Epileptic Seizure Detection Based on Complexity Feature of EEG

Abstract

Brain disorder characterized by seizure is a common disease among people in the world. Characterization of electroencephalogram (EEG) signals in terms of complexity can be used to identify neurological disorders. In this study, a non-linear epileptic seizure detection method based on multiscale entropy (MSE) has been employed to characterize the complexity of EEG signals. For this reason, the MSE method has been applied on Bonn dataset containing seizure and non-seizure EEG data and the corresponding results in terms of complexity have been obtained. Using statistical tests and support vector machine (SVM), the classification ability of the MSE method has been verified on Bonn dataset. Our results show that the MSE method is a viable approach to identifying epileptic seizure demonstrating a classification accuracy of 91.7%.