processed using MapReduce functions. Thus Hadoop provides a reliable shared storage and analysis system, with storage provided by HDFS and analysis carried out by MapReduce. The proposed design uses Hadoop technology for processing largescale healthcare information gathered in a fast and efficient manner to provide the desired output.

2.3 CLOUD COMPUTING The cloud is a large set of heterogeneous objects that appear as a cluster of objects from a distance. Computing within these clusters is called cloud computing. The cloud computing platform is a high-performance, emerging technology that gives users access on a pay-per-use basis. It is a mechanism to store and access data through the internet at a lower cost, with access to resources available continuously, as long as an internet connection is available. The cloud, integrated with SOA, virtualization, utility computing, grid computing, and the internet, provides us with platform, infrastructure, and services at low

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cost. This computing platform allows ubiquitous access to shared resources.Cloud computing has characteristics of different models such as peer-to-peer, client-server model, grid computing, mainframe computing, green computing, utility computing, cloud sandbox, etc. A few major characteristics of cloud computing are: • • • • • • • • • • • • •

Resource pooling Performance Cost reduction Reliability Availability Accessibility Security Scalability Migration flexibility Low maintenance Measured services Location and device independence On-demand self service

A few issues in cloud computing are: • • • • • •

Lack of security Lack of privacy Dependence on internet Cultural issues High cost of implementation Uniformity in the adoption of technology

2.3.1 Cloud in healthcare The healthcare industry is continuously evolving and so are the technologies used. Many hospitals with various legacy systems have now migrated towards digital systems such as EHR. Different vendors now offer healthcare services and solutions, for example medical imaging, patient management over the cloud, patient data management over the cloud, electronic data interchange (EDI), etc. A SWOT (Strength, Weakness, Opportunity, Threats) analysis of cloud computing for healthcare is as follows: Strengths: • •

Evolving IT in health industry Support from cloud

Weakness: • •

Lack of experts Feasibility issues

2 Techniques and technologies

Opportunities: • • •

Rapid elasticity Availability Accessibility

Threats: • • •

Lack of trust Lack of regulations Lack of education and usage.

As mentioned, many healthcare stakeholders focused on providing more cost effective and efficient services for patients are moving their healthcare services to the cloud. Cloud computing has solved the need to invest in various IT infrastructure, like hardware, computing resources, etc., and it improves sharing by means of virtualization. Most healthcare organizations are modernizing their infrastructure and the way they are dealing with their patients. Cloud computing, IoT, AI, and big data are jointly providing better solutions.

2.4 WIRELESS BODY ACCESS NETWORK Wireless body access network (WBAN) is an IEEE standard 802.15.6 protocol that is used to disseminate the information of body parts to remote locations [13]. WBAN is a low power, short range, and extremely reliable communicating medium for the human body. Data rates vary from 75.9 kbps (Narrowband) up to 15.6 Mbps (ultraband). The type of WBAN chosen depends upon its usage. WBAN technology can interact with the internet and many wireless technologies like Bluetooth, wireless local area networks (WLANs), ZigBee, WSNs, wireless personal area network (WPAN), cellular networks, and video surveillance systems. WBANs transform the view of people in how they interact and are benefited by IT. WBAN sensors can sample, monitor, process, and communicate various vital signs and help in providing real-time feedback to the user and medical personnel conveniently. WBANs can be used for continuous monitoring of patient physiological parameters and thus provide greater mobility and flexibility to the patient. Thus, we can say that WBAN is used to provide security, easy access, privacy, real-time data, and compatibility of patient data [14]. WBAN architecture consists of nodes defined as an independent device that has interaction capabilities. The nodes can be categorized into three groups based on their implementation, role, and functionality, as follows: 1. Implant node 2. Body surface node 3. External node The implant node is a node or sensors planted inside the human body, either immediately underneath the skin or inside body tissue. The body surface node is placed on

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the surface or 2 cm away from the patient’s body. The external node is placed from a few centimeters to approximately 5 m away from the patient’s body. Sensors are the devices that collect data based on bodily stimuli. Sensors are used to sense certain parameters of the patient’s body; the parameters can be internal or external, depending upon the need. These sensors can be ambient sensors, physiological sensors, or biokinetics sensors. Actuators receive data from sensors and then interact with the user. They provide feedback in the network by acting on sensor data. They can be used in applications like pumping of a correct medicine dose to the patient’s body, etc. A personal device is used for collecting information received from sensors and actuators and is used for handling of interaction with users. This is also known as a body gateway, sink, body control unit (BCU), or PDA in different application types [15]. Nodes can be classified in WBANs depending on their roles in networks in the following ways: 1. Coordinator 2. End nodes 3. Relay The coordinator node acts as a gateway to a WBAN network for the outside world, another WBAN, the access coordinator, or a trust center. It can be a PDA on the WBAN network used for communication with all other nodes. End nodes are used for the embedded application. These nodes cannot be used for relaying messages from other nodes. Relay nodes are intermediate nodes used for sensing of data. These nodes have a parent node, child node, and relay messages. If any node is a foot node or node at an extremity, then any data sent must be relayed by other nodes before reaching the PDA for display to the patient. The number of nodes within a WBSN network may range from just a few actuators or sensors up to tens to thousands of actuators or sensors communicating with the gateway to the internet. The maximum number of nodes supported by a WBSN network is scalable up to 256 nodes in a network within an area of 3 m3 [16]. Only 1 hub may exist in a WBAN supporting up to 64 nodes within its area, due to its limited transmission strategy. Within a person/patient, two to four WBAN networks can coexist within a square meter [13], thus supporting a maximum of 256 nodes within a network. In respect to addressing allocation, a one-octet WBAN identifier (WBAN ID) is utilized for allocating an abbreviated address to a node, hub, or WBAN in its frame exchanges [17]. The value of this octet ranges between x00 and xFF (0–255). In terms of security of data using WBAN technology, we can say that data is very secure within a WBAN network, as WBAN provides a security mechanism by means of secure management for encryption/decryption of data, availability of data, data authentication by sharing a secret key, integrity of data by data authentication protocols, and data freshness to ensure that data is not reused and its frames are in order.

2 Techniques and technologies

There are different types of routing protocols used in WBAN networks for communication of patient data. These routing protocols can be classified into five groups based on their network structure, locations, temperature, layer, and quality of service (QoS) metric as follows: 1. Cluster-based algorithm: This algorithm divides WBAN nodes into different clusters and assigns a cluster head for each cluster. Data is routed from sensors to sink through these cluster heads. Thus this protocol reduces the number of direct transmissions from sensor to the sink, but has the drawback of overhead and delay. 2. Probabilistic algorithm: This algorithm works based on link state information and finds the best path based on minimum cost derived from link state information. This algorithm periodically updates its cost function based on link state information. The drawback with this algorithm is the requirement of a large number of transmissions for updating of its link state information. 3. Cross layer algorithm: This algorithm uses the concept of a spanning tree for routing of traffic within the WBSN network. This protocol provides low energy consumption, high throughput, and fixed end-to-end delay. 4. Temperature-based algorithm: This algorithm is based on the concept that wireless communication generates an electric and magnetic field, and this electromagnetic field exposure leads to radiation absorption resulting in an average rise in temperature of a body [9]. The main disadvantage of this algorithm is that they overtook network lifetime and reliability. 5. QoS-based routing algorithm: This algorithm works by providing different modules for different QoS metrics that work in coordination with each other.

