Artificial Intelligence for Neurological Disorders

Front Cover
Artificial Intelligence for Neurological Disorders
Copyright
Dedication
Contents
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
	Introduction
		Support vector machine
		Random forest
		Logistic regression
		Convolutional neural network
	Literature review
		Machine learning algorithms
		Deep learning algorithms
	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
	Introduction
	Literature review
	Materials and methods
		IoT-based Muse headband
		Feature selection
		Datasets
			Feature selection algorithms
				Symmetric uncertainty
		Deep learning model
			LSTM classification
	Result analysis
	Conclusion and discussion
	References
Chapter 3: Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proli
	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
	Introduction
	Human visual system
		Visual cortex
		Vision disorders
		Cortical blindness
			Acquired cortical blindness
			Congenital cortical blindness
			Transient cortical blindness
	Convolutional neural network
		Image recognition
		Image classification
		Cognitive application
	Neurological visual disorder identifying model
		Receptive field
		Activation map
		Kernel filter
	Conclusion
	References
Chapter 5: Recurrent neural network model for identifying neurological auditory disorder
	Introduction
	Human auditory system
		Neurological auditory disorder
		Central auditory nervous system
		Cortical deafness
	Recurrent neural network
		Speech recognition
		Auditory event-related potentials
		Sentence boundary disambiguation
	Neurological auditory disorder identifying model
		Audio segmentation
		Phonetic recognition
		Attention mechanism
	Conclusion
	References
Chapter 6: Recurrent neural network model for identifying epilepsy based neurological auditory disorder
	Introduction
	Related research
		Multiperspective learning techniques
		TSK fuzzy system
	Proposed method
		Shallow feature acquisition of EEG signals
		Shallow feature construction in time-frequency domain
		Acquisition of deep features based on deep learning
		Frequency domain deep feature extraction network
		Time-frequency domain deep feature extraction network
		Multiview TSK blur system based on view weighting
	Experimental study
		Dataset
		Validity analysis
		Numerical analysis of deep feature extraction networks
	Conclusion
	References
Chapter 7: Dementia diagnosis with EEG using machine learning
	Introduction
		Prevalence of dementia worldwide
		Electroencephalogram
	Cognitive testing and EEG
		Data acquisition
		Preprocessing of EEG signal
		Feature extraction
			Linear approach
		Nonlinear approach
		Classification of dementia
	Discussion
	Conclusion
	References
Chapter 8: Computational methods for translational brain-behavior analysis
	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
		Application of system biology in translational brain tumor research
	Summary
	Conclusion
	References
Chapter 9: Clinical applications of deep learning in neurology and its enhancements with future directions
	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
	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
	Introduction
	Brain diseases affecting cognitive functions
	Multimodal information
		Connectome mapping
		Post-operative seizure
		Gene signature
	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
	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
	Introduction
	Neural network systems, biomarkers, and physiological signals
	Neurological techniques, biomedical informatics, and computational neurophysiology
		Neurological techniques
		Biomedical informatics
		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
	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
		Electroencephalography
	Electrocardiograph recording and placement
	Electroencephalography and other non-invasive techniques
	Applications of electroencephalography
	Electrocorticography
		Role of data scientists in epileptic seizure detection
		Intelligent diagnostic approaches: Machine learning and deep learning
		Selecting appropriate machine learning classifiers and features
	Summary
	Conclusion and future research
	References
Chapter 15: Neural signaling and communication using machine learning
	Introduction
	Electrophysiology of brain waves
		Electrophysiology of alpha waves
		Electrophysiology of beta waves
		Electrophysiology of delta waves
		Electrophysiology of theta waves
		Electrophysiology of gamma waves
		Electrophysiology of mu waves
		Electrophysiology of sensorimotor rhythms
	Neural signaling and communication
		Neural signaling and communication
		Electrical signals as physiological signals
		Action potentials
		Application of electrical signals
	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
	Introduction
	Patient datasets
	Related medical examinations
		Clinical tests
		Biomarkers
	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
	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
	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 speec ...
	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
	Introduction
	Deep learning concepts
		Recurrent neural network (RNN)
		Convolutional neural network (CNN)
		Self-organizing map (SOM)
		Boltzmann machine (BM)
		Restricted Boltzmann machine (RBM)
		Autoencoder (AE)
	Neuroimaging
	Deep learning case studies in neurological disorders
		Alzheimer's disease (AD)
		Parkinson's disease (PD)
		Attention-deficit/hyperactive disorder (ADHD)
		Autism spectrum disorder (ASD)
		Schizophrenia analysis
	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
	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
	Introduction
	Feature engineering for identifying clinical biomarkers
		Population-based metaheuristics for biomarker selection
	Application of machine learning methods for diagnosing PD
	Methodology and result analysis
		Characteristics of chaotic maps
	Proposed model
	Conclusion
	References
	Further Reading
Index
Back Cover