Respiratory rate extraction from pulse oximeter and electrocardiographic recordings

Abstract

We present an algorithm of respiratory rate extraction using particle filter (PF), which is applicable to both photoplethysmogram (PPG) and electrocardiogram (ECG) signals. For the respiratory rate estimation, 1 min data are analyzed with combination of a PF method and an autoregressive model where among the resultant coefficients, the corresponding pole angle with the highest magnitude is searched since this reflects the closest approximation of the true breathing rate. The PPG data were collected from 15 subjects with the metronome breathing rate ranging from 24 to 36 breaths per minute in the supine and upright positions. The ECG data were collected from 11 subjects with spontaneous breathing ranging from 36 to 60 breaths per minute during treadmill exercises. Our method was able to accurately extract respiratory rates for both metronome and spontaneous breathing even during strenuous exercises. More importantly, despite slow increases in breathing rates concomitant with greater exercise vigor with time, our method was able to accurately track these progressive increases in respiratory rates. We quantified the accuracy of our method by using the mean, standard deviation and interquartile range of the error rates which all reflected high accuracy in estimating the true breathing rates. We are not aware of any other algorithms that are able to provide accurate respiratory rates directly from either ECG signals or PPG signals with spontaneous breathing during strenuous exercises. Our method is near real-time realizable because the computational time on 1 min data segment takes only 10 ms on a 2.66 GHz Intel Core2 microprocessor; the data are subsequently shifted every 10 s to obtain near-continuous breathing rates. This is an attractive feature since most other techniques require offline data analyses to estimate breathing rates.