Data fusion for estimating respiratory rate from a single-lead ECG

Respiratory rate, an important antecedent of patient deterioration, is inadequately recorded by hospital staff, partially due to the absence of a reliable automated technique for measuring it. The ECG has been proposed by several authors in recent years as a source of reliable respiratory information. Most algorithms proposed use either respiratory sinus arrhythmia (RSA) or the R-peak amplitude (RPA) modulation of the ECG. In this paper, we propose a novel method for estimating respiratory rate from the ECG which fuses frequency information from the two methods. The method was evaluated on data from 40 young and elderly subjects and validated against a “gold standard” respiratory rate obtained from simultaneously recorded respiration data. The fusion method outperformed the RSA and RPA methods, giving a mean absolute error of 0.81 bpm for the young subject population and 0.84 bpm for the elderly, using 1-min windows of data. Unlike other algorithms, the technique does not underperform at the lower or higher respiratory rates.

► We fuse RSA and R-amplitude ECG information for deriving a respiratory rate.
► We validate against simultaneously recorded respiration data.
► Fusion method outperforms both RSA and R-amplitude methods.
► Mean absolute error is 0.81 bpm for the young and 0.84 bpm for the elderly population.
► The technique does not under-perform at the lower and higher respiratory rates.