Electroencephalogram for the Diagnosis of Depression: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.

Depression is a prevalent mental health condition with a significant global burden, yet its diagnosis remains challenging due to the inherent limitations of conventional assessment tools. The electroencephalography (EEG) could be a potential diagnostic modality of depression. This study aims to evaluate the diagnostic test accuracy of EEG in depression diagnosis. A systematic search was performed in the major academic databases from inception to January 25, 2025. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist was used to assess the bias of all the included studies. Bivariate random-effect models and hierarchical summary receiver operating characteristic (HSROC) curve models were used to illustrate the diagnostic performance. The pooled sensitivity (SEN), specificity (SPE), and the Diagnostic Odds Ratio (DOR) were calculated to evaluate the diagnostic accuracy. Subgroup analyses were conducted to investigate the possible sources of heterogeneity. A total of 18 studies were included, contributing 58 reported results in total. The diagnostic test accuracy of EEG for depression was high across the studies, as the pooled SEN, SPE, and DOR were 0.939 (95% CI: 0.915-0.957), 0.898 (95% CI: 0.840-0.937), and 137.493 (95% CI: 62.383-303.038), respectively. This study shows that EEG has a good application prospect in depression diagnosis, which could provide a definitive and quantitative reference. However, the quality of evidence in this study is affected by factors such as patient selection bias in the original case-control studies, small sample sizes, and unexplained clinical and statistical heterogeneity, so the findings should be interpreted with caution.