EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

Description

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. 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