A new Machine Learning approach for epilepsy diagnostic based on Sample Entropy

Abstract

Abstract Irregularity is the main characteristic of electroencephalographic signals (EEG), which needs a specific analysis method for neurological disease diagnosis. An efficient tool for signal irregularity analysis is Sample Entropy (SampEn). In this context, our paper was elaborated. We used SampEn to design a Machine Learning model for brain state detection based on EEG signals, which allows to differentiate between healthy (H) subjects, epileptic subjects during seizures free intervals (E) and epileptic subjects during seizures (S). Two main novelties are presented in our paper. The first one is related to the outline of the designed machine learning model, signal derivatives are determined as preprocessing step, then extracted features are SampEn and Standard Deviation (STD) from EEG signals and its first and second derivatives. These features are firstly used to train a K-Nearest Neighbor classifier (KNN) and yield high accuracy. After that, we select the most relevant features and we design our proposed classifier that provides better accuracy. The second one is related to the performance of our model to overcome some crucial purposes. In addition to the highest achieved accuracy, 100% for seizure detection, 99.2% for epilepsy detection and 99.86% for three class classification cases, our model used few features and simple classifier which involves fast running time. That is why we can consider our model as a suitable tool for real time applications.