Diagnosis of schizophrenia based on electroencephalogram signal using deep learning

Schizophrenia is a disorder of the brain in which people misinterpret reality. This mental disorder is characterized by behavioral symptoms such as hallucinations and speech disorders. Electroencephalogram (EEG) signal indicates brain disorders and is widely used to study brain diseases. The aim of this article is to automatically detect schizophrenia based on the EEG signal. A common method in research is the manual extraction of features from the EEG signal. Because deep learning algorithms have the ability to automatically extract important features and classify them, in this study, in order to extract more useful features, the EEG signal was applied to an eleven-layer convolutional deep recursive neural network. The EEG signals collected at the Warsaw Institute from 14 healthy individuals and 14 patients with schizophrenia are studied here. The mean values of the accuracy evaluation criteria of the model including Accuracy, Sensitivity, Specificity and PPV for the proposed model were 98.79%, 98.73%, 98.86% and 99.06%, respectively, which improved the performance of the proposed model for classifying schizophrenic patients and approves healthy people compared to previous models. The proposed model can be used as a diagnostic tool to help physicians diagnose the early stages of schizophrenia.