Classification of schizophrenia using general linear model and support vector machine via fNIRS

In this study, we designed an optimized data-preprocessing method accompanied with techniques of general linear model feature extraction, independent sample t-test feature selection and support vector machine to identify a set of robust fNIRS pattern features as a biomarker to discriminate schizophrenia patients and healthy people. Experimental results demonstrated that the proposed combination way of data preprocessing, feature extraction, feature selection and support vector machine classification can effectively identify schizophrenia patients and the healthy people with a leave-one-out-cross-validation classification accuracy of 89.5%.

Source: Australasian Physical and Engineering Sciences in Medicine - October 28, 2020 Category: Biomedical Engineering Source Type: research