Automatic schizophrenia discrimination on fNIRS by using PCA and SVM

Abstract

A method is proposed to distinguish patients with schizophrenia from healthy controls based on data measured by functional near-infrared spectroscopy (fNIRS) during a cognitive task, which combines principal component analysis (PCA) and support vector machine (SVM). Firstly, a data reduction technique is applied prior to PCA, and then PCA is used to extract features on oxygenated hemoglobin (oxy-Hb) signals from 52-channel fNIRS data of schizophrenia and healthy subjects. Secondly, a classifier based on SVM is designed to discriminate schizophrenia from healthy controls. We recruited a large sample of 52 schizophrenia patients and 38 healthy controls. The hemoglobin response was measured in the prefrontal cortex during the one-back memory task using a 52-channel fNIRS system. The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 93.33%, 100% for schizophrenia samples and 84.62% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.