Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers - Kim, Taehyoung; Park, Ukeob; Kang, Seung Wan (Frontiers in Psychiatry, 2022)
Depression is the mental disorder that prevalent 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 entail 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 distinguished potential depression groups and normal groups with a test accuracy of up to 92.31% and a 10-fold cross validation loss of 0.13. This performance proposes a model with z-score QEEG metrics considering sex and age as an objective and a reliable biomarker for early-screening for the potential depression.