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光电容积脉搏波信号的脉搏率变异分析中的离群值管理。

Outlier Management for Pulse Rate Variability Analysis from Photoplethysmographic Signals.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:649-652. doi: 10.1109/EMBC48229.2022.9871942.

Abstract

Pulse rate variability (PRV) has been proposed as a surrogate for the estimation of Heart Rate Variability (HRV), which is a non-invasive technique used to assess the cardiac autonomic activity. However, both physiological and technical factors may affect the relationship between HRV and PRV, and there are no standards for the analysis of PRV from photoplethysmographic (PPG) signals. The aim of this study was to determine the best outlier management strategies for PRV analysis. 117 PPG signals with randomly generated PRV information were simulated using Gaussian signals. From these, interbeat intervals were detected and different outlier detection and correction techniques were applied. Time and frequency-domain and non-linear PRV indices were extracted and compared with respect to the gold standard values obtained from the simulated PRV information. The results show that, in good quality PPG signals, there is no need to apply any outlier management technique for the extraction of PRV information. Clinical relevance- Establishing guidelines for PRV mea-surement can lead to more reliable and comparable results, as well as to the increase in the use of this variable for the diagnosis and monitoring of cardiovascular and autonomic conditions.

摘要

脉搏率变异性(PRV)已被提议作为心率变异性(HRV)估计的替代方法,HRV 是一种用于评估心脏自主活动的非侵入性技术。然而,生理和技术因素都可能影响 HRV 和 PRV 之间的关系,并且对于从光体积描记(PPG)信号中分析 PRV 还没有标准。本研究旨在确定 PRV 分析的最佳异常值管理策略。使用高斯信号模拟了 117 个具有随机生成的 PRV 信息的 PPG 信号。从这些信号中,检测到了心搏间期,并应用了不同的异常值检测和校正技术。提取了时频域和非线性 PRV 指数,并与从模拟 PRV 信息中获得的金标准值进行了比较。结果表明,在高质量的 PPG 信号中,提取 PRV 信息不需要应用任何异常值管理技术。临床意义- 为 PRV 测量制定指南可以带来更可靠和可比的结果,并增加该变量在心血管和自主条件的诊断和监测中的使用。

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