可穿戴设备在健康受试者和瓣膜性心脏病患者24小时呼吸模式分析中的定量方法及初步应用

Jiachen Wang, Zhicheng Yang, Yuqiang Wang, Chenbin Ma, Jian Zhang, Peng-ming Yu, Ying-qiang Guo, Zheng Zhang
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引用次数: 0

摘要

24小时呼吸模式可能与健康状况和疾病进展密切相关。然而,对于基于可穿戴设备监测的24小时呼吸信号的潜在价值挖掘,目前尚无一致且被广泛接受的方法。本研究提出了一种基于可穿戴连续生理参数监测系统的24小时呼吸模式分析参考方法,包括信号质量评估、潮气量校准和呼吸模式参数,包括时域、频域和非线性域。70名健康受试者和76名接受心脏瓣膜手术的患者参加了本研究。呼吸模式的正常参考范围以健康受试者为基础计算。根据患者是否发生术后肺部并发症(PPCs)进行亚组研究。与非PPCs组相比,PPCs组平卧位呼吸频率变异系数较小。在白天,PPCs组呼吸频率峰度和腹部贡献较小。夜间,PPCs组呼吸频率和SD2变异系数较小。本研究提出的定量方法填补了24小时呼吸模式量化领域的空白,可有效区分不同人群,有望在COVID-19疫情背景下得到广泛应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Quantitative Approach and Preliminary Application in Healthy Subjects and Patients with Valvular Heart Disease for 24-h Breathing Patterns Analysis Using Wearable Devices
The 24-h breathing patterns may be closely related to health status as well as disease progression. However, there is no consistent and widely accepted approach for mining the potential value in 24-h respiratory signals based on wearable device monitoring. This study presented a reference approach including signal quality assessment, calibration of tidal volume, and breathing patterns parameters based on a wearable continuous physiological parameter monitoring system for 24-h breathing patterns analysis, including time domain, frequency domain and nonlinear domain. 70 healthy subjects and 76 patients undergoing heart valve surgery were enrolled in this study. The normal reference range of breathing patterns was calculated based on healthy subjects. A subgroup study was conducted based on whether patients developed postoperative pulmonary complications (PPCs). Compared with non-PPCs group, the coefficient of variation of breathing rate in the recumbent position was smaller in the PPCs group. During the daytime, the kurtosis of breathing rate and contribution of the abdomen was smaller in PPCs group. During the nighttime, the coefficient of variation of breathing rate and SD2 was smaller in the PPCs group. The quantitative method proposed in this study fills the gap in the field of quantifying 24-h breathing patterns which is effective in discriminating different populations and is expected to be used widely in the context of COVID-19 epidemic.
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