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使用非侵入性光纤传感器的基于偏斜持续时间的全局指标评估睡眠呼吸暂停。

Sleep apnea assessment using declination duration-based global metrics from unobtrusive fiber optic sensors.

机构信息

Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan. These authors contributed equally to this work.

出版信息

Physiol Meas. 2019 Jul 30;40(7):075005. doi: 10.1088/1361-6579/ab21b5.

Abstract

OBJECTIVE

Sufficient sleep helps to restore the immune, nervous and cardiovascular systems, but is sometimes disturbed by sleep apnea (SA). The early diagnosis of sleep apnea is beneficial for the prevention of diseases. Polysomnography (PSG) recording provides comprehensive data for such assessment, but is not suitable for use at home due to discomfort during measurement and the difficulty of identification. This study proposes an unobtrusive measurement process by placing fiber optic sensors (FOSs) in a pillow (head-neck) or a bed mattress (thoracic-dorsal).

APPROACH

We test two approaches: drop degrees from the baseline to validate the capability of catching respiratory drops, and linear regression models based on a new global measure, the percentage of the total duration of respiratory declination (PTDRD), to estimate the hand-scored apnea/hypopnea index (AHI).

MAIN RESULTS

Based on data recorded from 63 adults, the drop degrees derived from respiratory signals exhibited statistical differences among central sleep apnea (CSA), obstructive sleep apnea (OSA) and normal breathing. The regression models based on the PTDRDs derived from head-neck FOS and thoracic-dorsal FOS also achieved good agreement with manually scored AHIs in Bland-Altman plots as well as oronasal airflow and thoracic wall movement.

SIGNIFICANCE

The aforementioned performance demonstrates the capability of the FOS measurement and the efficacy of the PTDRD metrics for SA assessment.

摘要

目的

充足的睡眠有助于恢复免疫系统、神经系统和心血管系统,但有时会被睡眠呼吸暂停(SA)打断。早期诊断睡眠呼吸暂停有助于预防疾病。多导睡眠图(PSG)记录为这种评估提供了全面的数据,但由于测量时的不适和识别的困难,不适合在家中使用。本研究提出了一种非侵入性的测量方法,即在枕头(头颈部)或床垫(胸部-背部)中放置光纤传感器(FOS)。

方法

我们测试了两种方法:从基线下降的度数来验证捕捉呼吸下降的能力,以及基于新的全局测量指标(呼吸下降的总持续时间百分比(PTDRD))的线性回归模型来估计手动评分的呼吸暂停/低通气指数(AHI)。

主要结果

基于 63 名成年人记录的数据,来自呼吸信号的下降度数在中枢性睡眠呼吸暂停(CSA)、阻塞性睡眠呼吸暂停(OSA)和正常呼吸之间表现出统计学差异。基于头颈部 FOS 和胸部-背部 FOS 得出的 PTDRD 的回归模型,在 Bland-Altman 图以及口鼻气流和胸壁运动方面,与手动评分的 AHI 也具有良好的一致性。

意义

上述性能表明了 FOS 测量的能力以及 PTDRD 指标在 SA 评估中的有效性。

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