Performance of dynamic features in classifying scalp epileptic interictal and normal EEG.

Over 50 million people worldwide suffer from epilepsy. Recently, researchers have proposed computer-aided epilepsy diagnostic systems based on classifying scalp epileptic interictal and normal EEG. Features used in the classification can be divided into two groups: classical spectral features and dynamic features. Classical spectral features are similar to major frequency component identification that physicians use in conventional EEG reading. Because dynamic features are new compared to classical spectral features, we are interested in knowing whether they are suitable for this classification problem. To study this, we build such a system and compare the results between using classical spectral features and dynamic features. Furthermore, we study which dynamic features are more suitable, i.e., more discriminative, by ranking them using F-score. According to the result, we discuss redesigning certain dynamic features for better classification. This research is a preliminary study of using dynamic features of scalp interictal EEG for epilepsy diagnosis.