WAVE-FORM DETECTION WITH RBF NETWORK - APPLICATION TO AUTOMATED EEG-ANALYSIS

Automated detection of different waveforms in physiological signals ha s been one of the most intensively studied applications of signal proc essing in the clinical medicine. During recent years an increasing amo unt of neural network based methods have been proposed. In this paper we present a radial basis function (RBF) network based method for auto mated detection of different interference waveforms in epileptic EEG. This kind of artefact detector is especially useful as a preprocessing system in combination with different kinds of automated EEG analyzers to improve their general applicability. The results show that our neu ral network based classifier successfully detects artefacts at the rat e of over 75% while the correct classification rate for normal segment s is as high as about 95%. (C) 1998 Elsevier Science B.V. All rights r eserved.