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

Su Mi Park, Boram Jeong, Da Young Oh, Chi Hyun Choi, Hee Yeon Jung, Jun Young Lee, Donghwan Lee, Jung Seok Choi

Research output: Contribution to journalArticlepeer-review

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 performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive–compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

Original languageEnglish
Article number707581
JournalFrontiers in Psychiatry
Volume12
DOIs
StatePublished - 18 Aug 2021

Keywords

  • classification
  • electroencephalography
  • functional connectivity
  • machine learning
  • power spectrum density
  • psychiatric disorder
  • resting-state brain function

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