Title Data analysis for neurodegenerative disorders / Deepika Koundal, Deepak Kumar Jain, Yanhui Guo, Amira S. Ashour, Atef Zaguia, editors

Published Singapore : Springer, 2023

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Description 1 online resource Series Cognitive Technologies Cognitive technologies. Contents Intro -- 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 Summary This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders. This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features: Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection. Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders. Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used Analysis Internal Medicine Medical Notes Online resource; title from PDF title page (SpringerLink, viewed June 13, 2023) Subject Nervous system -- Degeneration -- Data processing Big data. Medical informatics. Big data Medical informatics Form Electronic book Author Koundal, Deepika. Jain, Deepak Kumar Guo, Yanhui. Ashour, Amira, 1975- Zaguia, Atef ISBN 9789819921546 9819921546

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