ArticleFrontiers in psychiatry2022
Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Methodological guidance on clinical prediction models in mental health research.Psychological medicine · 2026Review
- Fatigue in Parkinson's Disease Is Due to Decreased Efficiency of the Frontal Network: Quantitative EEG Analysis.Journal of movement disorders · 2024Article
- Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.Brain sciences · 2023Article
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3 authors.
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Abstract
Depression is a prevalent mental disorder in modern society, causing many people to suffer or even commit suicide. Psychiatrists and psychologists typically diagnose depression using representative tests, such as the Beck's Depression Inventory (BDI) and the Hamilton Depression Rating Scale (HDRS), in conjunction with patient consultations. Traditional tests, however, are time-consuming, can be trained on patients, and entailed a lot of clinician subjectivity. In the present study, we trained the machine learning models using sex and age-reflected z-score values of quantitative EEG (QEEG) indicators based on data from the National Standard Reference Data Center for Korean EEG, with 116 potential depression subjects and 80 healthy controls. The classification model has distinguished potential depression groups and normal groups, with a test accuracy of up to 92.31% and a 10-cross-validation loss of 0.13. This performance proposes a model with
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