Department of Electrical Engineering and Computer Science, Faculty of Systems Design, Tokyo Metropolitan University, Tokyo 191-0065, Japan.
Maynds Tower Mental Clinic, Tokyo 151-0053, Japan.
Sensors (Basel). 2023 Apr 10;23(8):3867. doi: 10.3390/s23083867.
To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the , and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.
为了鼓励有潜在重度抑郁症（MDD）的患者参加诊断环节，我们开发了一种新的基于睡眠诱发自主神经反应的 MDD 筛查系统。该方法仅需要佩戴腕带式设备 24 小时。我们通过腕部光体积描记法（PPG）评估心率变异性（HRV）。然而，先前的研究表明，使用可穿戴设备获得的 HRV 测量值容易受到运动伪影的影响。我们提出了一种通过去除不可靠的 HRV 数据（基于 PPG 传感器获得的信号质量指数（SQI）识别）来提高筛查准确性的新方法。该算法能够实时计算频域中的信号质量指数（SQI-FD）。在梅因兹塔心理诊所进行的一项临床研究纳入了 40 名 MDD 患者（平均年龄 37.5 ± 8.8 岁），这些患者是根据 诊断的，29 名健康志愿者（平均年龄 31.9 ± 13.0 岁）。加速度数据用于识别睡眠状态，使用 HRV 和脉搏率数据对线性分类模型进行训练和测试。十折交叉验证显示，敏感性为 87.3%（不使用 SQI-FD 数据时为 80.3%），特异性为 84.0%（不使用 SQI-FD 数据时为 73.3%）。因此，SQI-FD 极大地提高了敏感性和特异性。
