EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

✍ Scribed by Sandeep Kumar Satapathy, Satchidananda Dehuri, Alok Kumar Jagadev, Dr. Shruti Mishra

• Publisher: Academic Press
• Year: 2019
• Tongue: English
• Leaves: 127
• Category: Library

✦ Synopsis

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field.

This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification.

✦ Table of Contents

Cover
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
Back Cover