基于三轴加速度计实现跨设备、抗睡眠障碍的睡眠-清醒检测。
A robust generalizable device-agnostic deep learning model for sleep-wake determination from triaxial wrist accelerometry
- 用深度学习模型处理30秒加速度数据，结合决策树优化清醒期识别。
- 睡眠/清醒分类F1值达0.86，与多导睡眠图相关性达0.69（总睡眠时间）。
- 在三种设备上均表现稳定，适合有睡眠呼吸暂停等障碍人群使用。
研究目标：腕部加速度计广泛用于推断睡眠-清醒状态。以往研究存在清醒期检测差、跨设备泛化能力不足，且未在不同年龄及睡眠障碍人群中验证的问题。我们开发了一种鲁棒的深度学习模型，用于从三轴加速度计数据中检测睡眠-清醒状态，并在三个设备和涵盖广泛年龄范围的大型成人队列（含与不含睡眠障碍者）中评估其有效性。方法：在三级睡眠中心对453名成人同步采集加速度计与多导睡眠图（PSG）数据，使用三种设备。提取30秒时序特征，训练三分类模型区分清醒、睡眠及觉醒状态，再通过决策树合并为清醒与睡眠两类。为提升清醒期检测，模型在某一设备记录中随机选取低睡眠效率或高觉醒指数的受试者数据进行训练，然后在其余数据上测试。结果：模型性能优异，F1分数为0.86，睡眠敏感性0.87，清醒特异性0.78；预测总睡眠时间（与PSG相关性R=0.69）和睡眠效率（R=0.63）均有显著中度相关。模型对睡眠障碍（包括睡眠呼吸暂停和周期性肢体运动）具有鲁棒性，且在所有三种加速度计设备上表现一致。结论：我们提出一种深度学习模型，可有效从成人的活动记录中检测睡眠-清醒状态，对睡眠障碍具有较强鲁棒性，并具备在多种常用腕戴设备间的良好泛化能力。
原文摘要 · Abstract (English)
Study Objectives: Wrist accelerometry is widely used for inferring sleep-wake state. Previous works demonstrated poor wake detection, without cross-device generalizability and validation in different age range and sleep disorders. We developed a robust deep learning model for to detect sleep-wakefulness from triaxial accelerometry and evaluated its validity across three devices and in a large adult population spanning a wide range of ages with and without sleep disorders. Methods: We collected wrist accelerometry simultaneous to polysomnography (PSG) in 453 adults undergoing clinical sleep testing at a tertiary care sleep laboratory, using three devices. We extracted features in 30-second epochs and trained a 3-class model to detect wake, sleep, and sleep with arousals, which was then collapsed into wake vs. sleep using a decision tree. To enhance wake detection, the model was specifically trained on randomly selected subjects with low sleep efficiency and/or high arousal index from one device recording and then tested on the remaining recordings. Results: The model showed high performance with F1 Score of 0.86, sensitivity (sleep) of 0.87, and specificity (wakefulness) of 0.78, and significant and moderate correlation to PSG in predicting total sleep time (R=0.69) and sleep efficiency (R=0.63). Model performance was robust to the presence of sleep disorders, including sleep apnea and periodic limb movements in sleep, and was consistent across all three models of accelerometer. Conclusions: We present a deep model to detect sleep-wakefulness from actigraphy in adults with relative robustness to the presence of sleep disorders and generalizability across diverse commonly used wrist accelerometers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。