Using Electroencephalographic Signal Processing and Machine Learning Binary Classification to diagnose Schizophrenia

Abstract

Electroencephalography (EEG) is an electrical activity measurement technique used to identify brain activity in Schizophrenic patients. Novel machine learning methods have emerged with useful applications for Schizophrenia classification. This research aims to compare the performance of several models post signal processing, such as Random Forest (RF), Support Vector Machine (SVM), Extra Trees (ET), and K-Nearest Neighbor (KNN), in the classification of healthy and Schizophrenic patients. The dataset used in this study contains 14 healthy and 14 Schizophrenic patients (n=28), with 17 channels and 2 reference electrodes designated to each patient, from the Nalecz Institute of Biocybernetics and Biomedical Engineering and the Institute of Psychiatry and Neurology in Warsaw, Poland. Signal processing feature extraction was performed using time-series or frequency-series Electroencephalographic data. The results suggest that Random Forest achieved the best performance metrics, achieving an accuracy, precision, recall, F1 score, and AUC of 92.4%, 95.5%, 95.5%, 0.939, and 0.932, respectively. These results propose that machine learning algorithms can be used to classify hospitalized patients who may have Schizophrenia, a useful supplement to additional clinical diagnosis performed by physicians.