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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection


SATAPATHY/DEHURI/JAGADEV/MISHRA  

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

134 Seiten, 1. Auflage, 2019

  • 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
Table of Contents:

1. INTRODUCTION
  • 1.1 Problem Statement
  • 1.2 General and Specific Goals
  • 1.3 Basic Concepts of EEG Signal
  • 1.4 Overview of Machine Learning Techniques
  • 1.5 Swarm Intelligence
  • 1.6 Tools for Feature Extraction
  • 1.7 Our Contributions
  • 1.8 Summary and Structure of Thesis
2. LITERATURE SURVEY
  • 2.1 EEG Signal Analysis Methods
  • 2.2 Pre-processing of EEG Signal
  • 2.3 Tasks of EEG Signal
  • 2.4 Classical vs. Machine Learning Methods for EEG Classification
  • 2.5 Machine Learning Methods for Epilepsy Classification
  • 2.6 Summary
3. EMPIRICAL STUDY ON THE PERFORMANCE OF THE CLASSIFIERS IN EEG CLASSIFICATION
  • 3.1 Multilayer Perceptron Neural Network
  • 3.1.1 MLPNN with Back-Propagation
  • 3.1.2 MLPNN with Resilient-Propagation
  • 3.1.3 MLPNN with Manhatan Update Rule
  • 3.2 Radial Basis Function
  • 3.3 Probabilistic Neural Network
  • 3.4 Recurrent Neural Network
  • 3.5 Support Vector Machines
  • 3.6 Experimental Study
  • 3.6.1 Datasets and Environment
  • 3.6.2 Parameters
  • 3.6.3 Results and Analysis
  • 3.7 Summary
4. EEG SIGNAL CLASSIFICATION USING RBF NEURAL NETWORK TRAINED WITH IMPROVED PSO ALGORITHM FOR EPILEPSY IDENTIFICATION
  • 4.1 Related Work
  • 4.2 Radial Basis Function Neural Network
  • 4.2.1 RBFNN Architecture
  • 4.2.2 RBFNN Training Algorithm
  • 4.3 Particle Swarm Optimization
  • 4.3.1 Architecture
  • 4.3.2 Algorithm
  • 4.4 RBFNN with Improved PSO Algorithm
  • 4.4.1 Architecture of Proposed Model
  • 4.4.2 Algorithm for Proposed Model
  • 4.5 Experimental Study
  • 4.5.1 Dataset Preparation and Environment
  • 4.5.2 Parameters
  • 4.5.3 Results and Analysis
  • 4.6 Summary
5. ABC OPTIMIZED RBFNN FOR CLASSIFICATION OF EEG SIGNAL FOR EPILEPTIC SEIZURE IDENTIFICATION
  • 5.1 Related Work
  • 5.2 Artificial Bee Colony algorithm
  • 5.2.1 Architecture
  • 5.2.2 Algorithm
  • 5.3 RBFNN with Improved ABC Algorithm
  • 5.3.1 Architecture of Proposed Model
  • 5.3.2 Algorithm for Proposed Model
  • 5.4 Experimental Study
  • 5.4.1 Dataset Preparation and Environment
  • 5.4.2 Parameters
  • 5.4.3 Results and Analysis
  • 5.5 Performance Comparison between Modified PSO and Modified ABC Algorithms
  • 5.6 Summary
6. CONCLUSION AND FUTURE RESEARCH
  • 6.1 Findings and Constraints of Our Work
  • 6.2 Future Research Work
References


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