Influence of Sex, Age and Body Mass Index on an Algorithm for Electrocardiogram-derived Respiratory Rate

Respiratory rate (RR) is an important biomarker of cardiopulmonary status. Its role is particularly evident in conditions like obstructive sleep apnea, which significantly increase risk of heart disease. Electrocardiogram (ECG)-derived RR is an emerging alternative to traditional RR measurement, which requires cumbersome and specialized equipment. Here, we developed a novel algorithm to estimate instantaneous RR using only single-lead body-surface ECG. We comprehensively tested the influence of sex, age, and body mass index on the efficacy of ECG-derived RR. ECG and RR waveforms were obtained from 50 patients enrolled in a polysomnography sleep study. Algorithm-based ECG-derived RR estimates were compared with reference RR measurements from the sleep study. A close linear correlation between the reference and algorithmic RR estimates was observed across the entire cohort of patients. The mean absolute RR estimation error was 0.8±0.9 breaths/min. Importantly, the algorithm's accuracy was independent of the patient's age, sex, and body habitus. Our algorithm provides a robust estimation of RR and holds promise for remote pulmonary assessment in patients with respiratory disorders.