Table of Contents
Cover image
Title page
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 earlystage 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
Copyright
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Dedication
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Subhendu Kumar Pani, Editor
Laura García-Hernández, Editor
Contributors
Mayowa J. Adeniyi Department of Physiology, Edo State University Uzairue, Iyamho, Nigeria
Charles O. Adetunji Department of Microbiology, Applied Microbiology, Biotechnology and Nanotechnology Laboratory, Edo State University Uzairue, Iyamho, Nigeria
Olorunsola Adeyomoye Department of Physiology, University of Medical Sciences, Ondo, Nigeria
Rishabh Anand Service Deliver Manager, HCL Technologies Limited, New Delhi, India
Korhan Cengiz College of Information Technology, University of Fujiarah, Fujairah, United Arab Emirates
Ayobami Dare Department of Physiology, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, Westville Campus, University of KwaZulu-Natal, Durban, South Africa
Sujata Dash Department of Computer Application, Maharaja Sriram Chandra Bhanja Deo University, Baripada, Odisha, India
Pijush Du a Department of Electronics and Communication Engineering, Greater Kolkata College of Engineering and Management, West Bengal, India
Alex Enoch Department of Human Physiology, Ahmadu Bello University Zaria, Zaria, Nigeria
M.A. Jabbar Department of CSE (AI & ML), Vardhaman College of Engineering, Hyderabad, India
Maheshkumar H. Kolekar Department of Electrical Engineering, Indian Institute of Technology Patna, Bihta, Patna, Bihar, India
Asok Kumar Student Welfare Department, Vidyasagar University, Medinipur, West Bengal, India
Hima Bindu Maringanti MSCB University, Baripada, Odisha, India
Minati Mishra P.G. Department of Computer Science, Fakir
Mohan University, Balasore, Odisha, India
Ricky Mohanty School of Information System, ASBM University, Bhubaneswar, India
Nihar Ranjan Nayak Department of MCA, Sri Venkateswara College of Engineering & Technology (Autonomous), Chi oor, Andhra Pradesh, India
Olugbemi T. Olaniyan Department of Physiology, Laboratory for Reproductive Biology and Developmental Programming, Rhema University, Aba, Nigeria
Sagar Dhanraj Pande Intelligent System, School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India
Subhendu Kumar Pani Krupajal Computer Academy, Bhubaneswar, India
Shobhandeb Paul Department of Electronics and Communication Engineering, Guru Nanak Institute of Technology, Kolkata, West Bengal, India
Subhransu Pradhan Directorate of Health Services, Bhubaneswar, Odisha, India
Syed Saba Raoof Department of CSE, VIT University, Vellore, India
V. Selvakumar Department of Maths and Statistics, Bhavan's Vivekananda College of Science, Humanities and Commerce,
Hyderabad, Telangana, India
Neelam Sharma Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
K. Rupabanta Singh Department of Computer Application, Maharaja Sriram Chandra Bhanja Deo University, Baripada, Odisha, India
Brijesh K. Soni Department of Computer Science and Technology, AKS University, Satna, Madhya Pradesh, India
Mukesh Soni Department of Computer Science and Engineering, Jagran Lakecity University, Bhopal, India
Akhilesh A. Waoo Department of Computer Science and Technology, AKS University, Satna, Madhya Pradesh, India
