Data Analysis for Neurodegenerative Disorders (Cognitive Technologies) 9789819921539, 9789819921546, 9819921538

Table of contents :
Preface
Contents
Overview of Neurodegenerative Disorders
Overview of Neurodegenerative Disorders
1 Introduction
2 Neurodegenerative Disorders (NDDs)
2.1 Alzheimer’s Disease
2.2 Parkinson’s Disease
2.3 Huntington Disorder
2.4 Lewy Body Disease
2.5 Cerebral Aneurysm
2.6 Epilepsy
2.7 Spinocerebellar Ataxia (SCA)
2.8 Amyotrophic Lateral Sclerosis (ALS)
3 Conclusion
References
AI and Machine Learning Models for Neurodegenerative Disorders
Artificial Intelligence and Machine Learning Models for Diagnosing Neurodegenerative Disorders
1 Introduction
2 Description of Medical Examination
2.1 Brain Imaging
2.2 Clinical Tests
2.3 Biomarkers
2.4 Staging
3 Datasets for Diagnosing Neurodegenerative Disorders
3.1 Alzheimer Dataset
3.2 Parkinson Dataset
3.3 Huntington Dataset
3.4 Amyotrophic Lateral Sclerosis Dataset
4 Methodology of AI and ML Models for Diagnosing Neurodegenerative Disorder
5 AI and ML Models in Diagnosing Neurodegenerative Disorders
5.1 Convolutional Neural Network Model
5.2 Deep Learning Model
5.3 Long Short Term Memory Models
5.4 Graph Convolutional Network Model
5.5 Support Vector Machine Model
5.6 Random Forest Model
5.7 Survival Analysis Model
6 Contributions of AI and ML Models in Diagnosing Neurodegenerative Disorders
6.1 Contributions of DL Models
6.2 Contributions of CNN Models
6.3 Contributions of LSTM Models
6.4 Contributions of GCN Models
6.5 Contributions of SVM Models
6.6 Contributions of RF Models
6.7 Contributions of Hybrid Models
6.8 Contributions of Survival Analysis Models
7 Challenges and Opportunities for Diagnosing Neurodegenerative Disorders
8 Results and Discussion
9 Conclusion
References
Neurodegenerative Alzheimer’s Disease Disorders and Deep Learning Approaches
1 Introduction
2 Proposed Work
3 Results
4 Discussions and Limitations
5 Conclusion
References
Yoga Practitioners and Non-yoga Practitioners to Deal Neurodegenerative Disease in Neuro Regions
1 Introduction
2 Grey Matter Volume (GM)
2.1 White Matter Volume (WM)
2.2 Cerebral Fluid (CF)
2.3 The Free Surfer Method
3 Yoga
4 Magnetic Resonance Imaging
5 Brain Age
6 Mechanism for Cortex Measurement
6.1 Normalization of MRI Data
6.2 Noise in MRI Data
6.3 Feature Selection
7 Recent Study
8 Conclusion
References
Machine Learning Models for Alzheimer’s Disorders
Automated Electroencephalogram Temporal Lobe Signal Processing for Diagnosis of Alzheimer Disease
1 Introduction
2 Related Work
3 Methodology
4 Dataset Used in Experimentation
4.1 Details of Dataset 1
4.2 Details of Dataset 2
5 Deep Learning Model
6 Results and Discussion
7 Conclusion
References
Machine Learning Models for Alzheimer’s Disease Detection Using OASIS Data
1 Introduction
2 Related Work
3 Understanding of Data
3.1 Data
3.2 Initial Data Analysis (IDA)
3.3 Data Pre-Processing
4 Performance Evaluation
4.1 Evaluation Metric
4.2 Algorithms
5 Result Analysis
6 Conclusion and Future Directions
References
Electroencephalogram Analysis Using Convolutional Neural Networks in Order to Diagnose Alzheimer’s Disease
1 Introduction
2 Review of Literature
3 The Proposed Methodology
3.1 Extraction of Characteristics
3.2 Proposed Model
4 Results
5 Conclusion
References
Alzheimer’s Disease Diagnosis Assistance Through the Use of Deep Learning and Multimodal Feature Fusion
1 Introduction
2 Background
3 The Proposed Method
3.1 The Data Sources
3.2 Simple 3D CNN
3.3 Utilizes Three-Dimensional Multi-Scale Convolutional Neural Networks
4 Results
4.1 Identifying Differences by Comparing AD and NC
4.2 Identifying Differences Between MCI and NC Outcomes
4.3 Variations in Observed Results Between AD and MCI
4.4 Discussions and Evaluations of the Most Cutting-Edge Research Methodologies
