Schizophrenia Identification Using Machine Learning Methods with Graph-Theoretic Features

To realize the automatic identification of electroencephalogram (EEG) signals from patients with schizophrenia, this study calculated graph-theoretic features derived from event-related potentials (ERP) recorded during a Go/NoGo task. The ERP data were collected from 42 individuals diagnosed with schizophrenia and 29 healthy controls. Different functional networks of single-frequency band and all band of these EEG data, were constructed by phase locking value method. Their graph-theoretic features were extracted. Three machine learning classifiers were used to automatically distinguish the patients and health controls. The classification accuracies of different models with graph-theoretic features of different networks were compared. The results revealed that F-score combined with support vector machine (SVM) model achieved the highest accuracy. This model with graph-theoretic features of all-band network and theta band networks resulted in the highest classification accuracy, which was 95.45