2.5 CASE STUDY Computer technology was introduced into medical science beginning in the 1950s. Gustav Wagner in 1949 established the first professional organization for health informatics in Germany. During the 1960s, there were specialized university departments and informatics training centers established in Netherland, Germany, France, and Belgium. During the 1970s medical informatics research units appeared in the United States and Poland and much work towards the development of high-quality health education began in these developed countries. These developmental works focused on research, infrastructure, and the education field of healthcare services using ICT technology. South Africa: South Africa uses a real-time mobile system for fast tracking and improved medical service, called the Dokoza System. This mobile system was used initially for HIV/AIDS and tuberculosis treatment, with a plan to include many other diseases. It uses a system of SMS services and cell phone technology for information management and personal communication and transactional exchange. This system uses a regular issue sim card across any existing cell phone network. The backend system of Dokoza is integrated with the existing hospital system. The Dokoza system

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can be accessed on a real-time basis. Regarding security as a particular concern for HIV patient data, this system is highly vulnerable to unauthorized access to sensitive information. Indonesia: Indonesia implements a mobile telemedicine system in part of Sukabumi, West Java. This project covers an area of 4248 km2 with a population of about 2.3 million people. This system uses mobile telemedicine for efficient ICT-based health monitoring services. In India, many initiatives have been carried out with the goal of including ICT technologies in the field of healthcare services. In collaboration with many international organizations, progress in this respect is ongoing. Indian experts in collaboration with UK-Based Loughborough University experts developed a unique mobile based health monitoring system in 2005. This system uses mobile phone service for transmission of patient vital information such as electrocardiogram (ECG) heart signals, blood pressure, oxygen saturation, or blood glucose level to any hospitals or experts anywhere in the world. An association with IIT Delhi, AIIMS, Aligarh Muslim University, and London Kingston University are set up for further development of this system. An initiative for ICT-based healthcare service has been carried out at IIT Kanpur, called Sehat Sathi. This is a rural telemedicine system developed at Media Lab Asia at IIT Kanpur. The focus of this system is to carry medical services to remote areas of the country. This system is frontally supported and is carried out by trained nonmedical professionals, whereas the backend is supported by doctors and other health specialists. Media Lab Asia collaborated with AIIMS for use of handheld computers for healthcare data collection and planning.

3 PROPOSED DESIGN With the advances in IT, almost all industries are profiting, and so is the health industry. Health IT (Health Information System) is the analysis, design, development, testing, maintenance, and use of information systems throughout the healthcare industry. IT in healthcare reduces costs, improves care, reduces chances of errors, and improves efficiency and patient satisfaction. Ambulances have been used in emergency care with good effects for many decades. Many emergency cases can be properly treated within an hour of an accident (called the “Golden Hour”) if accurate early diagnosis and proper medical care can be provided in time. We are proposing a smart ambulance to be equipped with ICT technology to aid the ambulance staff/crew to deal with or treat the patient in a more efficient and effective way, and hence possibly improve/save the patient’s life. Ambulance staff can work with the hospital doctors/staff and can obtain on-time advice. The use of ICT technology in the ambulance will improve the quality of diagnosis made on the accident scene and provide more accurate solutions as to which hospital to take the patient to, and to which doctor. A smart ambulance can also contain a mobile laboratory, very useful in diagnosis.

3 Proposed design

WBAN

IoT data aggregation Hospital[1]

Raw data

WBAN Servers WBAN

Cloud

VM1 VM2

VMN WBAN

IoT data aggregation Hospital[N]

WBAN Servers WBAN Process Big data analytics WWW Processed data Patients

Doctors

FIG. 1 Design of smart ambulance system.

Real-time patient monitoring systems in smart ambulance is discussed and proposed in this section (Fig. 1). These systems can monitor/track vital signs, critical activities, physical and mental state, medications, etc., of the patient. Real-time patient monitoring will help in responding quickly to those situations requiring immediate attention. In the proposed system data is monitored and accessed from the patients using WBAN (a network of heterogeneous sensors connected to a hub). Each ambulance is considered as a node in the wireless network, which is connected to a centralized hospital server installed at a particular hospital, using the internet. Similarly, many ambulances of the same hospital send data about the patient to the particular hospital server. Further, all the servers of all hospitals in a particular range are connected to a cloud network and all hospital servers send their data to the centralized server at the cloud. This raw data is further processed using big data analytics and then the

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processed data/reports can be utilized by patients or doctors for further diagnosis and treatment. The WBAN checks the status of the patient and the medical history of the patient is also uploaded over the hospital server for physician reference. The proposed system also performs the following tasks: 1. Patient logs his report: A patient report or medical history can also be uploaded over the system. Patient history can be retrieved from the centralized cloud network using a unique patient ID (probably Aadhar card number). 2. Medical prescription with digital signatures: A smart ambulance can fetch any medical prescriptions with digital signatures so any medicines required enroute can be bought. 3. Information about pharmacists: An application is provided with a module that shows nearby pharmacists for better time management and traceability. 4. Future report generation and 24 × 7 access of medical record facility: Facility for customized report generation is available by means of big data analytics for doctors and patients. The 24  7 access to reports or records can lead to lower stress for patients, doctors, and families. The risk of losing the hard copy of records is also reduced, as all records are available online. 5. Risk stratification and assessment: For treatment in any hospital, it will be easier to identify risk based on prior history. Further harnessing analytics can be done to help in studying and analyzing the patient population. 6. Specific segmentation: Segmentation is based on locality, gender, time, etc., for efficient decision making by means of data analytics for taking preventive and curative actions. It is hoped that the smart ambulances will have far more potential of saving a life in emergency situations than the previously existing ambulance system. The standard of treatment and use of ICT in healthcare also gains if the concept of the smart ambulance is accepted worldwide.

3.1 TECHNICALITIES OF SMART AMBULANCE The data obtained from WBAN networks is passed on to the cloud through data aggregation using PDA devices, since there is a large amount of patient data that has been collected, from X-ray reports to blood testing. Large amounts of medical data of multiple patients can be accommodated in the cloud in electronic form

3 Proposed design

and processing of large data will be handled by means of big data analytics, using Hadoop. With the introduction of genomics and personalized medicine as part of the future of healthcare services, the quantity of data is increasing too rapidly, and more insights into these data will be needed. Hadoop will take the raw data format received from sensors and process them through its software, giving the result in a processed manner as required by the user. When designing the Hadoop distributed file system cluster, two types of nodes were created, which work in a master-worker pattern, where the node that works as a master is called Namenode and the nodes that work as workers is called Datanode. The Namenode is used for managing the file system namespace. Namenode maintains all files and directories in the file system tree and also maintain metadata for these files and directories. Two files, called namespace image and edit log, are stored persistently in the local disk and contain information about the architecture of the Hadoop. Apart from managing the file system namespace, Namenode also regulates the client access to the file and also is used to execute the file system operations like renaming, closing, and opening of files and directories. The Namenode is also known as a data node, on which all the blocks for a given file are located. The Datanode performs read-write operations on the file system as per the client request. Datanode provides various operations like deletion, creation, and replication as per the instruction of the Namenode. Datanodes are the workhorses of the file system. Datanode is based on the command from client or the Namenode store or retrieve blocks and reports back the result to the Namenode periodically with a list of blocks that they are storing. A file system cannot work without a Namenode as there would be no way to reconstruct the files from the blocks on the Datanode if lost. In order to avoid such types of situations, data backup is needed to be maintained. In order to back up the data, a secondary Namenode is installed whose core purpose is to prevent the edit log from becoming too large by periodically merging the edit log with the namespace image. Since the system requires ample CPU and memory for merging, Namenode usually runs on a separate machine. A client accesses the file system on behalf of the user by communicating with the Namenode and Datanode. It is represented that all the metadata operations occur in the Namenode. Metadata is the data that contains information about the list of files, list of blocks for each file, list of Datanodes for each block, file attributes like time, replication factor, etc., and it even stores transaction logs like the record of file creation, file deletion, etc. Thus, it can be said that Namenode contains information of overall file directory structure and place where the data block is stored. The metadata is stored in the main memory of Namenode and there is no demand-paging of FS meta-data. The Namenode thus contains only metadata, not the actual data. The client interacts with Namenode for metadata operation that is used for extracting information from Datanode which contain actual information. A client can read or write to the Datanode through a java interface or HDFS command line. The end user can get the result through the client. The client can directly read and write to the Datanode but gets information on the location of Datanode from

