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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection
TRUE PDF
Price 1€
2020
(Original quality)
1th Edition
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.
- Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures
- Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers
- Provides a number of experimental analyses, with their results discussed and appropriately validated
Review
Provides the knowledge necessary to classify EEG brain signals for the detection of epileptic seizures using machine learning techniques
Contents
۱. INTRODUCTION
۱.۱ Problem Statement
۱.۲ General and Specific Goals
۱.۳ Basic Concepts of EEG Signal
۱.۴ Overview of Machine Learning Techniques
۱.۵ Swarm Intelligence
۱.۶ Tools for Feature Extraction
۱.۷ Our Contributions
۱.۸ Summary and Structure of Thesis
۲. LITERATURE SURVEY
۲.۱ EEG Signal Analysis Methods
۲.۲ Pre-processing of EEG Signal
۲.۳ Tasks of EEG Signal
۲.۴ Classical vs. Machine Learning Methods for EEG Classification
۲.۵ Machine Learning Methods for Epilepsy Classification
۲.۶ Summary
۳. EMPIRICAL STUDY ON THE PERFORMANCE OF THE CLASSIFIERS IN EEG CLASSIFICATION
۳.۱ Multilayer Perceptron Neural Network
۳.۱.۱ MLPNN with Back-Propagation
۳.۱.۲ MLPNN with Resilient-Propagation
۳.۱.۳ MLPNN with Manhatan Update Rule
۳.۲ Radial Basis Function
۳.۳ Probabilistic Neural Network
۳.۴ Recurrent Neural Network
۳.۵ Support Vector Machines
۳.۶ Experimental Study
۳.۶.۱ Datasets and Environment
۳.۶.۲ Parameters
۳.۶.۳ Results and Analysis
۳.۷ Summary
۴. EEG SIGNAL CLASSIFICATION USING RBF NEURAL NETWORK TRAINED WITH IMPROVED PSO ALGORITHM FOR EPILEPSY IDENTIFICATION
۴.۱ Related Work
۴.۲ Radial Basis Function Neural Network
۴.۲.۱ RBFNN Architecture
۴.۲.۲ RBFNN Training Algorithm
۴.۳ Particle Swarm Optimization
۴.۳.۱ Architecture
۴.۳.۲ Algorithm
۴.۴ RBFNN with Improved PSO Algorithm
۴.۴.۱ Architecture of Proposed Model
۴.۴.۲ Algorithm for Proposed Model
۴.۵ Experimental Study
۴.۵.۱ Dataset Preparation and Environment
۴.۵.۲ Parameters
۴.۵.۳ Results and Analysis
۴.۶ Summary
۵. ABC OPTIMIZED RBFNN FOR CLASSIFICATION OF EEG SIGNAL FOR EPILEPTIC SEIZURE IDENTIFICATION
۵.۱ Related Work
۵.۲ Artificial Bee Colony algorithm
۵.۲.۱ Architecture
۵.۲.۲ Algorithm
۵.۳ RBFNN with Improved ABC Algorithm
۵.۳.۱ Architecture of Proposed Model
۵.۳.۲ Algorithm for Proposed Model
۵.۴ Experimental Study
۵.۴.۱ Dataset Preparation and Environment
۵.۴.۲ Parameters
۵.۴.۳ Results and Analysis
۵.۵ Performance Comparison between Modified PSO and Modified ABC Algorithms
۵.۶ Summary
۶. CONCLUSION AND FUTURE RESEARCH
۶.۱ Findings and Constraints of Our Work
۶.۲ Future Research Work