An end-to-end deep learning framework based on EEGNet achieved a diagnostic accuracy of 90.98% for major depressive disorder using EEG signals evoked by happy-neutral face pairs.
Cross-Sectional (n=53)
Does an end-to-end deep learning framework based on EEGNet accurately diagnose major depressive disorder using EEG signals?
An end-to-end deep learning framework based on EEGNet can accurately distinguish patients with major depressive disorder from healthy controls using EEG signals.
Major depressive disorder (MDD) is a common and highly debilitating condition that threatens the health of millions of people. However, current diagnosis of depression relies on questionnaires that are highly correlated with physician experience and hence not completely objective. Electroencephalography (EEG) signals combined with deep learning techniques may be an objective approach to effective diagnosis of MDD. This study proposes an end-to-end deep learning framework for MDD diagnosis based on EEG signals. We used EEG signals from 29 healthy subjects and 24 patients with severe depression to calculate Accuracy, Precision, Recall, F1-Score, and Kappa coefficient, which were 90.98%, 91.27%, 90.59%, and 81.68%, respectively. In addition, we found that these values were highest when happy-neutral face pairs were used as stimuli for detecting depression. Compared with exiting methods for EEG-based MDD classification, ours can maintain stable model performance without re-calibration. The present results suggest that the method is highly accurate for diagnosis of MDD and can be used to develop an automatic plug-and-play EEG-based system for diagnosing depression.
Liu et al. (Fri,) conducted a cross-sectional in Major depressive disorder (n=53). EEGNet deep learning framework was evaluated on Diagnostic accuracy. An end-to-end deep learning framework based on EEGNet achieved a diagnostic accuracy of 90.98% for major depressive disorder using EEG signals evoked by happy-neutral face pairs.