169

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Namenode. Racks are the physical location providing space for multiple Datanodes. Multiple racks altogether form the cluster. Datanode is a commodity hardware which is divided into blocks and each block has specific size varied from 64 MB, 128 MB. Remote procedure call (RPC) is a protocol used for communication among devices with each other. Namenode is master of the system. Namenode is a high quality and high availability machine that maintains and manages the blocks present on Datanode. Namenode is a single point of failure, so it is thus a single machine in whole cluster: if Namenode fails, there is no backup for Namenode and whole systems fail in such a case. Secondary Namenode is a solution for disaster recovery. It does not provide redundancy in case of Namenode failure, but rather secondary Namenode is used to make the backup of metadata of Namenode every hour to external drive. Thus, in case of primary Namenode fails then saved Namenode can be used to build a failed Namenode. Thus, secondary Namenode is not a hot standby for Namenode, rather it is used to connect Namenode every hour for backup Namenode [18]. Datanodes are the nodes responsible for processing read and write requests of the client. They are the slaves that usually provide actual storage and are deployed over each machine. A cheap replica can be made for Datanode and can be in any number. Datanode works as the database for files in the file system. Blocks are stored and retrieved in Datanode when they are asked by the client or Namenode to do so. The report containing the lists of blocks is then sent back to Namenode from the Datanode. The namespace of a file system is managed using the Namenode. Also, the metadata and file system tree for the files and directories are maintained and managed using Namenode. Two files named as namespace and edit log with the information of the architecture is stored in the local disk persistently. Apart from managing the file system namespace, Namenode also regulates the client access to the file and also is used to execute the file system operations like renaming, closing, and opening of files and directories. The Namenode is also known as data nodes on which all the blocks for a given files are located. The Datanode performs readwrite operations on the file system as per the client request. Datanode also performs operations like block creation, deletion, and replication as per the instruction of the Namenode. Datanodes are the workhorses of the file system. Datanode is based on the command from the client or Namenode store or retrieve blocks and reports back the result periodically to the Namenode. A file system is constructed using Namenode only, as there is no process to make the files from the blocks of the Datanode, so a file system cannot work if a Namenode is lost. In order to avoid such situations, data backup is needed to be maintained. In Fig. 2, it can be seen that all the metadata operations occur in the Namenode. Metadata is the data containing information such as lists of files, lists of blocks for each file, lists of Datanodes for each block, file attributes like time, replication factor, etc. and it even stores transaction logs, such as a record of file creations, file deletions, etc. Thus it can be said that Namenode contains information on overall file directory structure and is the place where the data block is stored. The metadata is stored in the main memory of Namenode and there is no demand-paging of FS

3 Proposed design

FIG. 2 HDFS architecture.

metadata. The Namenode thus contains only metadata, not the actual data. The clients interact with Namenode for the metadata operations used for extracting information from Datanode. A Client can read or write to Datanode through the Java interface or HDFS command line. The end user can get the result through the client. The client can directly read and write to the Datanode but gets information about the location of Datanode from Namenode. Racks are the physical location providing space for multiple Datanodes. Multiple racks together form the cluster. Datanode is a commodity hardware divided into blocks and each block has a specific size varying from 64 MB to 128 MB. Remote procedure call (RPC) is a protocol used for communication among devices. Namenode receives information from Datanode. The data received from sensors are managed in tabular format as shown: Data Send by Individual Sensors at 00:00 Hours:

Patient ID

Patient name

Date

Time

Sensors data

Type of disease

Urgency type

C00001

ABC

00102015

0000

87,569

Diabetics

Intermediate

171

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CHAPTER 6 Concept of big data and internet of things

Consolidated data from different sensors at different times is collected and merged in a single file as follows: Patient ID

Patient name

Date

Time

Sensors data

Type of disease

Urgency type

C0001

ABC

00102015

0000

7653

Intermediate

C00231 C00001 C00032

BCD CDE DCE

00102015 00102015 00102015

0010 0027 0044

65,432 87,569 2341

Heart disease Cancerous Fever Headache

High Low Low

Thus the list will be created for all the patients as per the requirements and demands of the doctor/patient and the data can be available from any location. The processed data can then be used for 24  7 access of reports. The risk of losing hard copies of records is thus reduced, as all the records are available online.

3.2 Conclusions Evolving IT has made tremendous changes in all aspects of life, and has had major influences on health services. In view of this, our proposed design introduces a smart ambulance that is IT enabled and provides a number of facilities using services such as Hadoop, cloud computing, IoT, and WBAN technology. The proposed system consists of an ambulance equipped with WBAN sensors used to detect real-time patient data. These sensors then send data to the center node or sink node through IoT technology by means of the Message Queuing Telemetry Transport (MQTT) protocol; IoT data aggregation is used to provide aggregate realtime data to doctors at remote hospitals. The raw data is collected and stored in the cloud, where various virtual machines are allocated for different software and infrastructures to store and process these data. The data gathered from different sink nodes are collected in the cloud, where they are processed as big data using Hadoop to arrive at the processed data in the forms required by doctors, administrations and patient. Hadoop provides a graphical view of the data, which can be used to see the number of patients grouped by area, disease type, and disease severity segmentation. Graph 1 shows an example of area-based segmentation of diseases, which gives an idea in graphical form of the number of patients in a particular area, which can be states, districts, or localities, as the parameters passed by the smart ambulance sensors contain area code. Hadoop technology will enable medical staff to get an idea of the number of patients with specific diseases in a specified area, which will help them in making administrative decisions for effective treatment and prevention of those diseases.

3 Proposed design

Number of patients

50 45 40 35 30

Area1

25

Area2

20 15

Area3

10

Area4

5 0 Diabetics

Influenza

H1N1

HIV

Cancer

Disease

GRAPH 1 Area-based segmentation of diseases.

70

Number of patient

60 50 Viral fever

40

Measles 30

Stomach infections Dengue

20 10 0 Month 1

Month 2

Month 3

Month 4

Month 5

GRAPH 2 Time-based segmentation of diseases.

Graph 2 shows the time-based segmentation of diseases, which could be by year, month, day, or even hour. This shows the number of patients with a particular disease in a particular locality/area in a timewise manner, by means of Hadoop MapReduce technology. This data can be used for finding the most crucial times for a particular disease during the year, which may be useful for taking preventive action for that particular disease and can help the administration to create a future action plan for subsequent years for that disease from this time analysis. Graph 3 shows the gender-based segmentation of diseases of a particular area over a given specified time duration. This data will be used to take preventive measures to overcome any gender-based solutions of specific diseases.

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60 50 Number of patient

174

40 30 Male 20 Female 10 0 Diabetics

Heart disease

H1N1

HIV

Cancer

Disease

GRAPH 3 Gender-based segmentation of diseases.

Similarly, we can find suitable segmentation graphs for the data retrieved from sensors on the smart ambulance for analytics of specified data of a patient, which can be used for taking preventive and curative measures.

4 Results The smart ambulance proposed in this chapter can be used to provide the healthcare services and management results described in the following paragraphs. Remote health care monitoring: Distant hospitals are a major issue in many developing countries. There are many cases when issues such as traffic jams lead to delays in handling patients on time. Keeping this in view, the proposed design and solution provides wireless body sensors (WBNs) to provide real-time patient information to the doctors. These sensors are connected to the patient’s body by trained personnel in the ambulance, and the sensors aggregate the data to the hospital server by means of IoT technology, where the doctor can begin treatment of the patient on a real-time basis in the ambulance using the emergency medically trained personnel and equipment in the ambulance. The proposed system is also enabled and helpful in providing remote health care management and telemedicine by means of technologies like IoT and sensor-based systems. The proposed system also supports the emergency response system, enabling doctors to treat a patient rapidly based on the data provided by sensors to the hospital’s servers.

