Diagnosis of schizophrenia using an extended multivariate autoregressive model for EEGs.

Schizophrenia is a complex brain disorder that leads to an abnormal interpretation of reality. One of its reliable biological markers is the auditory evoked potential P300. The aim of the current paper is to classify healthy-control subjects from schizophrenic patients using EEG signals collected during an auditory oddball paradigm. The electroencephalogram (EEG) is modeled by a multivariate autoregressive (MVAR) model that takes into account the instantaneous causality between the EEG channels. After preprocessing, 19 channels of the recorded signals were divided into seven clusters based on their location. Next, the PCA technique was employed to obtain the first principal component inside each cluster. By imposing realistic constraints to estimate instantaneous effects between the variables, the instantaneous interactions matrix and, consequently, the extended multivariate autoregressive (eMVAR) model were estimated. Then, extended partial directed coherences (ePDCs) were extracted as connectivity features. The mRMR algorithm was utilized to reduce the feature dimension, and finally, the selected features were imported into a deep neural network for classification between healthy and schizophrenic states. The results showed that the eMVAR model outperformed the strictly causal model in classifying schizophrenic patients. With eMVAR modeling, an accuracy of 91.11% was obtained by using only four features. Furthermore, the most discriminative connectivity feature was ePDC from left posterior (LP) to (LP), and the most informative frequency band was the gamma sub-band. We have therefore presented evidence that the proposed approach enhances the characterization and diagnosis of schizophrenia.