Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines

The electroencephalogram (EEG) has proven a valuable tool in the study and detection of epilepsy. This paper investigates for the first time the use of Permutation Entropy (PE) as a feature for automated epileptic seizure detection. A Support Vector Machine (SVM) is used to classify segments of normal and epileptic EEG based on PE values. The proposed system utilizes the fact that the EEG during epileptic seizures is characterized by lower PE than normal EEG. It is shown that average sensitivity of 94.38% and average specificity of 93.23% is obtained by using PE as a feature to characterize epileptic and seizure-free EEG, while 100% sensitivity and specificity were also obtained in single-trial classifications.

► The use of Permutation Entropy for automated seizure detection is investigated. ► Seizure EEG is characterized by lower Permutation Entropy values compared to normal EEG. ► Classification greater than 90% is obtained for discriminating seizure versus normal EEG. ► The low complexity of Permutation Entropy encourages its utilization in an automated seizure detection system.