Hebei University of Technology, School of Electrical Engineering, State Key Laboratory of Reliability and Intelligence of Electrical Equipment Co-constructed by Province and Ministry, Tianjin, 300400, China; Hebei Key Laboratory of Electromagnetic Field and Electrical Reliability, School of Electrical Engineering, Hebei University of Technology, Tianjin, 300400, China.
Hebei University of Technology, School of Life Science and Health Engineering, Tianjin, 300130, China.
Comput Biol Med. 2023 Aug;162:107060. doi: 10.1016/j.compbiomed.2023.107060. Epub 2023 May 30.
With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.
随着 COVID-19 大流行在全球范围内给医院收治带来挑战，家庭健康监测在辅助诊断心理健康障碍方面的作用变得越来越重要。本文提出了一种可解释的机器学习解决方案，以优化男性和女性患者的重度抑郁症（MDD）初始筛查。该数据来自斯坦福技术分析和睡眠基因组研究（STAGES）。我们分析了 40 名 MDD 患者和 40 名健康对照者在夜间睡眠阶段的 5 分钟短期心电图（ECG）信号，男女比例为 1:1。在预处理后，我们根据 ECG 信号计算了心率变异性（HRV）的时频参数，并使用常见的机器学习算法进行分类，同时进行全局决策分析的特征重要性分析。最终，贝叶斯优化极端随机树分类器（BO-ERTC）在该数据集上表现出最佳性能（准确率 86.32%，特异性 86.49%，灵敏度 85.85%，F1 评分为 0.86）。通过对 BO-ERTC 确认的病例进行特征重要性分析，我们发现性别是影响模型预测的最重要因素之一，在我们的辅助诊断中不应忽视。这种方法可以嵌入便携式 ECG 监测系统，与文献结果一致。
