Suppr超能文献

利用可穿戴传感器通过纳入来自姿势/睡眠体位变化和身体加速度的信息来改善睡眠质量评估:胸部佩戴传感器、腕部活动记录仪和多导睡眠图的比较。

Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography.

机构信息

Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Department of Surgery, Baylor College of Medicine, Houston, Texas.

UAHS Center for Sleep and Circadian Science, University of Arizona, Tucson, Arizona.

出版信息

J Clin Sleep Med. 2017 Nov 15;13(11):1301-1310. doi: 10.5664/jcsm.6802.

Abstract

STUDY OBJECTIVES

To improve sleep quality assessment using a single chest-worn sensor by extracting body acceleration and sleep position changes.

METHODS

Sleep patterns of 21 participants (50.8 ± 12.8 years, 47.8% female) with self-reported sleep problems were simultaneously recorded using a chest sensor (Chest), an Actiwatch (Wrist), and polysomnography (PSG) during overnight sleep laboratory assessment. An algorithm for Chest was developed to detect sleep/wake epochs based on body acceleration and sleep position/postural changes data, which were then used to estimate sleep parameters of interest. Comparisons between Chest and Wrist with respect to PSG were performed. Identification of sleep/wake epochs was assessed by estimating sensitivity, specificity, and accuracy. Agreement between sensor-derived sleep parameters and PSG was assessed using correlation coefficients and Bland-Altman analysis.

RESULTS

Chest identified sleep/wake epochs with an accuracy of on average 6% higher than Wrist (85.8% versus 79.8%). Similar trends were observed for sensitivity/specificity values. Correlation between Wrist and PSG was poor for most of the sleep parameters of interest ( = 0.0-0.3); however, Chest and PSG correlation showed moderate to strong agreement ( = 0.4-0.8) with relatively low bias and high precision bias (precision): 9.2 (13.2) minutes for sleep onset latency; 17.3(34.8) minutes for total sleep time; 7.5 (29.8) minutes for wake after sleep onset; and 2.0 (7.3)% for sleep efficacy.

CONCLUSIONS

Combination of sleep postural/position changes and body acceleration improved detection of sleep/wake epochs compared to wrist acceleration alone. The chest sensors also improved estimation of sleep parameters of interest with stronger agreement with PSG. Our findings may expand the application of wearable sensors to clinically assess sleep outside of a sleep laboratory.

摘要

研究目的

通过提取身体加速度和睡眠体位变化来改善单胸佩戴式传感器的睡眠质量评估。

方法

21 名(50.8±12.8 岁,47.8%为女性)自述有睡眠问题的参与者同时使用胸传感器(Chest)、腕动记录仪(Wrist)和多导睡眠图(PSG)进行整夜睡眠实验室评估,记录其睡眠模式。开发了一种 Chest 算法,根据身体加速度和睡眠体位/姿势变化数据来检测睡眠/觉醒期,然后使用这些数据来估计感兴趣的睡眠参数。比较了 Chest 和 Wrist 与 PSG 的相关性。通过估计敏感性、特异性和准确性来评估睡眠/觉醒期的识别。使用相关系数和 Bland-Altman 分析评估传感器衍生的睡眠参数与 PSG 的一致性。

结果

Chest 平均比 Wrist 高出 6%(85.8%对 79.8%)的准确性来识别睡眠/觉醒期。敏感性/特异性值也存在类似的趋势。大多数感兴趣的睡眠参数的 Wrist 和 PSG 相关性都较差( = 0.0-0.3);然而,Chest 和 PSG 的相关性显示出中度至高度一致性( = 0.4-0.8),具有相对较低的偏差和较高的精度偏差(精度):睡眠潜伏期为 9.2(13.2)分钟;总睡眠时间为 17.3(34.8)分钟;睡眠后觉醒时间为 7.5(29.8)分钟;睡眠效率为 2.0(7.3)%。

结论

与腕部加速度相比,睡眠体位/姿势变化和身体加速度的组合可提高睡眠/觉醒期的检测。胸部传感器还改善了感兴趣的睡眠参数的估计,与 PSG 的一致性更强。我们的研究结果可能会扩大可穿戴传感器在睡眠实验室外临床评估睡眠的应用。

相似文献

引用本文的文献

7
Recent Progress in Long-Term Sleep Monitoring Technology.近期长时睡眠监测技术的进展
Biosensors (Basel). 2023 Mar 17;13(3):395. doi: 10.3390/bios13030395.

本文引用的文献

2
Understanding Bland Altman analysis.理解布兰德-奥特曼分析。
Biochem Med (Zagreb). 2015 Jun 5;25(2):141-51. doi: 10.11613/BM.2015.015. eCollection 2015.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验