Artificial Intelligence for Neurological Disorders

1st Edition - September 22, 2022
Imprint: Academic Press
Editors: Ajith Abraham, Sujata Dash, Subhendu Kumar Pani, Laura García-Hernández
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 2 7 7 - 9
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 2 7 8 - 6

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurologi… Read more

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Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation.

The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.

Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
About the editors
Preface
Overview
Objective
Organization
Acknowledgment
Chapter 1: Early detection of neurological diseases using machine learning and deep learning techniques: A review
Abstract
Introduction
Literature review
Methodology and result analysis
Proposed method
Conclusion
References
Chapter 2: A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave dataset
Abstract
Introduction
Literature review
Materials and methods
Result analysis
Conclusion and discussion
References
Chapter 3: Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proliferation in human brain
Abstract
Introduction
How does AD affect the patient's life and normal functioning?
Can AD onset be avoided or at least be delayed?
Symptoms
Pathophysiology of AD
Management of AD
Introduction to machine learning and deep learning and their suitability to AD diagnosis
State of the art/national and international status
Conclusion
References
Further reading
Chapter 4: Convolutional neural network model for identifying neurological visual disorder
Abstract
Introduction
Human visual system
Convolutional neural network
Neurological visual disorder identifying model
Conclusion
References
Chapter 5: Recurrent neural network model for identifying neurological auditory disorder
Abstract
Introduction
Human auditory system
Recurrent neural network
Neurological auditory disorder identifying model
Conclusion
References
Chapter 6: Recurrent neural network model for identifying epilepsy based neurological auditory disorder
Abstract
Introduction
Related research
Proposed method
Experimental study
Conclusion
References
Chapter 7: Dementia diagnosis with EEG using machine learning
Abstract
Introduction
Cognitive testing and EEG
Discussion
Conclusion
References
Chapter 8: Computational methods for translational brain-behavior analysis
Abstract
Introduction
Computational physiology
Medical and data scientists
Translational brain behavioral pattern
Cognitive mapping and neural coding
Neuroelectrophysiology modeling
Clinical translation of cognitive mapping and neural coding
Systems biology in translational and computational biology
Summary
Conclusion
References
Chapter 9: Clinical applications of deep learning in neurology and its enhancements with future directions
Abstract
Introduction
Medical data and artificial intelligence in neurology
Neurology-centered medical system
Clinical applications of artificial intelligence and deep learning
Artificial intelligence for medical imaging and precision medicine
Examples of neurology AI
Challenges of deep learning applied to neuroimaging techniques
AI for assessing response to targeted neurological therapies
Conclusion and future perspectives
References
Chapter 10: Ensemble sparse intelligent mining techniques for cognitive disease
Abstract
Introduction
Cognitive disease
Machine learning and deep ensemble sparse regression network
Intelligent medical diagnostics with ensemble sparse intelligent mining techniques
High-dimensional data science in cognitive diseases
Diagnostic challenges with artificial intelligence
Summary
Conclusion and future perspectives
References
Chapter 11: Cognitive therapy for brain diseases using deep learning models
Abstract
Introduction
Brain diseases affecting cognitive functions
Multimodal information
Overview of deep learning techniques
Data preprocessing techniques
Early brain disease diagnosis using deep learning techniques
Artificial intelligence and cognitive therapies and immunotherapies
Summary
Conclusion and future perspectives
References
Chapter 12: Cognitive therapy for brain diseases using artificial intelligence models
Abstract
Introduction
Brain diseases
Brain diseases and physiological signals
Artificial intelligence
Artificial intelligence, neuroscience, and clinical practice
Data acquisition and image interpretation
Artificial intelligence and cognitive behavioral therapy
Challenges and pitfalls
Summary
Conclusion and future direction
References
Chapter 13: Clinical applications of deep learning in neurology and its enhancements with future predictions
Abstract
Introduction
Neural network systems, biomarkers, and physiological signals
Neurological techniques, biomedical informatics, and computational neurophysiology
Data and image acquisition
Artificial intelligence and deep learning
Artificial intelligence and neurological disease prediction
Non-clinical health-related applications
Challenges and potential pitfalls of neurological techniques
Conclusion and future directions
References
Chapter 14: An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning
Abstract
Introduction
Epileptic seizure
Seizure localization
Physiological and pathophysiological signals
Chemical signals as physiological signals
Endocrine disorders as deviations from physiological signals
Neurotransmitter detection using artificial intelligence
Electrical signals as physiological signals
Action potentials
Application of electrical signals
Artificial intelligence and action potential detection
Electrocorticography and electroencephalography
Electrocardiograph recording and placement
Electroencephalography and other non-invasive techniques
Applications of electroencephalography
Electrocorticography
Summary
Conclusion and future research
References
Chapter 15: Neural signaling and communication using machine learning
Abstract
Introduction
Electrophysiology of brain waves
Neural signaling and communication
Brain–computer interface (data acquisition)
Algorithm classification of brain functions using machine learning
Artificial intelligence and neural signals, communications
Challenges and opportunities
Summary
Conclusion and future perspectives
References
Chapter 16: Classification of neurodegenerative disorders using machine learning techniques
Abstract
Introduction
Patient datasets
Related medical examinations
Clinical tests and biomarkers
Classification of neurodegenerative diseases
Machine learning techniques as computer-assisted diagnostic systems
Multimodal analysis
Conclusion and future perspectives
References
Chapter 17: New trends in deep learning for neuroimaging analysis and disease prediction
Abstract
Introduction
Deep learning techniques
Neuroimaging and data science
Cognitive impairment
Images, text, sounds, waves, biomarkers, and physiological signals
Artificial intelligence and disease diagnosis and prediction
Current challenges of heterogeneous multisite datasets and opportunities
Summary
Conclusion and future directions
References
Chapter 18: Prevention and diagnosis of neurodegenerative diseases using machine learning models
Abstract
Introduction
Neurodegenerative diseases
Artificial intelligence (AI) and machine learning (ML)
AI and clinical practice
Neurodegenerative diseases and physiological signals
Neurodegenerative disease data acquisition
Challenges in data handling
Summary
Conclusion and future perspectives
References
Chapter 19: Artificial intelligence-based early detection of neurological disease using noninvasive method based on speech analysis
Abstract
Introduction
Neurological disorders
Cognitive analysis—Psychological evaluation and physiological signals
Noninvasive screening methods for speech analysis
Computer-aided diagnosis (CAD) systems
Artificial intelligence and machine learning techniques
Deep learning-based techniques
Artificial intelligence and CAD systems for early detection of neurological disorders
Summary
Conclusion and future perspective
References
Chapter 20: An insight into applications of deep learning in neuroimaging
Abstract
Introduction
Deep learning concepts
Neuroimaging
Deep learning case studies in neurological disorders
Dementia diagnosis
Open-source tool kits for deep learning
Challenges and future directions
Conclusion
References
Chapter 21: Incremental variance learning-based ensemble classification model for neurological disorders
Abstract
Introduction
Literature review
Proposed incremental variance learning-based ensemble classification model for neurological disorders
Discrete wavelet transform
Result and comparison
Conclusion and future scope
References
Chapter 22: A systematic review of adaptive machine learning techniques for early detection of Parkinson's disease
Abstract
Introduction
Feature engineering for identifying clinical biomarkers
Application of machine learning methods for diagnosing PD
Methodology and result analysis
Proposed model
Conclusion
References
Further Reading
Index

