Feature selection in high dimensional EEG features spaces for epileptic seizure prediction

Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy; seizure prediction based on the EEG information content is an area of intense activity since at least twenty years. In this paper we analyze the high dimensional feature space created by a variety of feature extraction methods for prediction of epileptic seizures. We combined features selection algorithm minimum redundancy maximum relevance (mRMR) and Support Vector Machines (SVMs) architectures to study the best features set for seizure prediction. We present the comparison between the classification results obtained by a feature set composed by 147 features and a reduced set based on the first 20-ranked features using mRMR scores. We critically discuss the composition of the feature subset. The results suggest some patient specificity in features and channel selection. The best models lead us to hypothesize the preference for wider preictal periods.