References

Electronic medical records: Storing patient data securely and privately in electronic form is another aspect of this proposal. The data can be accessed and collected from the cloud at any time and any location by means of secure login information. This mechanism will reduce the overhead of carrying paperwork and also can be used to collect any previous details needed in terms of health problems or treatment that was done earlier. Area-based segmentation: The big data technology using Hadoop can be used to provide an area-based segmentation report of any locality, to show the impact and preview of any disease based on area in graphical format. This information can be used by authorities to take any action necessary, based on the severity of the disease in a particular locality or area. Time-based segmentation: This can be achieved using big data applications, which will give the graphical representation of disease vs. month/year/day. This graphical representation can be used to find the highest impact of disease in corresponding months/years/days, which can assist the administration in keeping watch and making appropriate decisions for handling the disease at particular times. Gender-based segmentation: This segmentation can be used to find the comparative analysis of male vs female ratio in terms of diseases, at particular times and locations.

References [1] T.V.P. Sundararajan, M.G. Sumithra, R. Maheswar, A novel smart routing protocol for remote health monitoring in medical wireless networks, J. Healthcare Eng. 5 (1) (2014) 95–122. [2] E. Jovanov, J. Price, D. Raskovic, in: Wireless personal area networks in telemedical environment, Proceedings of the IEEE EMBS International Conference on Information Technology Applications in Biomedicine, Arlington, VA, Nov. 22–27, 2000. [3] H. Mamaghanian, N. Khaled, D. Atienza, et al., Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes, IEEE Trans. Biomed. Eng. 58 (9) (2011) 2456–2466. [4] S. Spinsante, E. Gambi, Remote health monitoring by OSGi technology and digital TV integration, IEEE Trans. Consumer Electronics 58 (4) (2012) 1434–1441. € urk, Requirements and design spaces of mobile medical care, ACM [5] P. Kulkarni, Y. Ozt€ SIGMOBILE, Mobile Computing Commun. Rev. 11 (2007) 12–30. [6] J.W. Zheng, Z.B. Zhang, T.H. Wu, et al., A wearable mobihealth care system supporting real-time diagnosis and alarm, Med. Biol. Eng. Comput. 45 (9) (2007) 877–885. [7] C.-M. Chen, Web-based remote human pulse monitoring system with intelligent data analysis for home healthcare, in: 2008 IEEE Conference on Cybernetics and Intelligent Systems, 2008, pp. 636–641. [8] A. Redondi, M. Tagliasacchi, M. Cesana, L. Borsani, et al., in: LAURA-LocAlization and ubiquitous monitoring of patients for health care support, Proceedings of the 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops (PIMRC Workshops):218–222, 2010.

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[9] Whitten, P., Doolittle, G., & Mackert, M. (2004). Telehospice in Michigan: use and patient acceptance. Am. J. Hospice Palliative Med. 21(3):191–195. [PubMed] [10] R. Wootton, Telemedicine, Br. Med. J. 323 (2001) 557–560 (PMC free article] [PubMed). [11] M. Mort, C.R. May, T. Williams, Remote doctors and absent patients: acting at a distance in telemedicine? Sci. Technol. Human Values 28 (2) (2003) 274–296. [12] J.W. Turner, Telemedicine: expanding healthcare into virtual environments, in: T. L. Thompson, A.M. Dorsey, K.I. Miller, R. Parrott (Eds.), Handbook of Health Communication, Lawrence Erlbaum Associates, Inc., Mahwah, NJ, 2003, pp. 515–535. [13] IEEE 802.15-10 wireless personal area networks, July 2011. [14] M. Samaneh, A. Mehran, L. Justin, S. David, J. Abbas, Wireless body area networks: a survey, IEEE Commun. Surv. Tuts. 16 (3) (2014) 1658–1686. [15] C. Siegel, T.E. Dorner, Information technologies for active and assisted living— influences to the quality of life of an ageing society, Int. J. Med. Inform. 100 (2017) 32–45. [16] D. Lewis, IEEE p802.15.6/d0 Draft Standard for Body Area Network, 2010. [17] T. Zasowski, F. Althaus, M. Stager, A. Wittneben, G. Troster, in: UWB for noninvasive wireless body area networks: channel measurements and results, IEEE Conference on Ultra Wide Band Systems and Technologies, pp. 285–289, 2003. [18] A. Dumka, Smart metering as a service using HADOOP (SMAASH) chapter, in: Computational Intelligence Applications in Business Intelligence and Big Data Analytics, Springer, 2017. ISBN-13: 978-1498761017, ISBN-10: 1498761011, pp. 211–236.

FURTHER READING [19] L. Griebel, H. Prokosch, F. Kopcke, et al., A scoping review of cloud computing in healthcare, BMC Med. Informatics Decision Making 1 (1) (2015) 1–16. [20] L. Liu, E. Stroulia, J. Nikolaidis, A. Miguel-Cruz, A.R. Rincon, Smart homes and home health monitoring technologies for older adults: a systematic review, Int. J. Med. Inform. 91 (2016) 44–59. [21] Rawat, S. & Sah, A. (2012). An approach to enhance the software and Services of Health Care Centre, Computer Eng. Intell. Syst., IISTE, volume 3, Number 7(2222–2863). [22] N. Zhu, et al., Bridging e-health and the Internet of Things: the SPHERE project, IEEE Intell. Syst. 30 (4) (2015) 39–46.

CHAPTER

Mathematical methods of ECG data analysis

7

Mitko Gospodinov, Evgeniya Gospodinova, Galya Georgieva-Tsaneva Institute of Robotics, Bulgarian Academy of Sciences, Bulgaria

CHAPTER OUTLINE 1 Introduction .......................................................................................................177 2 Preprocessing ECG Signals .................................................................................179 3 Mathematical Methods of ECG Data Analysis of HRV .............................................181 3.1 Linear Methods ...................................................................................181 3.2 Nonlinear Methods ...............................................................................191 3.3 Poincar
e Plot .......................................................................................193 4 The Influence of Cardiovascular Disease and Obesity on HRV ...............................194 4.1 Time-Domain Analysis of HRV of Patients With Cardiovascular Disease ...............................................................................................194 4.2 Frequency-Domain Analysis of HRV of Patients With Cardiovascular Disease ...............................................................................................196 4.3 Time-Frequency Analysis of HRV of Patients With Cardiovascular Disease .........................................................................197 4.4 Nonlinear Analysis of HRV of Patients With Cardiovascular Disease ..........200 4.5 The Influence of Obesity on HRV ...........................................................201 5 Conclusion .......................................................................................................205 References ...........................................................................................................206 Further Reading ....................................................................................................208

1 INTRODUCTION Cardiovascular diseases are a very common cause of disability and death of the population in developed countries. The latest scientific research shows that cardiovascular disease can be reduced by continuous 24-h screening for early detection and prognosis of the health status aimed to reduce the complications and improve life quality. The applied research methods are based on the analysis of bioelectric activity of the heart. The electrocardiogram (ECG) is the classic method for short-term recording of electrical activity of the heart during cardio cycles (depolarization Healthcare Data Analytics and Management. https://doi.org/10.1016/B978-0-12-815368-0.00007-5 # 2019 Elsevier Inc. All rights reserved.

