智能手机摄像头采集的脉冲光容积脉搏波信号,用于计算呼吸频率

J. Lázaro, Yunyoung Nam, E. Gil, P. Laguna, K. Chon
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引用次数: 11

摘要

提出了一种从智能手机摄像头采集的脉冲光容积脉搏波(SCPPG)信号中提取呼吸频率的方法。它结合了基于脉宽、幅度和速率变异性(PWV、PAV和PRV)的三种衍生呼吸信号的信息。在一个包含10名健康受试者在控制呼吸实验期间记录的SCPPG信号的数据库上进行评估,频率为0.2至0.6 Hz,步长为0.1 Hz,使用iPhone 4S设备。结果表明,PWV和PRV两种方法均可从SCPPG信号中估计出0.2 ~ 0.4 Hz的习惯性自发呼吸频率,相对误差较小(中位数为0.5%,IQR为2%)。PWV方法可以在0.5 Hz的频率下保持其性能,并且可以通过与其他方法(如PRV和PAV)相结合来提高精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smartphone-camera-acquired pulse photoplethysmographic signal for deriving respiratory rate
A method for deriving respiratory rate from smartphone-camera-acquired pulse photoplethysmographic (SCPPG) signal is presented. It combines information from three derived respiration signals based on pulse width, amplitude, and rate variability (PWV, PAV, and PRV). Evaluation is performed over a database containing SCPPG signals recorded from 10 healthy subjects during controlled respiration experiments at rates from 0.2 to 0.6 Hz with a step of 0.1 Hz, using iPhone 4S device. Results suggest that habitual spontaneous respiratory rates (0.2-0.4 Hz) can be estimated from SCPPG signals by PWV and by PRV with low relative error (median of order 0.5% and IQR of order 2%). PWV method maintained its performance at rates up to 0.5 Hz, and the accuracy can be improved by combining it with other methods such as PRV and PAV.
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