Max Nader Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, USA.
Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Sleep. 2024 Nov 8;47(11). doi: 10.1093/sleep/zsae123.
To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF).
A total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least 1 night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the apnea-hypopnea index (AHI ≥ 5, AHI ≥ 15).
Seventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only 48 participants (63%) could be successfully assessed for obstructive sleep apnea by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger-temperature features to detect moderate-severe sleep apnea (AHI ≥ 15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions.
This research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes.
SIESTA (Sleep of Inpatients: Empower Staff to Act) for Acute Stroke Rehabilitation, https://clinicaltrials.gov/study/NCT04254484?term=SIESTA&checkSpell=false&rank=1, NCT04254484.
评估可穿戴设备和机器学习在急性住院康复机构（IRF）中检测中风患者睡眠呼吸暂停的能力。
共有 76 名中风患者在入住 IRF 的第一周内至少佩戴了一整晚标准家用睡眠呼吸暂停测试（ApneaLink Air）、多模态无线可穿戴传感器系统（ANNE）和研究级活动记录仪（ActiWatch），这是一项更大规模临床试验的一部分。逻辑回归算法利用从 ANNE 传感器获得的生物特征和 ApneaLink Air 的睡眠呼吸暂停真实评分来训练以检测睡眠呼吸暂停。根据呼吸暂停低通气指数（AHI≥5、AHI≥15），使用不同的传感器组合和不同的睡眠呼吸暂停检测标准评估了多种算法。
71 名（96%）参与者连续多晚佩戴了 ANNE 传感器。相比之下，只有 48 名（63%）参与者可通过 ApneaLink 成功评估阻塞性睡眠呼吸暂停；28 名（37%）拒绝测试。表现最好的模型利用光容积描记法（PPG）和指温特征来检测中重度睡眠呼吸暂停（AHI≥15），其灵敏度为 88%，阳性似然比（LR+）为 44.00。该模型在额外的 ANNE 数据上进行了测试，当独立考虑每一个夜晚时，其灵敏度为 71%（10.14 LR+），当平均多晚预测时，准确率为 86%。
这项研究证明了使用可穿戴传感器和机器学习技术在中风康复过程早期准确检测中重度睡眠呼吸暂停的可行性。这些发现可以为改善中风后睡眠障碍的早期检测提供信息，从而促进患者康复和长期预后。
急性中风康复中的住院患者睡眠：增强工作人员的行动力（SIESTA），https://clinicaltrials.gov/study/NCT04254484?term=SIESTA&checkSpell=false&rank=1，NCT04254484。
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