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基于神经网络算法的 Belun 睡眠平台可穿戴设备检测阻塞性睡眠呼吸暂停及其与 STOP-Bang 问卷的联合应用。

Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire.

机构信息

Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center and Department of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America.

Belun Technology Company Limited, Sha Tin, Hong Kong.

出版信息

PLoS One. 2021 Oct 11;16(10):e0258040. doi: 10.1371/journal.pone.0258040. eCollection 2021.

Abstract

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5-15; 19% had AHI 15-30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703-0.888], 0.931 [95% CI, 0.772-0.992], and 0.735 [95% CI, 0.589-0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828-0.987], 0.944 [95% CI, 0.727-0.999], and 0.933 [95% CI, 0.779-0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1.

摘要

许多可穿戴设备可以在睡眠中获取生理数据,并使临床医生能够在睡眠实验室外评估睡眠。Belun 睡眠平台 (BSP) 是一种新型基于神经网络的家用睡眠呼吸暂停检测系统,它使用可穿戴戒指设备来检测阻塞性睡眠呼吸暂停 (OSA)。本研究的目的是评估 BSP 在评估 OSA 方面的性能。该队列纳入了服用影响心率药物和伴有非心律失常合并症的受试者。在因疑似睡眠呼吸暂停而被转介到睡眠实验室进行整夜睡眠研究的个体中,同时进行多导睡眠图 (PSG) 研究。睡眠研究使用美国睡眠医学学会评分手册 (第 2.4 版) 进行手动评分,使用 4%的脱氧低通气标准。共招募了 78 名受试者。其中,45%的人 AHI<5;18%的人 AHI 为 5-15;19%的人 AHI 为 15-30;18%的人 AHI≥30。Belun 呼吸暂停-低通气指数 (bAHI) 与 PSG-AHI 相关性良好 (r=0.888,P<0.001)。Belun 总睡眠时间 (bTST) 和 PSG-TST 具有高度相关系数 (r=0.967,P<0.001)。在分类 AHI≥15 时,BSP 的准确性、敏感度和特异性分别为 0.808 [95%CI,0.703-0.888]、0.931 [95%CI,0.772-0.992] 和 0.735 [95%CI,0.589-0.850]。β受体阻滞剂/钙通道拮抗剂的使用和合并症的存在并不影响 BSP 预测 OSA 的敏感性和特异性。结合 STOP-Bang 截断值为 5 和 bAHI 截断值为 15 事件/h 的诊断算法显示,对中重度 OSA 的诊断准确率、敏感度和特异性分别为 0.938 [95%CI,0.828-0.987]、0.944 [95%CI,0.727-0.999] 和 0.933 [95%CI,0.779-0.992]。BSP 是一种有前途的 OSA 评估测试工具,可潜在地纳入临床实践以识别 OSA。试验注册:ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/366b/8504733/a0b95686a202/pone.0258040.g001.jpg

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