We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self‐screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24‐h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non‐cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart‐device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning‐based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication.

中文翻译：

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睡眠阶段心率变异性预测压力严重程度：构建基于机器学习的压力预测模型

我们提出了一种通过使用智能设备测量睡眠阶段心率变异性 (HRV) 来预测压力严重程度的新方法。该设备有可能应用于大量人群的压力自我筛查。利用动态心电图（ECG）和华为智能设备，对159名轮班医务人员进行24小时双重记录。基于华为智能设备采集的光电体积描记法（PPG）和加速度计信号，我们对循环交替模式（CAP；不稳定睡眠）、非循环交替模式（NCAP；稳定睡眠）、觉醒和快速眼动（REM）的发作进行了排序。 ）基于心肺耦合（CPC）算法的睡眠。我们使用动态心电图和智能设备 PPG 信号进一步计算了 NCAP、CAP 和 REM 睡眠期间的 HRV 指数。后来，我们开发了一种机器学习模型，仅根据从参与者获得的智能设备数据以及对情绪和压力条件的临床评估来预测压力严重程度。睡眠阶段 HRV 指数可预测个体压力严重程度，CAP 或 REM 睡眠中的表现优于 NCAP。仅使用智能设备数据，基于机器学习的最佳压力预测模型的准确度为 80.3%，灵敏度为 87.2%，特异性为 63.9%。可以使用智能设备准确评估睡眠阶段心率变异性，随后可用于压力预测。