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and repolarization of the atria and heart chambers) from different points of the body. The ECG method is an effective means of analysis and diagnostics of the work of the heart. The ECG signal is recorded through several electrodes attached to the body of the patient. Generally, the frequency range of the ECG signal is from 0.05 to 125 Hz, with dynamic range from 1 to 10 mV [1, 2]. The typical ECG signal consists of a smooth low-amplitude P wave (due to depolarization of the atrium), a highamplitude QRS complex (formed during the reduction of normally excited heart chambers), and an average amplitude T wave (due to recovery of the chambers). The morphology of the ECG signal differs even in healthy individuals and it is unique for any individual [3]. Time periods in the ECG, also called RR intervals, are defined by the time duration from one R peak of the QRS complex to the next R peak of the next QRS complex. The change in duration of these intervals is dynamic and nonlinear, and it is known as heart rate variability in the specialized literature [4,5]. HRV is one of the important diagnostic parameters defined by the ECG and suitable for mathematical modeling and analysis of the patient’s cardiac status. The short definition of HRV is the change in the time duration measured between any two adjacent beats of the heart. For this purpose the modeling process needs continuous ECG records of all QRS complexes, detection of the intervals between QRS complexes (named R-R intervals), measurement of the instantaneous heart rate, and calculation of the variation. Measurements are based on the intervals between normal sinus abbreviations, resulting from depolarization of the sinus node, because they reflect the vegetative innervation of the heart [6]. As an important diagnostic parameter, determined by ECG, the HRV takes into account the difference between successive heart beats (RR time intervals). For a sufficiently long time interval, HRV can be used as a noninvasive method for measuring any aspects of cardiac activity. In [7] are given the measurement standards and clinical usage of the HRV methods for the evaluation of the risk in the case of different cardiovascular diseases, such as angina, heart attack, sudden cardiac death, and lifethreatening arrhythmias. HRV is a useful biomarker for autonomic nervous system (ANS) activity. The ANS regulates the activity of the internal organs, including the heart and blood vessels. It consists of two parts: sympathetic and parasympathetic. The two systems have exactly the opposite effect on the human organism. The parasympathetic nervous system generally slows down body functions, for example slows heart rate while resting and lowering blood pressure. The sympathetic nervous system does the opposite: it is turned on while the person is extremely active or in emotional stress. Under its influence, heart rate and blood pressure are rising, and heart rate variability decreases. Distortions in the work of the ANS may cause illnesses and even deaths. Heart rate (HR) changes constantly, even at rest. Rapid changes (from 2 to 6 s) are controlled by the parasympathetic, and slow changes (from 7 to 25 s) by sympathetic and parasympathetic, but studies show the sympathetic nervous system’s leading role. When parasympathetic activation is triggered by a pulse delay, the RR intervals increase and hence the HRV increases. When activating parasympathetic due to acceleration of the pulse, the RR interval is shortened, therefore HRV decreases [6].

2 Preprocessing ECG signals

The methods for modeling and analyzing the HRV signals can be distributed into two groups: linear and nonlinear. Linear methods are used for direct evaluation of HRV. They consist of methods of analysis in the time and frequency domains. The parameters in the time-domain analysis are statistical calculations of consecutive RR intervals, which are interconnected (SDNN, SDANN, pNN50, etc.). The parameters of the frequency-domain analysis are based on spectral analysis, which allows the allocation of each of many frequencies, present in the RR intervals. Research has shown that in the frequency domain, there are three discrete components: very low-frequency (VLF), low-frequency (LF), and high-frequency (HF), each associated with certain physiological factors. The quantitative dimensions of the parameters studied in linear analysis have significant clinical use because the limits of norm-pathology are known. The interest in nonlinear methods (Poincar
e plot, Rescaled Adjusted Range Statistics plot (R/S), Detrended Fluctuation Analysis (DFA)) has increased recently due to the more detailed observation of the dynamics of the fluctuations of HRV in different periods of time. The application of nonlinear methods is based on fractal and wavelet theory, allowing the discovery of new causes of fluctuations in HRV. Nonlinear analysis of HRV is useful not only to obtain comprehensive information about the physiological condition of the patient, but also it is a new scientific approach, giving a new concept of cardiac dynamics, hence allows prediction of pathological conditions. In recent years, wavelet analysis (WA) has been used with success in the processing and evaluation of ECG data. WA is applied for compression, noise reduction, detection of cardiac complex in ECG, HRV assessment, HRV simulation, etc. [8–13]. The chapter is organized as follows. Section 2 shows the preprocessing of the ECG signals. The mathematical methods for analyzing the HRV are presented in Section 3. In Section 4 the influence of cardiovascular disease and obesity on heart rate variability is demonstrated. Finally, conclusions are given in Section 5.

2 PREPROCESSING ECG SIGNALS HRV can be calculated for minutes, hours, days, and so on. In practice, it is calculated over a time period of 2 min to 2 days. HRV can be determined on ECG data obtained through the following devices:
electrocardiograph—measures short ECG records in the range of 2–30 min;
Holters—specialized monitoring devices used to record long-term ECG records of 24 h or more;
devices using a photoplethysmographic method to extract ECG records. A summary block-scheme of the processing and analysis of cardiac data is presented in Fig. 1. To obtain the HRV series of ECG signals, the following procedures need to be performed:
Decompression of input data if they are compressed.

179

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CHAPTER 7 Mathematical methods of ECG data analysis

ECG data

Input Data Electrocardiograph (5-10-30 min)

Holter (24, 48, 72 h)

Data preprocessing

QRS detection

RR interval

NN interval

Resampling

HRV data Heart Rate Variability Analysis Linear analysis

Time domain

Frequency domain

Time frequency domain

Nonlinear analysis

Fractal analysis

DFA

Poincare plot

R/S

FIG. 1 Block-scheme of processing and analysis of ECG signals.

Compression of ECG records is used to reduce the memory needed to store the data. There are different methods of compressing electrocardiographic data [8, 14].
Preprocessing of ECG data, including:  ECG artifacts minimizing. ECG records are filtered with a 50 Hz subtraction filter and with a second line linear filter;

3 Mathematical methods of ECG data analysis of HRV

 Detection of QRS complexes—with a method based on the Pan and Tompkins algorithm [15]; For the detection of QRS complexes, a second standard output (Lead II) obtained from a 12-channel electrocardiogram is used;  Generation of the RR time series (R—the highest peak in the cardiac complex);  Formation of the normal-to-normal time series (NN). Each incoming QRS complex is analyzed and classified, allowing only normal RR intervals to work. No extrasystoles and suspected RR intervals of non-sinusoidal origin are considered. These are the intervals around morphologically different from the usual QRS complexes, those that are 25% shorter and 25% longer than the median of the previous five RR intervals, and intervals shorter than 333 ms and longer from 2 s. With very high extrasystatic activity (ventricular and supraventricular) when the number of normal QRS complexes is below 60%, the results should also be accepted with a reserve as too many RR intervals are discarded.
The NN time series is interpolated (usually by cubic splines) and then downsampled at 2 Hz.
Analysis of the resulting HRV time series with the means of mathematical analysis.

3 MATHEMATICAL METHODS OF ECG DATA ANALYSIS OF HRV The mathematical methods of ECG data analysis of HRV can be grouped as follows:
Linear methods: time-domain, frequency-domain, and time-frequency domain methods.
Nonlinear methods.

3.1 LINEAR METHODS 3.1.1 Analysis of HRV in the time domain The methods for HRV analysis in the time domain include determination of statistical parameters and geometric parameters. The statistical parameters are mainly used to obtain statistical estimates. HRV time methods give possibility to calculate indices, not depending directly on the length of the cardio intervals. The parameters in the time-domain are calculations of consecutive normal-to-normal intervals. The geometric methods construct geometric shapes that are built on RR intervals. The properties of the resulting figure are examined. In this way information for HRV is obtained. The following parameters are used for time-domain HRV analysis: ➢ Statistical parameters [16, 17]

181

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– SDNN (ms)—Standard deviation of the average duration of all NN; reflects the overall variability: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  1 XN  SDNN ¼ RRi
RR i¼1 N

(1)

– SDANN (ms)—Standard deviation of the average NN in the blocks of 5 min data, to which the series is divided: SDANN ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2ffi 1 XN  RR
RR i i¼1 N

(2)

– RMSSD (ms)—The standard deviation of the intervals between successive heartbeats; reflects the activity of parasympathetic: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 1 XN
1  RMSSD ¼ RRi + 1
RRi i¼1 N
1

(3)

– SDNN index (ms)—Average of all NN for the blocks of 5 min data of the whole record: SDNN index ¼

1 XN SDNN i i¼N N

(4)

– NN50 (
)—The number of adjacent pairs of normal-to-normal intervals with a difference of >50 ms, across the record: NN50 ¼

XN i¼1

fjRRi + 1
RRi j > 50msg

(5)

– pNN50 (%)—NN50 divided by the number of normal intervals: pNN50 ¼

NN50 :100 N

(6)

