An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram

ObjectivesOver 100 algorithms have been proposed to estimate respiratory rate (RR) from the electrocardiogram (ECG) and photoplethysmogram (PPG). As they have never been compared systematically it is unclear which algorithm performs the best. Our primary aim was to determine how closely algorithms agreed with a gold standard RR measure when operating under ideal conditions. Secondary aims were: (i) to compare algorithm performance with IP, the clinical standard for continuous respiratory rate measurement in spontaneously breathing patients; (ii) to compare algorithm performance when using ECG and PPG; and (iii) to provide a toolbox of algorithms and data to allow future researchers to conduct reproducible comparisons of algorithms.MethodsAlgorithms were divided into three stages: extraction of respiratory signals, estimation of RR, and fusion of estimates. Several interchangeable techniques were implemented for each stage. Algorithms were assembled using all possible combinations of techniques, many of which were novel. After verification on simulated data, algorithms were tested on data from healthy participants. RRs derived from ECG, PPG and IP were compared to reference RRs obtained using a nasal-oral pressure sensor using the limits of agreement (LOA) technique.Results314 algorithms were assessed. Of these, 270 could operate on either ECG or PPG, and 44 on only ECG. The best algorithm had 95% LOAs of −4.7 to 4.7 bpm and a bias of 0.0 bpm when using the ECG, and −5.1 to 7.2 bpm and 1.0 bpm when using PPG. IP had 95% LOAs of −5.6 to 5.2 bpm and a bias of −0.2 bpm. Four algorithms operating on ECG performed better than IP.ConclusionsThe best ECG-based RR algorithms were found to have similar performance to the current clinical standard for electronic RR measurement. All high-performing algorithms consisted of novel combinations of time domain RR estimation and modulation fusion techniques. Algorithms performed better when using ECG than PPG. The toolbox of algorithms and data used in this study are publicly available at http://peterhcharlton.github.io/RRest ; the full text is in Physiological Measurement, DOI: 10.1088/0967-3334/37/4/610 .