Comprehensive analysis of extreme learning machine and continuous genetic algorithm for robust classification of epilepsy from EEG signals / Harikumar Rajaguru, Sunil Kumar Prabhakar
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 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 the epilepsy is to analysis the EEG (electroencephalogram) signal which is non invasive, multi channel recording of the brain's electrical activity. It is also essential to classify the risk levels of the epilepsy so that the diagnosis can be made easy. This project investigates the possibility of Extreme Learning Machine (ELM) and Continuous GA as a post classifier for detecting and classifying the epilepsy of various risk levels from the EEG signals. The Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used for dimensionality reduction