– SDSD (ms)—Standard deviation of differences between adjacent N-N intervals: SDSD ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 1 XN
1  jRRi
RRi + 1 j
RRdif i¼1 N
1

Parameters used in the preceding formulas: N—number of intervals in all segments; i—index; P RR ¼ N1 Ni¼1 RRi
average of all RR intervals; RRi
mean value of RR intervals in the segment;

(7)

3 Mathematical methods of ECG data analysis of HRV P RR ¼ M1 M i¼1 RRi
mean value of all RR averages over all 5 min segments (M per number), RRdif ¼

1 XN ðjRRi
RRi + 1 jÞ: i¼i N
1

➢ Geometric parameters [16, 18]: – HRVTi (triangular index) (
)—All NN intervals are integrated and then divided by the maximum density distribution parameter: XNb HRVTi ¼

i¼1

bðti Þ

max i bðti Þ

¼

N
1 , max i bðti Þ

(8)

b—bin corresponding to time ti; Nb—number of bins. – TINN (triangular interpolation) (ms)—This parameter is calculated from the difference triangular interpolation of the maximum of the NN sample density distribution. Histograms of the distribution of the RR intervals and of the distribution of the heart rate are plotted for the determination of the geometric parameters, depending on the frequency of their occurrence (Fig. 2). Histograms of the RR intervals are made using the same bin divisions for all data used. The vertical axis shows the count of RR intervals falling into each bin (corresponding to a specific RR interval to the x-axis). The reduction in HRV, measured in the time domain, is a strong predictor of sudden cardiac death (SCD) associated with myocardial infarction, diabetes, congestive heart failure, sleep apnea, alcohol abuse, hypertension, syncope. According to Rich et al. [19], if the value of one of the parameters (SDNN) is

> > > < < = = x
x cos ω t
τ x
x sin ω t
τ j j j j 1 j¼0 j¼0 PNðωÞ ¼ 2 + XN
1 X         N
1 > > 2σ > : : ; > ; cos 2 ω tj
τ sin 2 ω tj
τ j¼0 j¼0

(19)

187

CHAPTER 7 Mathematical methods of ECG data analysis

where σ2 ¼

2 1 XN
1  xj
x
dispersion j¼0 N
1

(20)

1 XN
1 x
average value j¼0 j N

(21)

x¼

0 XN
1 τ ¼ tan


1 @

2ω

  1 sin 2ωtj  A
offset cos 2ωt j j¼0

j¼0 XN
1

(22)

j—index, t—time, xj—input data, tj—sampling times, N—number of samples, ω—angle frequency. The estimation of the spectral density is performed in absolute units (ms2): aVLF, aLF, aHF, in percent (%): pVLF, pLF, pHF and in normal units (n.u.) of LF and HF over the total spectrum: nLF, nHF. In Fig. 5 a PSD plot is shown depending on the frequency distribution. On the axis y, the spectral power density values are applied, the axis x shows frequency in the investigated ranges from 0 to 0.4 Hz. The results obtained in the different frequency ranges (ultra-low frequency, low-frequency, and high-frequency) are presented on the graph in different colors. On the figure are presented the values of spectral power density—there are low values in the high frequency area (0.15–0.4); the values in the low frequencies (0.04–0.15) are higher, and in the area of the ultra-low frequencies (0–0.04), they are the highest. The results shown are for a patient with cardiac disease. In Table 1 are given the frequency spectral components and their respective reference values, according to the recommendations of the European Cardiological and North American Electrophysiological Societies [7].

2 PSD (s2/Hz)

188

0−0.04 1.5 1

VLF (Hz)

0.04−0.15

LF (Hz)

0.15−0.4

HF (Hz)

0.5 0

FIG. 5 PSD plot.

0

0.05

0.1

0.15

0.2 0.25 Freq (Hz)

0.3

0.35

0.4

3 Mathematical methods of ECG data analysis of HRV

Table 1 Reference values of the frequency components Parameter

Unit

Reference values (mean  SD)

Total power LF power HF power LF power n. HFpower n. LF/HF

ms2 ms2 ms2 n.u. n.u. –

3466  1018 1170  416 975  203 54  4 29  3 1.5–2.0

3.1.3 Analysis of HRV in the time-frequency domain Time-frequency analysis methods allow the localization of spectral components simultaneously in the frequency and time domains. The main technologies used for time-frequency analysis by the window technique (window periodogram calculation) are FFT and continuous wavelet transformation.

Window periodogram The time series of data is divided into overlapping or nonoverlapping consecutive segments for which the spectral density is determined. Window periodogram of Burg. The time series of data under investigation is resampled and divided into blocks of equal length. Each of the blocks is given a Burg periodogram. Window periodogram of Lomb-Scargle. This method provides for the allocation of data to segments of equal duration. In nonuniformly sampled cardiac data, each segment may contain a different number of data reports. The Lomb-Scargle periodogram is applied to each of the segments.

Time-frequency analysis based on continuous wavelet transformation In recent years, wavelet analysis has often been applied to analyze the local variations of the frequency spectrum in time series. Through the time series representation in the time-frequency domain, the variability of the data studied can be more accurately determined and how this variability changes over time. Continuous wavelet transform. The method of continuous wavelet transform (CWT) is a representation of the data for all possible relocations, shrinking, and stretching of a function. The mathematical definition of the CWT for the signal s(t) is [18]: CWT ψs ða, bÞ ¼

∞ ð


∞

sðtÞψ ∗a, b ðtÞdt ¼

∞ ð


∞

 1 t
b sðtÞ: pffiffiffiffiffi :ψ ∗ dt, a jaj

(23)

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CHAPTER 7 Mathematical methods of ECG data analysis

where
ψ—mother wave;
a—parameter defining the scale of the ψ function, adjusts the width without changing the base waveform, analog of the frequency in Fourier analysis;
b—displacement parameter; gives the position of the function ψ;
t—time. where (*) indicates the complex part of the function. By altering the scale a and transforming the time index b, a graph is created giving a visual representation of variation in time of amplitude of the components in each scale. The reconstruction of the original signal s(t) [18] is: sðtÞ ¼

1 cψ

 1 t
b ψ dtda :CWT ð b, a Þ s 2 a
∞ a

ð∞ ð∞ 0

where cψ —multiplier:

ð cψ ¼ 2π

jψ^ ðωÞj2 dω < ∞ ω

(24)

(25)

where ψ^ ðωÞ
the Fourier transform of ψ(ω). The wavelet transform is suitable for studying the nonstationary HRV signal, as it provides an idea not only of the distribution of the spectral frequencies but also shows where they appear and disappear in the time axis. The wavelet method uses interpolation (with a certain frequency and most commonly with cubic spline functions), wavelet transform with a selected wavelet basis, and calculates a continuous wavelet spectrum. The mother wavelet is usually used in the time series spectral analysis as follows:
Derivate of Gaussian (DOG) is set with the formula [27]:
1m + 1 d m 
η2 =2  sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi :  m e 1 dη Г m+ 2

(26)

where m—derivative of a Gaussian wavelet; η—time parameter; for m ¼ 1, it is the WAVE wavelet (Fig. 6); for m ¼ 2, it is the Mexican hat wavelet or David Marr wavelet (Fig. 6);
Morlet wavelet [27]: 1

1η2 2

π
4 eiω0 η e

(27)

where ω0
nondimensional frequency;

η—time parameter;
Paul wavelet [27]:

2m im m! pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1
iηÞ
ðm + 1Þ , π ð2mÞ!

where m—order; η—time parameter.

(28)

3 Mathematical methods of ECG data analysis of HRV

1 1 0,5 0 −5

−3

−1

1

3

5

−6 −4

−2

0

2

4

6

−0,5 −1 Wavelet WAVE

Wavelet MHAT

FIG. 6 Graphical representation of some basic wavelets.

