Automatic Detection of Epileptic Seizures Using Permutation Entropy, Tsallis Entropy and Kolmogorov Complexity

Abstract

Epilepsy is a neurological disorder which is characterized by repeated seizures. Although these can be recorded using Electroencephalogram (EEG), it is very difficult task to classify the different states such as normal, preictal and ictal by visual inspection due to the non-linear and non stationary property of the EEG signals and also due to its long term recordings. Thus automatic detection of these states using various techniques has been in the literature for a very long time. We used novel features such as permutation entropy (PE), Tsallis entropy (TsE), and Kolmogorov complexity (KC) along with five different classifiers. We achieved a highest accuracy of 89.33% with Decision tree classifier. Our method being very simple and has fast computation time in comparison with other features in the literature and thus can form as a software tool that can be installed easily and also opens future opportunities towards real time detection and prediction of epileptic seizures.