Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea.
Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Sci Rep. 2018 Nov 19;8(1):17030. doi: 10.1038/s41598-018-35147-3.
Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power.
重度抑郁症（MDD）是一种常见的精神疾病，也是全球范围内导致残疾的主要原因。然而，目前用于诊断抑郁症的方法主要依赖于临床访谈和自我报告的抑郁症状量表，这些方法缺乏客观性和效率。为了解决这一挑战，我们提出了一种使用皮肤电活动（EDA）来筛选 MDD 的机器学习方法。参与者包括 30 名 MDD 患者和 37 名健康对照者。在五个实验阶段测量了他们的 EDA，包括基线、心算任务、应激任务恢复、放松任务和放松任务恢复，这些阶段引起了自主活动的多种变化。从每个阶段提取选定的 EDA 特征，并评估两个不同阶段之间的差分 EDA 特征。通过使用这些特征作为输入数据，并使用 SVM-RFE 进行特征选择，我们的决策树分类器可以实现 74%的准确率、74%的灵敏度和 71%的特异性。SVM-RFE 选择的最相关特征包括差分 EDA 特征和应激和放松任务的特征。这些发现表明，基于 EDA 特征的自动检测抑郁症是可行的，并且在个体经历自主唤醒和恢复时监测生理信号的变化可能会增强区分能力。
