TY  - BOOK
AU  - Harikumar Rajaguru
AU  - Sunil Kumar Prabhakar
PY  - 2016
CY  - Hamburg, Germany
PB  - Anchor Academic Publishing
SN  - 9783960675990
TI  - Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy from EEG Signals
UR  - https://m.anchor-publishing.com/document/342648
N2  - Epilepsy is a common and diverse set of chronic neurological disorders characterized by seizures. It is a paroxysmal behavioral spell generally caused by an excessive disorderly discharge of cortical nerve cells of the brain. Epilepsy is marked by the term “epileptic seizures”. Epileptic seizures result from abnormal, excessive or hyper-synchronous neuronal activity in the brain. About 50 million people worldwide have epilepsy, and nearly 80% of epilepsy occurs in developing countries. The most common way to interfere with epilepsy is to analyse the EEG (electroencephalogram) signal which is a non-invasive, multi channel recording of the brain’s electrical activity. It is also essential to classify the risk levels of epilepsy so that the diagnosis can be made easier. This study investigates the possibility of Extreme Learning Machine (ELM) and Continuous GA as a post classifier for detecting and classifying epilepsy of various risk levels from the EEG signals. Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used for dimensionality reduction. 
KW  - Epilepsy, Epileptic seizure, Extreme Learning Machine, Singular Value Decomposition, Independent Component Analysis, Principal Component Analysis, Continuous GA
LA  - English
ER  -