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使用可穿戴心率传感器WHS-1进行新的R-R间期分析以识别阻塞性睡眠呼吸暂停的临床实用性:使用可穿戴心跳传感器进行的阻塞性睡眠呼吸暂停和R-R间期分析

Clinical Usefulness of New R-R Interval Analysis Using the Wearable Heart Rate Sensor WHS-1 to Identify Obstructive Sleep Apnea: OSA and RRI Analysis Using a Wearable Heartbeat Sensor.

作者信息

Arikawa Takuo, Nakajima Toshiaki, Yazawa Hiroko, Kaneda Hiroyuki, Haruyama Akiko, Obi Syotaro, Amano Hirohisa, Sakuma Masashi, Toyoda Shigeru, Abe Shichiro, Tsutsumi Takeshi, Matsui Taishi, Nakata Akio, Shinozaki Ryo, Miyamoto Masayuki, Inoue Teruo

机构信息

Department of Cardiovascular Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Tochigi 321-0293, Japan.

Division of Cardiology, Eda Memorial Hospital, Kanagawa 225-0012, Japan.

出版信息

J Clin Med. 2020 Oct 20;9(10):3359. doi: 10.3390/jcm9103359.

Abstract

Obstructive sleep apnea (OSA) is highly associated with cardiovascular diseases, but most patients remain undiagnosed. Cyclic variation of heart rate (CVHR) occurs during the night, and R-R interval (RRI) analysis using a Holter electrocardiogram has been reported to be useful in screening for OSA. We investigated the usefulness of RRI analysis to identify OSA using the wearable heart rate sensor WHS-1 and newly developed algorithm. WHS-1 and polysomnography simultaneously applied to 30 cases of OSA. By using the RRI averages calculated for each time series, tachycardia with CVHR was identified. The ratio of integrated RRIs determined by integrated RRIs during CVHR and over all sleep time were calculated by our newly developed method. The patient was diagnosed as OSA according to the predetermined criteria. It correlated with the apnea hypopnea index and 3% oxygen desaturation index. In the multivariate analysis, it was extracted as a factor defining the apnea hypopnea index ( = 0.663, = 0.003) and 3% oxygen saturation index ( = 0.637, = 0.008). Twenty-five patients could be identified as OSA. We developed the RRI analysis using the wearable heart rate sensor WHS-1 and a new algorithm, which may become an expeditious and cost-effective screening tool for identifying OSA.

摘要

阻塞性睡眠呼吸暂停(OSA)与心血管疾病高度相关,但大多数患者仍未被诊断出来。夜间会出现心率的周期性变化(CVHR),据报道,使用动态心电图进行R-R间期(RRI)分析有助于筛查OSA。我们研究了使用可穿戴心率传感器WHS-1和新开发的算法进行RRI分析以识别OSA的有效性。将WHS-1和多导睡眠图同时应用于30例OSA患者。通过使用为每个时间序列计算的RRI平均值,识别出伴有CVHR的心动过速。通过我们新开发的方法计算CVHR期间的积分RRI与整个睡眠时间的积分RRI之比。根据预定标准将患者诊断为OSA。它与呼吸暂停低通气指数和3%氧饱和度下降指数相关。在多变量分析中,它被提取为定义呼吸暂停低通气指数(=0.663,=0.003)和3%氧饱和度指数(=0.637,=0.008)的一个因素。25例患者可被识别为OSA。我们使用可穿戴心率传感器WHS-1和新算法开发了RRI分析,这可能成为一种快速且经济高效的OSA识别筛查工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3fe/7589311/7de87c0bbf99/jcm-09-03359-g001.jpg

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