EEG signal classification using frequency band analysis towards epileptic seizure prediction

Abstract

Epilepsy is one of the most common and diverse set of chronic neurological disorders characterized by an abnormal excessive or synchronous neuronal activity in the brain that is termed “seizure”, affecting about 50 million individuals worldwide. Electroencephalogram (EEG) signal processing technique plays a significant role in detection and prediction of epileptic seizure. Recently, many research works have been devoted to detect/predict of epileptic seizure based on analysis of EEG signals. Even though remarkable works have been conducted on seizure detection/prediction, experimental results are not mature enough in terms of sensitivity, specificity, and accuracy. In this paper we present a new approach for seizure detection to analysis preictal (before seizure onset) and interictal (period between seizures) EEG signals by extracting different features from gamma frequency band by decomposing the signals using discrete wavelet transformation. Note that the detection of preictal and interictal EEG signals leads to predict the epileptic seizure. Experimental results demonstrate that the propose method outperforms the state-of-the-art method in terms of sensitivity, specificity and accuracy to classify seizure by analyzing EEG signals to the benchmark dataset in different brain locations.