Optimizing functional near-infrared spectroscopy (fNIRS) channels for schizophrenic identification during a verbal fluency task using metaheuristic algorithms.

ABSTRACT:

Objective

We aimed to reduce the complexity of the 52-channel functional near-infrared spectroscopy (fNIRS) system to facilitate its usage in discriminating schizophrenia during a verbal fluency task (VFT).

Methods

Oxygenated hemoglobin signals obtained using 52-channel fNIRS from 100 patients with schizophrenia and 100 healthy controls during a VFT were collected and processed. Three features frequently used in the analysis of fNIRS signals, namely time average, functional connectivity, and wavelet, were extracted and optimized using various metaheuristic operators, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and their parallel and serial hybrid algorithms. Support vector machine (SVM) was used as the classifier, and the performance was evaluated by ten

SUBMITTER: Xia D 

PROVIDER: S-EPMC9342670 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature