EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

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Language: English

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134 p. · 15x22.8 cm · Paperback

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.

1. Introduction2. Literature survey3. Empirical study on the performance of the classifiers in EEG classification4. EEG signal classification using RBF neural network trained with improved PSO algorithm for epilepsy identification5. ABC optimized RBFNN for classification of EEG signal for epileptic seizure identification6. Conclusion and future research

bioinformaticians; clinicians; medical doctors; neuroscientists
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.
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 O
  • 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