A Study of EEG Feature Complexity in Epileptic Seizure Prediction

The purpose of this study is (1) to provide EEG feature complexity analysis in seizure prediction by inter-ictal and pre-ital data classification and, (2) assess the between-subject variability considered features. In past several decades, there has been a sustained interest predicting epilepsy using data. Most methods classify features extracted from EEG, which they assume are characteristic presence an episode, for instance, distinguishing pre-ictal interval (which given window just before onset seizure) preceding windows following seizure). To evaluate difficulty problem independently model, we investigate exhaustive list 88 various metrics, i.e., Fisher discriminant ratio, volume overlap, individual efficiency. Complexity measurements on real synthetic testbeds reveal that pre-ictal/inter-ictal distinction significant complexity. It shows clearly useful, without decidedly identifying optimal set.