4.5 Conceptualization in Three and Four Dimensions
5 Conclusion
References
Machine Learning Models for Alzheimer’s Disease Detection Using Medical Images
1 Introduction
1.1 Neurodegeneration
1.2 Consequences
1.3 Medical Imaging
1.4 Rise in the Use of Artificial Intelligence (AI) in Healthcare for Computer-Aided Diagnosis (CAD) Using ML and DL Based Neuroimaging
2 Alzheimer’s Disease: Causes and Effects
3 Neuro-Imaging Used in Diagnostic Approaches for Alzheimer’s Disease
3.1 Magnetic Resonance Imaging Technique (MRI)
3.2 Computed Tomography (CT)
3.3 Single-Photo Emission Computes Tomography (SPECT)
3.4 Positron Emission Tomography (PET)
4 Use of Artificial Intelligence in Computer Aided Diagnosis (CAD) Based on Machine Learning and Deep Learning Algorithms
4.1 Pre-Processing of MRI and CT Images for Machine Learning
4.2 Algorithms Based on Machine Learning
4.3 Rise of Deep Learning in CAD
5 Conclusions
References
Machine Learning Models for Diagnosing Alzheimer’s Disorders
1 Introduction
2 ML Based Medical Examination Description for Diagnosing Alzheimer Disorder
3 Alzheimer Datasets Description
4 ML Models’ Methodology for Diagnosing Alzheimer’s Disorder
5 Architecture of ML Models in Diagnosing Alzheimer’s Disorders
6 Comparison of ML Models in Diagnosing Alzheimer’s Disorders
7 Contributions of ML Models in Diagnosing Alzheimer’s Disorders
8 Challenges and Opportunities of ML Models in Diagnosing Alzheimer’s Disorders
8.1 Challenges
8.2 Opportunities
9 Results and Discussion of ML Models in Diagnosing Alzheimer’s Disorders
10 Conclusion
References
Alzheimer’s Disease Diagnosis Using MRI Images
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Dataset
3.2 Data Preprocessing
4 Results and Analysis
4.1 Effects of Multiple Channels
4.2 Convergence Analysis
5 Conclusion
References
AI Based Diagnosis of Parkinson’s Disorders
The Colossal Impact of Machine Learning Models on Parkinson’s Disorder: A Comparative Analysis
1 Introduction
2 Literature Survey
2.1 Bibliometric Analysis
2.2 Technical Review
3 Parkinson’s Disease
3.1 Source for Data Collection
3.2 About Data
4 AI Methodology for Parkinson’s Disease
5 Result and Discussion
6 Conclusion and Future Directions
References
Artificial Intelligence Based Diagnosis of Parkinson’s Disorders
1 Introduction
2 AI Based Medical Examination Description for Diagnosing Parkinson’s Disorder
3 Datasets to Diagnose Parkinson’s Disorder
4 AI Models’ Methodology for Diagnosing Parkinson’s Disorder
5 Architecture of AI Models in Diagnosing Parkinson’s Disorders
6 Comparison of AI Models in Diagnosing Parkinson’s Disorders
7 Contributions of AI Models in Diagnosing Parkinson’s Disorders
8 Challenges and Opportunities of AI Models in Diagnosing Parkinson’s Disorders
8.1 Challenges
8.2 Opportunities
9 Applications of AI Models in Diagnosing Parkinson’s Disorders
10 Results and Discussion of AI Models in Diagnosing Parkinson’s Disorders
11 Conclusion
References
An Artificial Intelligence Based Effective Diagnosis of Parkinson Disease Using EEG Signal
1 Introduction
1.1 Disease of the Nervous System
1.2 Bio-signal Consequence
1.3 Symptoms
2 Related Work Done
3 The Objective of the Research Work
4 The Proposed Work
5 Result and Analysis
6 Conclusion
References
Conclusions and Future Perspectives for Automated Neurodegenerative Disorders Diagnosis
Future Perspectives for Automated Neurodegenerative Disorders Diagnosis: Challenges and Possible Research Directions
1 Introduction
2 Current AI and ML Models for Neurodegenerative Disease Diagnosis
3 Strengths of AI and Machine Learning Models for Neurodegenerative Disorder Diagnosis
4 Limitations of AI and Machine Learning Models for Neurodegenerative Disorder Diagnosis
5 Challenges
6 Recent Developments
7 Need for Research and Development in the Field
8 Future Research Directions
9 Conclusion
References