A nearest neighbor based approach for classifying epileptiform EEG using nonlinear DWT features

Abstract

Epilepsy is a pathological condition characterized by spontaneous, unforeseeable occurrence of seizures, during which the perception or behaviour of a person is altered, if not disturbed. In prediction of occurance of seizures, better classification accuracies have been reported with the use of non linear features and hence they have been estimated from wavelet transformed Electro Encephalo Graph (EEG) data and used to train k Nearest Neighbour (kNN) classifier to classify the EEG into normal, background and epileptic classes. Very good accuracy performance of nearly 100% has been reported from the current work. © 2012 IEEE.