When the basic wavelet functions Ψ (n) are complex, the applied wavelet transform is also complex and consists of a real and imaginary part (amplitude and phase). For a given wavelet transform Wi applied on a time series ti, i ¼ 1, 2…M (M—all elements in the data), the wavelet spectrum in the local block is: jwi(t)j2. The global wavelet spectrum is calculated by [27]: M 1 X W 2 ðtÞ ¼ : jwi ðtÞj2 N i¼1

(29)

The wavelet spectrogram gives the distribution of signal power as a function of time. It provides distribution of signal energy in time area and in frequency area.

3.2 NONLINEAR METHODS 3.2.1 Fractal analysis methods In recent years, studies of various physiological signals such as electrocardiograms, electroencephalograms, magnetic resonance imaging, mammogram images, and others have shown that they have a fractal structure [28]. The analysis process of ECG is a major research interest in medical signal processing. The investigations of cardiology data show the fractal character of the processes, which are characterized by the following features:
Self-similarity—HRV cardio signal can be decomposed into smaller components, each one similar to the basic signal. The degree of self-similarity is defined by the parameter Hurst exponent (H). If its value is within the interval (0.5, 1.0) the process can be considered fractal. If the value of H is equal to 0.5, the process is ordinary Brownian motion [5]. In order to determine the value of the Hurst parameter of the fractal process a range of statistical methods can be used, generally combined into two groups:  Time-based methods, including the R/S method (rescaled adjusted range statistic), variance-time plot, index of dispersion for counts (IDC), wavelet-based method;  Frequency-based methods: Periodogram method, Whittle method.

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The fundamental analytical parameter for describing self-similarity of the structures is fractal dimension (D) [21]. This parameter can be used in the process of the analysis of ECG data for identification of the specific states and physiological functions. The higher value of the fractal dimension indicates greater irregularity of the signal. Several methods are available to determine the fractional dimension of the signals, among others are the methods proposed by Higuchi and Katz [29]. From a practical point of view, one often estimates the fractional dimension via the box-counting method [29]. The relationship between Hurst parameter and fractal dimension is defined as: D ¼ 2-H [30]. From the relationship between the fractal dimension and the Hurst parameter, the following conclusion can be drawn: When the values of the Hurst parameter H ! 0, the fractal dimension DH ! 2 and the graph of the process is a severely curved curve. When H ! 1, the fractal dimension DH ! 1 and the graph of the process approximates to a smooth line. Analyses of medical data in terms of fractal geometry indicate that the fractal dimension decreases after stress and/or trauma [29, 31]. The fractal dimension of the brain cells decreases by 20% after medium bleeding and by 40% following severe bleeding in laboratory animals (pigs) [29]. Similar results were seen in humans: the fractal dimension decreased from 1.62 to 1.56 in medium bleeding and from 1.62 to 1.41 in severe bleeding [29].

3.2.2 Detrended Fluctuation Analysis method The determination of the fractal correlation of the physiological signals is based on the detrended fluctuation analysis (DFA) method [32]. The advantage of this method over conventional methods is that it enables the correlation properties of the signal to be determined. The signal fluctuations (F) can be represented as a function of time intervals (n) by the formula: F(n) ¼ pnα, where p is a constant and α is a scaling parameter. By changing the parameter n the variation of the signal fluctuations can be studied. Linear behavior of the dependence F(n) indicates the scalable behavior of the signal. The slope of the straight line determines the value of the parameter α. If the signals are without correlation, the parameter α is in the range (0, 0.5). When α > 0.5 this is an indication for the correlation. If the value of the parameter α is equal to 1, the signal is “1/f noise”, but when the value of α is equal to 1.5 it is “usually Brownian motion.” Using the method DFA only one parameter can be defined for description of the investigated data. This method is more suitable for investigation of monofractal signals, which are homogeneous, having equal scalable characteristics, and could be described with only one value of the Hurst exponent and/or the fractal dimension for the whole signal. Refs. [21,33,34] contain information showing the values of these parameters are different in the case of investigation of groups of healthy and unhealthy patients.

3 Mathematical methods of ECG data analysis of HRV

3.2.3 R/S method The Rescaled adjusted range Statistics plot (R/S) is a method for measurement of the variability of a time series [30, 35]. The Hurst exponent (H) could be determined by the implementation of the R/S method. Normally the Hurst exponent defines the scale of the self-similarity and correlation properties of the fractional Brownian noise and the fractional Gaussian process. In the case of self-similar processes, the statistical properties do not change for different aggregation levels. The implementation of this method for the analyzed data X(n) is described by: RðnÞ=SðnÞ∞nH :

(30)

where
R(n) is the scope between the minimum and maximum accumulated values;
S(n) is the standard deviation of the analyzed data X(n);
H is the Hurst parameter. The basic steps of the R/S method for determining the value of the Hurst parameter are as follows: 1. The investigated HRV data is divided into two equal blocks, which in turn are divided into two, and so on. The division process continues until block size reaches m ¼ 8 points. 2. For each block, R(n) and S(n) are calculated. 3. A linear regression model is constructed between the dependent variable Log (R/S) and the independent variable Log(block size). The least squares method determines the regression coefficients: – β^0 is the point where the regression line crosses the ordinate; – β^1 is the slope of the regression line. ^ β^1. 4. The Hurst parameter is calculated using the following dependency: H¼

3.3 POINCAR
E PLOT The Poincar
e plot is a geometrical method to assess the dynamics of HRV [30,36,37]. This method reflects the relationship between adjacent RR intervals by building a “cloud” of points with coordinates (Ri, Ri+1), where Ri is ith RR interval. Thus, each RR interval (except for the first and the last) is involved in succession as both the abscissa and the ordinate. The distance from the center of the cloud to the beginning of the coordinate system gives the duration of the heart cycle. The distance from the bisector to the point on the left side shows how much cardiocycle is shorter than the previous one, and the distance from the bisector to the point on the right shows how much cardiocycle is longer than the previous one. The parameters of this method are the following:
The SD1 parameter is associated with rapid variations between individual heart intervals (parasympathetic effects);

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The SD2 parameter is responsible for long-term variations (sympathetic and parasympathetic effects);
The SD1/SD2 ratio is associated with the randomness of the HRV signal. The “cloud” of points is categorized for different functional states [21]:
Poincar
e plot of RR intervals of healthy subjects has the shape of a comet;
Poincar
e plot of the RR intervals of the heart failure patient is in the form of a torpedo.

4 THE INFLUENCE OF CARDIOVASCULAR DISEASE AND OBESITY ON HRV This section presents the results of the implementation of the mathematical methods of HRV analysis by software, developed for assistance of physicians in the process of cardiovascular diagnoses [38].The subjects of the analysis are patients with various cardiac diseases compared with the results of healthy controls /healthy patients/. The analysis follows the recommendations of the European Cardiological and North American Electrophysiological Society.

4.1 TIME-DOMAIN ANALYSIS OF HRV OF PATIENTS WITH CARDIOVASCULAR DISEASE The results of the time-domain analysis of the studied patient groups, including: Group 1—healthy controls, Group 2—patients with ischemic heart disease, Group 3—patients with heart failure, and Group 4—patients with syncope, are shown in Table 2. The results are presented as mean  standard deviation. The differences between the values of the parameters of the test groups were tested by the ANOVA test and were considered reliable at the level of significance p < 0.05. Among the investigated parameters, only the SDNN, SDANN, RMSSD, and pNN50 have statistical significance (p < 0.05). The graphs in Fig. 7 show the difference in the shape of the histograms in the cases of patients with cardiovascular disease and the healthy patients. Healthy controls are characterized by the central positioning of the poles in the diagram of RR intervals with location of the highest poles in the range 0.5–1.0 s. In the case of ischemic heart disease, a histogram shifting of the RR intervals is observed to the right, and the syncope shows the opposite effect (displacement to the left). In the case of heart failure, apart from the histogram shift to the left, there is a narrowing of the base. The quantitative dimensions of the parameters studied in this type of analysis have significant clinical use, because the norm-pathology [7] is known.

