Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach.

ABSTRACT: We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction perf

SUBMITTER: Park SM 

PROVIDER: S-EPMC8416434 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature