SIGNAL-PROCESSING OF THE CONTINGENT NEGATIVE-VARIATION IN SCHIZOPHRENIA USING MULTILAYER PERCEPTRONS AND PREDICTIVE STATISTICAL DIAGNOSIS

An event related potential known as the contingent negative variation (CNV) was recorded from two sites from the brains of 20 medicated schi zophrenics and 20 normal control subjects. The aim was to apply signal processing, artificial neural networks and statistical techniques to the CNV waveform to improve the understanding of schizophrenia and to develop a neurophysiological technique for its identification and moni toring. CNV recording sites were the vertex and from a point midline a pproximately 30 mm anterior to the vertex (frontal). Three-layer multi layer perceptrons (MLPs) were used to discriminate between the CNV wav eforms of the schizophrenics and normal subjects. Although the MLP tec hnique was successful in discrimination, it did not provide a quantita tive measure for the analysis. Furthermore, during the test phase it a lways classified the subjects into one of the two categories and did n ot provide an output for either type (unknown type). To improve the cl inical diagnosis a discrimination technique based on predictive statis tical diagnosis (PSD) was developed. The input parameters to the PSD w ere a time domain feature and three features obtained from the energy spectrum of the CNV waveform. The PSD output indicated the probability and the atypicality index of each subject belonging to one of the two groups. Discrimination accuracy of the PSD was 100% for normal subjec ts. Three schizophrenics could not be classified into either type, but the rest were identified correctly. T-tests carried out on the record ed CNV waveforms showed that the CNV waveform recorded from the vertex site in normal subjects is significantly different from that recorded from the frontal site; however this was not the case for schizophreni cs.