Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features.

ABSTRACT:

Background

The search for a method that utilizes biomarkers to identify patients with schizophrenia from healthy individuals has occupied researchers for decades. However, no single indicator can be employed to achieve the good in clinical practice. We aim to develop a comprehensive machine learning pipeline based on neurocognitive and electrophysiological combined features for distinguishing schizophrenia patients from healthy people.

Methods

In the present study, 69 patients with schizophrenia and 50 healthy controls participated. Neurocognitive (contains seven specific domains of cognition) and electrophysiological [prepulse inhibition, electroencephalography (EEG) power spectrum, detrended fluctuation analysis, and fractal dimension (FD)] features were collected, all the

SUBMITTER: Tian Q 

PROVIDER: S-EPMC9016153 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature