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一种使用非接触式床传感器并具有谐波伪影抑制功能的时频呼吸跟踪系统。

A time-frequency respiration tracking system using non-contact bed sensors with harmonic artifact rejection.

作者信息

Beattie Zachary T, Jacobs Peter G, Riley Thomas C, Hagen Chad C

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:8111-4. doi: 10.1109/EMBC.2015.7320276.

DOI:10.1109/EMBC.2015.7320276
PMID:26738176
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4705551/
Abstract

Sleep apnea is a breathing disorder that affects many individuals and has been associated with serious health conditions such as cardiovascular disease. Clinical diagnosis of sleep apnea requires that a patient spend the night in a sleep clinic while being wired up to numerous obtrusive sensors. We are developing a system that utilizes respiration rate and breathing amplitude inferred from non-contact bed sensors (i.e. load cells placed under bed supports) to detect sleep apnea. Multi-harmonic artifacts generated either biologically or as a result of the impulse response of the bed have made it challenging to track respiration rate and amplitude with high resolution in time. In this paper, we present an algorithm that can accurately track respiration on a second-by-second basis while removing noise harmonics. The algorithm is tested using data collected from 5 patients during overnight sleep studies. Respiration rate is compared with polysomnography estimations of respiration rate estimated by a technician following clinical standards. Results indicate that certain subjects exhibit a large harmonic component of their breathing signal that can be removed by our algorithm. When compared with technician transcribed respiration rates using polysomnography signals, we demonstrate improved accuracy of respiration rate tracking using harmonic artifact rejection (mean error: 0.18 breaths/minute) over tracking not using harmonic artifact rejection (mean error: -2.74 breaths/minute).

摘要

睡眠呼吸暂停是一种影响许多人的呼吸障碍,并且与心血管疾病等严重健康状况相关。睡眠呼吸暂停的临床诊断要求患者在睡眠诊所过夜,同时连接到众多侵入性传感器。我们正在开发一种系统,该系统利用从非接触式床传感器(即放置在床架下方的称重传感器)推断出的呼吸频率和呼吸幅度来检测睡眠呼吸暂停。由生物因素产生或由于床的脉冲响应而产生的多谐波伪影使得在时间上以高分辨率跟踪呼吸频率和幅度具有挑战性。在本文中,我们提出了一种算法,该算法可以在去除噪声谐波的同时逐秒准确跟踪呼吸。该算法使用在夜间睡眠研究期间从5名患者收集的数据进行测试。将呼吸频率与技术人员按照临床标准估计的多导睡眠图呼吸频率估计值进行比较。结果表明,某些受试者的呼吸信号存在较大的谐波成分,可以通过我们的算法去除。与使用多导睡眠图信号由技术人员记录的呼吸频率相比,我们证明使用谐波伪影抑制跟踪呼吸频率的准确性(平均误差:0.18次/分钟)优于不使用谐波伪影抑制的跟踪(平均误差:-2.74次/分钟)。

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