ENSEMBLE FEATURE SELECTION (EFS) AND ENSEMBLE HYBRID CLASSIFIERS (EHCS) FOR DIAGNOSIS OF SEIZURE USING EEG SIGNALS

Abstract

Epilepsy is the neural disorder that occurs in the individual mind which affects nearly 50 million people around the world. It is also said to be the universal disorder which affects all ages. The disturbance that occurs in the nervous system causes seizure. The classification of epileptiform activity in the EEG plays an essential role in the identification of epilepsy. To extract the relevant information and to improve the accuracy level from the given EEG signals, Fuzzy Based Cuckoo Search (FCS) and ant colony optimization (ACO) methods are planned to select the related and best information’s. Finally utilizes the Ensemble Hybrid Classifiers (EHCs) which combine the procedure of Modified Convolutional Neural Network (MCNN), Improved Relevance Vector Machine (IRVM) and Logistic Regression (LR) classifiers for analysis of EEG signals. The planned effort is implemented to notice the irregularity in three different levels of EEG signals (normal, affected and unaffected).

Keywords

• epilepsy
• seizure
• ant colony optimization