Information-Theoretic Analysis of EEG Signals to Differentiate Schizophrenic Patients with Positive and Negative Symptoms and Control Group

The precise differentiation of schizophrenic patients with positive and negative symptoms is still challenging; hence, psychiatrists mainly focus on diagnosing schizophrenic patients with positive symptoms. However, schizophrenic patients with negative symptoms have revealed remarkably poor outcomes.

This study aimed to differentiate schizophrenic patients with positive and negative dominant symptoms quantitatively by classifying their electroencephalography (EEG) features.

In this study, 36 patients with schizophrenia and 26 age-matched control subjects voluntarily participated. Their EEG signals were captured and characterized by elicited multiscale entropy to decode the number of irregularities captured in each EEG channel. The principal component analysis (PCA) was deployed to decrease the dimension of elicited features, and the reduced features were applied to three Gaussian Naive Bayes classifiers, each of which was trained for a specific class.

The classification of the three groups resulted in 77.86% accuracy, while this accuracy in the schizophrenic groups provided 65% accuracy. In the resting state, the normal and schizophrenic subjects were differentiated by a high rate (95.43%).

Exploiting information-theoretic features of the EEG signals over the scalp and automatic classification of these features, we can well-differentiate schizophrenic patients with different dominant symptoms. Moreover, better classification results can be achieved by passing the EEG features through PCA.