Liu Bo, Chang Hongli, Peng Kang, Wang Xuenan
Department of Emergency, The Second Hospital of Shandong University, Jinan, China.
School of Information Science and Engineering, Southeast University, Nanjing, China.
Front Psychiatry. 2022 Mar 11;13:864393. doi: 10.3389/fpsyt.2022.864393. eCollection 2022.
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.
重度抑郁症（MDD）是一种常见且极具致残性的疾病，威胁着数百万人的健康。然而，目前抑郁症的诊断依赖于与医生经验高度相关的问卷，因此并不完全客观。脑电图（EEG）信号与深度学习技术相结合可能是有效诊断MDD的一种客观方法。本研究提出了一种基于EEG信号的用于MDD诊断的端到端深度学习框架。我们使用了来自29名健康受试者和24名重度抑郁症患者的EEG信号来计算准确率、精确率、召回率、F1分数和kappa系数，其分别为90.98%、91.27%、90.59%和81.68%。此外，我们发现当使用快乐-中性面部配对作为检测抑郁症的刺激时，这些值最高。与现有的基于EEG的MDD分类方法相比，我们的方法无需重新校准即可保持稳定的模型性能。目前的结果表明，该方法对MDD的诊断具有高度准确性，可用于开发一种基于EEG的自动即插即用抑郁症诊断系统。