4 The influence of cardiovascular disease and obesity on HRV

Table 2 Time-domain analysis Group 1 (n 5 16)

Group 2 (n 5 38)

Group 3 (n 5 23)

Group 4 (n 5 15)

Parameters

mean  sd

mean  sd

mean  sd

mean  sd

P value

Mean RR (ms) Mean HR SDNN (ms) SDANN (ms) SDindex (ms) RMSSD (ms) pNN50 (%)

840.8  68.2 74.1  6.1 135.9  33.9 125.2  32.3 64.6  12.6 28.3  11.3 12.4  11.1

886.5  195.4 72.8  18.1 120.8  11.1 92.4  47.6 63.9  35.1 66.4  42.1 28.6  25.1

887.1  127.4 65.1  11.3 122.9  49.6 100.1  48.1 60.1  29.1 56.2  36.6 22.7  22.1

951.7  146.4 66.6  10.7 145.9  38.1 119.4  42.1 45.2  15.2 40.1  13.5 10.6  8.7

0.3 0.09 0.02 0.03 0.16 0.001 0.01

FIG. 7 Histograms of RR intervals in healthy subject (A), patients with ischemic heart disease (B), heart failure (C) and syncope (D).

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CHAPTER 7 Mathematical methods of ECG data analysis

4.2 FREQUENCY-DOMAIN ANALYSIS OF HRV OF PATIENTS WITH CARDIOVASCULAR DISEASE The results of the analysis in the frequency domain of the study groups of patients: Group 1—16 healthy controls, Group 2—16 patients with atrial fibrillation are shown in Fig. 8A, B. Significant correlations are observed between FFT and AR methods in the spectral analysis of patients with atrial fibrillation on the one hand and healthy controls on the other. Signal energy at low (LF) and high (HF) frequencies measured in normalized units in healthy individuals is within the range of normal and is often greater than those with disease. According to recommendations [20], the LF/HF ratio should be within the range (1.5–2) in healthy subjects. In accordance with the obtained results, this treatment is beyond the acceptable range in patients with heart disease.

FIG. 8 Frequency analysis in healthy subject (A) and patient with heart failure (B).

4 The influence of cardiovascular disease and obesity on HRV

FIG. 8—CONT’D

Objective information could be provided by the implementation of spectral analysis about the state of the sympathetic system (LF-low frequencies), the parasympathetic system (HF-high frequencies), and the relationship between them (LF/HF index). The quantitative dimensions of the parameters, studied by this type of analysis, have significant clinical use, because the norm-pathology [7] limits are known.

4.3 TIME-FREQUENCY ANALYSIS OF HRV OF PATIENTS WITH CARDIOVASCULAR DISEASE Studies have been conducted on healthy and diseased individuals using the methods described previously. Spectrograms were built using the Berg method, LombScargle method, and via wavelet analysis.

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CHAPTER 7 Mathematical methods of ECG data analysis

The spectrograms are outlined given the frequencies distribution depending on time. The dark blue color is an indication of the absence of a given frequency in the frequency spectrum. Light blue to yellow to red indicates an increase in the power of the corresponding frequency in the energy spectrum. The highest power of the frequencies is shown by dark red. Dense lines separate adjacent frequency areas. The table results (Fig. 9) show the parameters of the spectrum in ms2, in percent and in normal units for the VLF, LF, and HF. Frequency peaks are determined for each frequency range. The sympatho-vagal balance index is also calculated LF/HF. In Fig. 9 is shown a spectrogram of a healthy individual obtained via the wavelet analysis method. Signal energy is high in all three areas—VLF, LF and HF—an indicator of high HR variability. Spectral analysis by means of the three methods studied and the defined frequency characteristics calculated for a real recording of a patient with a stroke in the brain are shown in Fig. 10. The graphical results show low values of the signal energy in the studied areas via the three time-frequency methods. This is indicative of low HR variability. A table presented in Fig. 11 shows the obtained numerical values of the parameters tested for the patient with a state experienced after stroke in the brain. The ratio of low/high frequencies—indicator of sympatho-vagal balance—is very low, between 0.23 and 0.254 for the three methods studied. This parameter is outside the recommended range for healthy individuals (1.5–2.0) [7]. The tested HRV is low; low signal energy values are obtained in all tested ranges, for VLF, LF, and HF frequencies. The time-frequency spectrograms prove to be an effective means of illustrating the state of health of individuals and of graphical visualization of the patient’s disease state.

Frequency (Hz)

´10

1.000

-6

1.2

0.500

1

0.250 0.8 0.125 0.6 0.063 0.4

0.031

0.2

0.016 0.008

0 2475

2480

2485

2490

2495

2500

Time (s)

FIG. 9 Wavelet spectrogram; the investigated individual was healthy.

2505

2510

4 The influence of cardiovascular disease and obesity on HRV

FIG. 10 Time-frequency spectrogram; state experienced after stroke in the brain of a 60-year-old man. (A) Burg method. (B) Lomb-Scargle method. (C) Wavelet analysis.

FIG. 11 The table results of the patient’s spectrum studies.

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CHAPTER 7 Mathematical methods of ECG data analysis

4.4 NONLINEAR ANALYSIS OF HRV OF PATIENTS WITH CARDIOVASCULAR DISEASE 4.4.1 DFA method In Fig. 12 the graphs of the F(n) dependence of the investigated signals corresponding to the HRV for a healthy subject (the graph on the left) and a CHF patient (the graph on the right) are shown. The obtained results show that there is a significant difference in the scalable behavior between healthy and pathological cases and the result is consistent with the results published in Refs. [23, 34]. The DFA method usually is used for estimation of the fractal properties of HRV. The values of fractal exponents for healthy subjects are higher than the values for CHF patients.

4.4.2 R/S method Fig. 13 shows the graphs of the investigated signals corresponding to the HRV for a healthy subject (the graph on the left) and a CHF patient (the graph on the right). The value of the Hurst exponent of the patient with congestive heart failure is significantly higher than the healthy subject. The analytical research of the dependence between the value of the Hurst exponent and cardiac status could be a useful way for diagnosis of cardio disease.

4.4.3 Poincare
plot method The graphical presentation of HRV by using the Poincar
e plot method is shown in Fig. 14 for healthy subject and CHF patient. For the healthy subjects the shape of the graphics is like an ellipse, while the shape of the points for the CHF patients is similar to a circle. The demonstrated geometry of these plots illustrates the difference between healthy and unhealthy subjects.

FIG. 12 DFA analysis of healthy subject (A) and of CHF patient (B).

4 The influence of cardiovascular disease and obesity on HRV

FIG. 13 R/S analysis of healthy subject. (A) and of CHF patient (B).

FIG. 14 Poincar
e plot analysis of healthy subject and CHF patient.

The analytical parameters of Poincar
e plot analysis could be physiologically connected with HRV. The short-term variability of heart rate is defined by the parameter SD1. The long-term variability is defined by the parameter SD2. The described nonlinear methods could be used for diagnoses of cardiovascular diseases at an early stage as well as for determination of disease level. The developed application software for linear and nonlinear analysis of ECG signals could be used by physicians as an additional mathematical and analytical tool to assess patients’ cardiac status based on HRV data.

4.5 THE INFLUENCE OF OBESITY ON HRV Obesity is a widespread disease with an increasing incidence rate of epidemic. This fact makes it one of the most serious health problems of our modern society. Obese individuals have a significantly higher risk of cardiovascular disease, such as

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hypertension, hyperlipidemia, type 2 diabetes mellitus, and others. Several studies have shown that there is a direct correlation between obesity and cardiovascular mortality [39]. The diagnostic tool to determine whether the weight of an individual is within a healthy range relative to height is the body mass index (BMI). BMI is calculated using the following formula [40]:   BMI ¼ W=h2 kg=m2 ,

(31)

where
W is the weight in kilograms;
h is the height in meters. The World Health Organization (WHO) specifies the following BMI standards regardless of age, gender, or other individual characteristics [40]:





Underweight—BMI is