Abstract
Continuous monitoring of respiration is essential for early detection of critical illness. Current methods require sensors attached to the body and/or are not robust to subject motion. Alternative camera-based solutions have been presented using motion vectors and remote photoplethysmography. In this work, we present a non-contact camera-based method to detect respiration, which can operate in both visible and dark lighting conditions by detecting the respiratory-induced colour differences of the skin. We make use of the close similarity between skin colour variations caused by the beating of the heart and those caused by respiration, leading to a much improved signal quality compared to single-channel approaches. Essentially, we propose to find the linear combination of colour channels which suppresses the distortions best in a frequency band including pulse rate, and subsequently we use this same linear combination to extract the respiratory signal in a lower frequency band. Evaluation results obtained from recordings on healthy subjects which perform challenging scenarios, including motion, show that respiration can be accurately detected over the entire range of respiratory frequencies, with a correlation coefficient of 0.96 in visible light and 0.98 in infrared, compared to 0.86 with the best-performing non-contact benchmark algorithm. Furthermore, evaluation on a set of videos recorded in a Neonatal Intensive Care Unit (NICU) shows that this technique looks promising as a future alternative to current contact-sensors showing a correlation coefficient of 0.87.
Highlights
Monitoring of respiration is important in clinical care since it provides valuable information of a person’s health status
An overview of the results for the different guided breathing scenarios is visualized in Fig. 6 for both chrominance-based method (CHROM) and PBV
It should be mentioned that we evaluate our algorithm based on individual breath-intervals without post-processing, whereas many other algorithm, e.g. the RRoxi algorithm of Addison et al [36], use long time-windows and may average over previous estimates to arrive at a breathing rate
Summary
Monitoring of respiration is important in clinical care since it provides valuable information of a person’s health status. [3] assessed the performance of 314 algorithms for the estimation of RR from ECG and PPG waveforms under ideal operation conditions They showed that most time-domain techniques perform better compared to frequency-domain techniques. It should be noted that Charlton et al used single-channel contact PPG signals, and they benchmarked the algorithms under ideal operation conditions. Albeit their ability to measure respiration, most of these methods are cumbersome and can cause stress and discomfort to the patient. Photoplethysmography (PPG) uses this principle for the optical measurement of blood volume variations by capturing the reflected or transmitted light from/through the illuminated skin, resulting in a PPG waveform
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