Chon Ki H, Dash Shishir, Ju Kihwan
Department of Biomedical Engineering, State University of New York (SUNY) at Stony Brook, Stony Brook, NY 11794 USA.
IEEE Trans Biomed Eng. 2009 Aug;56(8):2054-63. doi: 10.1109/TBME.2009.2019766. Epub 2009 Apr 14.
We present a new method that uses the pulse oximeter signal to estimate the respiratory rate. The method uses a recently developed time-frequency spectral estimation method, variable-frequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform. This FM has a measurable periodicity, which provides an estimate of the respiration period. We compared the performance of VFCDM to the continuous wavelet transform (CWT) and autoregressive (AR) model approaches. The CWT method also utilizes the respiratory sinus arrhythmia effect as represented by either FM or AM to estimate respiratory rates. Both CWT and AR model methods have been previously shown to provide reasonably good estimates of breathing rates that are in the normal range (12-26 breaths/min). However, to our knowledge, breathing rates higher than 26 breaths/min and the real-time performance of these algorithms are yet to be tested. Our analysis based on 15 healthy subjects reveals that the VFCDM method provides the best results in terms of accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.3 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12-36 breaths/min.
我们提出了一种利用脉搏血氧仪信号来估计呼吸频率的新方法。该方法使用了一种最近开发的时频谱估计方法——可变频率复解调（VFCDM），来识别光电容积脉搏波波形的频率调制（FM）。这种FM具有可测量的周期性，可用于估计呼吸周期。我们将VFCDM的性能与连续小波变换（CWT）和自回归（AR）模型方法进行了比较。CWT方法也利用由FM或调幅（AM）表示的呼吸性窦性心律不齐效应来估计呼吸频率。先前已证明，CWT和AR模型方法都能对正常范围内（12 - 26次/分钟）的呼吸频率提供相当不错的估计。然而，据我们所知，高于26次/分钟的呼吸频率以及这些算法的实时性能尚未经过测试。我们基于15名健康受试者的分析表明，在提取12 - 36次/分钟变化的呼吸频率时，VFCDM方法在准确性（中位数误差更小）、一致性（中位数的四分位间距更小）和计算效率（使用MATLAB实现对1分钟数据的处理时间不到0.3秒）方面提供了最佳结果。
