Classification of long-term EEG recordings

Abstract

Computer assisted processing of long-term EEG recordings is gaining a growing importance. To simplify the work of a physician, that must visually evaluate long recordings, we present a method for automatic processing of EEG based on learning classifier. This method supports the automatic search of long-term EEG recording and detection of graphoelements - signal parts with characteristic shape and defined diagnostic value. Traditional methods of detection show great percent of error caused by the great variety of non-stationary EEG. The idea of this method is to break down the signal into stationary sections called segments using adaptive segmentation and create a set of normalized discriminative features representing segments. The groups of similar patterns of graphoelements form classes used for the learning of a classifier. Weighted features are used for classification performed by modified learning classifier fuzzy k-Nearest Neighbours. Results of classification describe classes of unknown segments. The implementation of this method was experimentally verified on a real EEG with the diagnosis of epilepsy. © Springer-Verlag Berlin Heidelberg 2004.