EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

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EEG BRAIN SIGNAL
CLASSIFICATION
FOR EPILEPTIC
SEIZURE
DISORDER
DETECTION
Copyright
Preface
1
Introduction
	Problem Statement
	General and Specific Goals
	Basic Concepts of EEG Signal
		Delta Wave (δ)
		Theta Waves (θ)
		Alpha Waves (α)
		Beta Waves (β)
		Gamma Waves (γ)
		Mu Waves (μ)
	Overview of ML Techniques
		Multilayer Perceptron Neural Network
		Radial Basis Function Neural Network
		Recurrent Neural Network
		Probabilistic Neural Network
		Support Vector Machines
	Swarm Intelligence
	Tools for Feature Extraction
	Contributions
	Summary and Structure of Book
2
Literature Survey
	EEG Signal Analysis Methods
	Preprocessing of EEG Signal
	Tasks of EEG Signal
	Classical vs Machine Learning Methods for EEG Classification
	Machine Learning Methods for Epilepsy Classification
	Summary
3
Empirical Study on the Performance of the Classifiers in EEG Classification
	Multilayer Perceptron Neural Network
		MLPNN With Back-Propagation
		MLPNN With Resilient Propagation
		MLPNN With Manhattan Update Rule
	Radial Basis Function Neural Network
	Probabilistic Neural Network
	Recurrent Neural Network
	Support Vector Machines
	Experimental Study
		Datasets and Environment
		Parameters
		Results and Analysis
	Summary
4
EEG Signal Classification Using RBF Neural Network Trained With Improved PSO Algorithm for Epilepsy Identification
	Related Work
	Radial Basis Function Neural Network
		RBFNN Architecture
		RBFNN Training Algorithm
	Particle Swarm Optimization
		Architecture
		Algorithm
	RBFNN With Improved PSO Algorithm
		Architecture of Proposed Model
		Algorithm for Proposed Model
	Experimental Study
		Dataset Preparation and Environment
		Parameters
		Results and Analysis
	Summary
5
ABC Optimized RBFNN for Classification of EEG Signal for Epileptic Seizure Identification
	Related Work
	Artificial Bee Colony Algorithm
		Architecture
		Algorithm
	RBFNN With Improved ABC Algorithm
		Architecture of the Proposed Model
		Algorithm for the Proposed Model
	Experimental Study
		Dataset Preparation and Environment
		Parameters
		Result and Analysis
		Performance Comparison Between Modified PSO and Modified ABC Algorithm
	Summary
6
Conclusion and Future Research
	Findings and Constraints
	Future Research Work
References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	R
	S
	T
	U
	V
	W
	X
	Z
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