Leonard Paul A, Douglas J Graham, Grubb Neil R, Clifton David, Addison Paul S, Watson James N
Department of Accident and Emergency Medicine, The Royal Hospital for Sick Children, Sciennes Rd, Edinburgh EH9 1LF, UK.
J Clin Monit Comput. 2006 Feb;20(1):33-6. doi: 10.1007/s10877-005-9007-7. Epub 2006 Mar 11.
To determine if an automatic algorithm using wavelet analysis techniques can be used to reliably determine respiratory rate from the photoplethysmogram (PPG).
Photoplethysmograms were obtained from 12 spontaneously breathing healthy adult volunteers. Three related wavelet transforms were automatically polled to obtain a measure of respiratory rate. This was compared with a secondary timing signal obtained by asking the volunteers to actuate a small push button switch, held in their right hand, in synchronisation with their respiration. In addition, individual breaths were resolved using the wavelet-method to identify the source of any discrepancies.
Volunteer respiratory rates varied from 6.56 to 18.89 breaths per minute. Through training of the algorithm it was possible to determine a respiratory rate for all 12 traces acquired during the study. The maximum error between the PPG derived rates and the manually determined rate was found to be 7.9%.
Our technique allows the accurate measurement of respiratory rate from the photoplethysmogram, and leads the way for developing a simple non-invasive combined respiration and saturation monitor.
确定一种使用小波分析技术的自动算法是否可用于从光电容积脉搏波描记图(PPG)可靠地确定呼吸频率。
从12名自主呼吸的健康成年志愿者身上获取光电容积脉搏波描记图。自动采集三种相关的小波变换以获得呼吸频率的测量值。将其与通过要求志愿者右手握住一个小按钮开关并与呼吸同步按下所获得的辅助计时信号进行比较。此外,使用小波方法解析各个呼吸,以识别任何差异的来源。
志愿者的呼吸频率在每分钟6.56次至18.89次之间变化。通过算法训练,可以确定研究期间采集的所有12条记录的呼吸频率。发现PPG得出的频率与手动确定的频率之间的最大误差为7.9%。
我们的技术能够从光电容积脉搏波描记图准确测量呼吸频率,并为开发一种简单的非侵入性呼吸与饱和度联合监测仪开辟了道路。