Ajith Abraham

Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia.

Affiliations and expertise

Pro Vice-Chancellor, Sai University, Chennai, India

Sujata Dash

Sujata Dash holds the position of Professor at the Information Technology School of Engineering and Technology, Nagaland University, Dimapur Campus, Nagaland, India, bringing more than three decades of dedicated service in teaching and mentoring students. She has been honoured with the prestigious Titular Fellowship from the Association of Commonwealth Universities, United Kingdom. As a testament to her global contributions, she served as a visiting professor in the Computer Science Department at the University of Manitoba, Canada. With a prolific academic record, she has authored over 200 technical papers published in esteemed international journals, and conference proceedings, and edited book chapters by reputed publishers Serving as a reviewer and Associate Editor for approximately 15 international journals.

Affiliations and expertise

Department of Information Technology, School of Engineering and Technology, Nagaland University, India

Subhendu Kumar Pani

Dr. Subhendu Kumar Pani received his Ph.D. from Utkal University, Odisha, India in the year 2013. He is working as a professor at Krupajal Engineering College under BPUT, Odisha, India. He has more than 20 years of teaching and research experience His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is the recipient of 5 researcher awards. In addition to research, he has guided two PhD students and 31 M. Tech students. He has published 150 International Journal papers (100 Scopus index). His professional activities include roles as Book Series Editor (CRC Press, Apple Academic Press, Wiley-Scrivener), Associate Editor, Editorial board member and/or reviewer of various International Journals. He is an Associate with no. of the conference societies. He has more than 250 international publications, 5 authored books, 25 edited and upcoming books; 40 book chapters into his account. He is a fellow in SSARSC and a life member in IE, ISTE, ISCA, and OBA.OMS, SMIACSIT, SMUACEE, CSI.

Affiliations and expertise

Krupajal Engineering College, Prashanti Vihar, Near CIFA, Kausalya Ganga, Bhubaneswar, Khordha, Odisha, India

Laura García-Hernández

LAURA GARCÍA-HERNÁNDEZ received the M.Sc. degree in computer science from the Universitat Oberta de Catalunya, Spain, in 2007, and the European Ph.D. degree in Engineering from the University of Córdoba, Spain, and also from the Institut Français de Mécanique Avancée, Clermont-Ferrand, France, in 2011. She has been an Invited Professor during a semester in the Institut Français de Mécanique Avancée, Clermont-Ferrand. She is currently an Associate Professor in the Area of Project Engineering at the University of Córdoba, Spain. Her primary areas of research are engineering design optimization, intelligent systems, machine learning, user adaptive systems, interactive evolutionary computation, project management, risk prevention in automatic systems, and educational technology. In these fields, she has authored or co-authored more than 70 international research publications. She has given several invited talks in different countries. She has realized several postdoctoral internships in different countries with a total duration of more than two years. She received the prestigious National Government Research Grant ‘‘José Castillejo’’ for supporting their post-doc research during six months in the University of Algarve, Portugal. She has been an Investigator Principal in two Spanish research projects and has also been an Investigator Collaborator in some research contracts and projects. She is an Expert Member of ISO/TC 184/SC working team and the National Standards Institute of Spain (UNE). Moreover, she is a member of the Spanish Association of Engineering Projects (IPMA Spain). Considering her research, she received the Young Researcher Award granted by the Spanish Association of Engineering Projects (IPMA), Spain, in 2015. Additionally, she received two times the General Council of Official Colleges Award at prestigious International Conference on Project Management and Engineering both 2017 and 2018 editions. She is the Co-Editor-in-Chief of the Journal of Information Assurance and Security. Also, she is an Associate Editor in the following ISI Journals: Applied Soft Computing, Complex & Intelligent Systems, and Journal of Intelligent Manufacturing.

Affiliations and expertise

Associate Professor of Project Engineering, University of Córdoba, Córdoba, Spain

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Artificial Intelligence for Neurological Disorders

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Ajith Abraham

Sujata Dash

Subhendu Kumar Pani

Laura García-Hernández

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