Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients.

IF 5.6 2区医学 Q1 Medicine Sleep Pub Date : 2024-11-08 DOI:10.1093/sleep/zsae123

Jacob Sindorf, Alison L Szabo, Megan K O'Brien, Aashna Sunderrajan, Kristen L Knutson, Phyllis C Zee, Lisa Wolfe, Vineet M Arora, Arun Jayaraman

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Abstract

Study objectives: To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF).

Methods: 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).

Results: 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.

Conclusions: 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.

Clinical trial: SIESTA (Sleep of Inpatients: Empower Staff to Act) for Acute Stroke Rehabilitation, https://clinicaltrials.gov/study/NCT04254484?term=SIESTA&checkSpell=false&rank=1, NCT04254484.

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无线可穿戴传感器有助于快速检测住院中风患者的睡眠呼吸暂停。

研究目的评估可穿戴设备和机器学习在急性住院康复设施（IRF）中检测中风患者睡眠呼吸暂停的效果：作为一项大型临床试验的一部分，共有 76 名中风患者在入住 IRF 后的第一周内至少有一晚佩戴了标准家用睡眠呼吸检测仪（ApneaLink Air）、多模态无线可穿戴传感器系统（ANNE）和研究级动电仪（ActiWatch）。利用从 ANNE 传感器获得的生物特征和从 ApneaLink Air 获得的实际呼吸暂停评级，对逻辑回归算法进行了训练，以检测睡眠呼吸暂停。使用不同的传感器组合和基于呼吸暂停-高通气指数（AHI≥5、AHI≥15）的不同呼吸暂停检测标准对多种算法进行了评估：71名参与者（96%）连续多晚佩戴ANNE传感器。相比之下，只有 48 名参与者（63%）能通过 ApneaLink 成功评估 OSA；28 名参与者（37%）拒绝测试。表现最好的模型是利用光动压描记术（PPG）和手指温度特征来检测中重度睡眠呼吸暂停（AHI≥15），灵敏度为 88%，阳性似然比 (LR+) 为 44.00。该模型在更多夜晚的 ANNE 数据上进行了测试，当独立考虑每个夜晚时，灵敏度达到 71%（10.14 LR+），当平均多晚预测时，准确率达到 86%：这项研究证明了利用可穿戴传感器和机器学习技术在中风恢复早期准确检测中度-重度睡眠呼吸暂停的可行性。这些研究结果可为今后改善中风后睡眠障碍的早期检测提供参考，从而提高患者的康复水平和长期疗效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sleep Medicine-Neurology (clinical)

CiteScore

8.70

自引率

10.70%

发文量

期刊介绍： SLEEP® publishes findings from studies conducted at any level of analysis, including: Genes Molecules Cells Physiology Neural systems and circuits Behavior and cognition Self-report SLEEP® publishes articles that use a wide variety of scientific approaches and address a broad range of topics. These may include, but are not limited to: Basic and neuroscience studies of sleep and circadian mechanisms In vitro and animal models of sleep, circadian rhythms, and human disorders Pre-clinical human investigations, including the measurement and manipulation of sleep and circadian rhythms Studies in clinical or population samples. These may address factors influencing sleep and circadian rhythms (e.g., development and aging, and social and environmental influences) and relationships between sleep, circadian rhythms, health, and disease Clinical trials, epidemiology studies, implementation, and dissemination research.