Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy.

ABSTRACT: Functional near-infrared spectroscopy (fNIRS) has been widely employed in the objective diagnosis of patients with schizophrenia during a verbal fluency task (VFT). Most of the available methods depended on the time-domain features extracted from the data of single or multiple channels. The present study proposed an alternative method based on the functional connectivity strength (FCS) derived from an individual channel. The data measured 100 patients with schizophrenia and 100 healthy controls, who were used to train the classifiers and to evaluate their performance. Different classifiers were evaluated, and support machine vector achieved the best performance. In order to reduce the dimensional complexity of the feature domain, principal component analysis (PCA) was applied. The classifi

SUBMITTER: Yang J 

PROVIDER: S-EPMC7575761 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature