EEG Brain Signal Classification for Epileptic Seizure Disorder Detection - 1st Edition - ISBN: 9780128174265

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

1st Edition

Authors: Sandeep Satapathy Satchidananda Dehuri Alok Jagadev Shruti Mishra
Paperback ISBN: 9780128174265
Imprint: Academic Press
Published Date: 7th February 2019
Page Count: 150
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Description

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides knowledge to classify the EEG brain signal for detection of epileptic seizures using machine learning techniques. Machine learning has recently developed most efficient techniques for solving different kind of problems and this book mainly concentrates on supervised machine learning techniques.

The book is divided into six short and right to the point chapters: the first presents an overview of machine learning techniques and tools available; the second discusses previous studies and compares the classical and the machine learning approaches for EEG classification; the third chapter encompasses empirical studies on the performance of the NN and SVM classifiers; a discussion on RBF neural network trained with improved PSO algorithm for epilepsy identification is presented in chapter four; the fifth discusses ABC algorithm optimized RBFNN for classification of EEG signal; and the final chapter presents future developments on the field.

This book is a valuable source for bioinformaticians, medical doctors, and several members of biomedical field who need to be up to speed on the most recent and promising automated techniques for EEG classification.

Key Features

  • Explores machine learning techniques, modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform technique for the identification of epileptic seizure
  • Encompasses also an overview of machine learning techniques easily understandable for non-specialized readers and especially developed for biomedical researchers
  • Provides a number of experimental analysis and their results are discussed with appropriate validation

Readership

Bioinformaticians; clinicians; medical doctors; neuroscientists

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

Details

No. of pages:
150
Language:
English
Copyright:
© Academic Press 2019
Published:
Imprint:
Academic Press
Paperback ISBN:
9780128174265

About the Author

Sandeep Satapathy

Dr. Sandeep Kumar Satapathy is currently working as an Associate Professor in the Department of Computer Science & Engineering and is also the Head of the Department of Information Technology at Vignana Bharathi Institute of Technology. Dr. Satapathy did his doctorate in the field of Data Mining & Machine Learning, and his thesis included a detailed classification of brain EEG signals using machine learning techniques. He has been member to various academic committees within the institution. Also, he has been an active reviewer in various peer reviewed journals and presented papers in prestigious conferences. He has also reviewed many research articles and books for Elsevier for possible publication. Prof. Satapathy is highly engrossed into the area of deep learning and image processing. He has many research publications to his credit, that is more than 25 research articles, book chapters and has guided more than 10 master thesis. Dr. Satapathy has also authored a book entitled Frequent Pattern Discovery from Gene Expression Data: An Experimental Approach. He is currently member of many professional organizations and societies. His research interest includes Bioinformatics and computational approaches to biomedical field.

Affiliations and Expertise

Associate Professor, Department of Computer Science & Engineering and Head of the Department, Department of Information Technology, Vignana Bharathi Institute of Technology (VBIT), India

Satchidananda Dehuri

Dr. Satchidananda Dehuri is working as an Associate Professor in the Department of Information and Communication Technology, Fakir Mohan University, Balasore, Odisha, India. Prior to this appointment, for a short stint (from Oct. 2012 to May 2014) he was an Associate Professor in the Department of Systems Engineering, Ajou University, South Korea. He received his M.Tech. and PhD degrees in Computer Science from Utkal University, Vani Vihar, Odisha in 2001 and 2006, respectively. He visited as a BOYSCAST Fellow to the Soft Computing Laboratory, Yonsei University, Seoul, South Korea under the BOYSCAST Fellowship Program of DST, Govt. of India in 2008. In 2010 he received Young Scientist Award in Engineering and Technology for the year 2008 from Odisha Vigyan Academy, Department of Science and Technology, Govt. of Odisha. He was at the Center for Theoretical Studies, Indian Institute of Technology Kharagpur as a Visiting Researcher in 2002 and 2017. During May-June 2006 he was a Visiting Scientist at the Center for Soft Computing Research, Indian Statistical Institute, Kolkata. His research interests include Evolutionary Computation, Neural Networks, Pattern Recognition, Data Warehousing and Mining, Object Oriented Programming and its Applications in Bioinformatics. He has already published about 220 research papers in reputed journals and referred conferences, has published five text books for undergraduate and graduate students and edited more than ten books of contemporary relevance.

Affiliations and Expertise

Reader, Department of Information & Communication Technology, Fakir Mohan University, India

Alok Jagadev

Dr. Alok Kumar Jagadev is currently working as a Professor in the School of Computer Engineering in KIIT (Deemed-to-be University), Bhubaneswar, Odisha, India. He was previously heading as an Associate Dean in the Department of Computer Science & Engineering, in Institute of Technical Education & Research, Siksha O Anusandhan (Deemed-to-be University), Bhubaneswar, Odisha, India. Dr. Jagadev has guided more than 4 doctoral students and more than 20 M.Tech students. He has published more than 30 research papers in reputed peer-reviewed journals, 4 book chapters and is having 4 books to his credits. His area of research comprises in the field of Data Mining, Bioinformatics, Soft Computing and Wireless Ad-hoc Networks.

Affiliations and Expertise

Professor, School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), India

Shruti Mishra

Dr. Mishra is currently working as an Associate Professor in the Department of Computer Science & Engineering, Vignana Bharathi Institute of Technology, Hyderabad and as former Head of the Department for the same institution. She was earlier working as an Assistant Professor, Department of Computer Science & Engineering, Institute of Technical Education & Research, Siksha O Anusandhan (Deemed-to-be University), Bhubaneswar, Odisha, India. She has guided 5 M.Tech thesis and more than 30 B.Tech students. Dr. Mishra has around 22 publications in various peer-reviewed journals and conference, 3 book chapters and 1 book to her credit. Her area of research is basically in Data Mining, Bioinformatics and Machine Learning. She is currently into the field of Geoinformatics and Deep Learning.

Affiliations and Expertise

Associate Professor, Department of Computer Science & Engineering, Vignana Bharathi Institute of Technology (VBIT), Hyderabad, India

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