Automatic seizure detection in long-term scalp EEG using Weighted Permutation Entropy and Support Vector Machine

The automated epileptic seizure detection has emerged as an important field in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this paper, a model has been introduced that integrates Weighted Permutation Entropy (WPE) as input feature to a Support Vector Machine (SVM) learning model to enhance the sensitivity and precision of the detection process. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG. Experiments have been conducted to demonstrate the sensitivity and precision of the proposed model. Results showed that the model can considerably discriminate between EEG brain signal into seizure and no-seizure classes.