He Hao, Li Chao, Ganglberger Wolfgang, Gallagher Kaileigh, Hristov Rumen, Ouroutzoglou Michail, Sun Haoqi, Sun Jimeng, Westover M Brandon, Katabi Dina
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA.
Sleep. 2025 Jan 13;48(1). doi: 10.1093/sleep/zsae187.
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine-learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e. polysomnography; n = 880) demonstrate that the model captures the sleep hypnogram (with an accuracy of 80.5% for 30-second epochs categorized into wake, light sleep, deep sleep, or REM), detects sleep apnea (AUROC = 0.89), and measures the patient's Apnea-Hypopnea Index (ICC = 0.90; 95% CI = [0.88, 0.91]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.
仅通过分析人们睡觉时从其身体反射回来的无线电波，就能在家中评估睡眠、捕捉睡眠阶段并检测呼吸暂停的发生（无需身体传感器），这一能力相当强大。这种能力将允许在患者家中进行纵向数据收集，为我们在临床试验和常规护理中理解睡眠及其与各种疾病的相互作用以及它们的治疗反应提供信息。在本文中，我们开发了一种先进的机器学习算法，用于被动监测睡眠期间从人体反射的无线电波以及夜间呼吸情况。与金标准（即多导睡眠图；n = 880）相比的验证结果表明，该模型能够捕捉睡眠脑电图（对于分为清醒、浅睡眠、深睡眠或快速眼动睡眠的30秒时段，准确率为80.5%），检测睡眠呼吸暂停（曲线下面积 = 0.89），并测量患者的呼吸暂停低通气指数（组内相关系数 = 0.90；95%置信区间 = [0.88, 0.91]）。值得注意的是，该模型在种族、性别和年龄方面表现出公平的性能。此外，该模型还揭示了睡眠阶段与一系列疾病（包括神经、精神、心血管和免疫疾病）之间的有益相互作用。这些发现不仅为临床实践和干预试验带来了希望，也凸显了睡眠作为理解和管理各种疾病的基本组成部分的重要性。
