K-COMPLEX DETECTION USING MULTILAYER PERCEPTRONS AND RECURRENT NETWORKS

The feasibility of using a multi-layer perceptron and Elman's recurren t network for the detection of specific waveforms (K-complexes) in ele ctroencephalograms (EEGs), regardless of their location in the signal segment, is explored. Experiments with simulated and actual EEG data w ere performed. In case of the perceptron, the input consisted of the m agnitude and/or phase values obtained from 10-s signal intervals, wher eas the recurrent net operated on the digitized data samples directly. It was found that both nets performed well on the simulated data, but not on the actual EEG data. The reasons for the failure of both nets are discussed.