A surrogate channel based analysis of EEG signals for detection of epileptic seizure

The human brain produces electrical signals which prove vital in understanding the degree of abnormality that may, in many cases, result in a person behaving unusually. Epileptic seizures are known to be sudden surges of electrical activity in the brain which cause the affected person to behave abnormally for a short time period. The information contained in these signals is recorded via an EEG machine. Traditionally, these signals are interpreted by specialist neurologists. This technique, however, induces the human error into the observation which may cause fatal damage. This research presents an autonomous system, capable of detecting the occurrence of an epileptic seizure, without the help of a specialist. The solution presented in this study consists of four steps i.e. pre-processing, feature extraction, feature selection and classification. The purpose of pre-processing is to organize the data in an orderly manner and to remove noise. We have also applied Laplacian smoothing on multichannel data to generate a surrogate channel having information of all channels. The feature extraction phase extracts temporal, spectral and time-spectral domain features for proper representation of seizure and non-seizure samples. The phase is followed by the process of feature selection, where the best set of features are determined using rank features and are finally used to classify input EEG signals as normal or abnormal using a hybrid classifier. The proposed system is tested on a publicly available dataset and results show the significance of the proposed system.