Spectral fusion for estimating respiratory rate from the ECG

A new method for extracting respiratory signals from the electrocardiogram (ECG) is proposed. The method performs AR spectral analysis on heart rate variability and beat morphology information extracted from the ECG and identifies the closest matched frequencies which then provide an estimate of the respiration frequency. Fusing frequency information from different sources reliably rejects noise and movement-induced artefact and is promising for application to ambulatory hospital data. The performance of the method was validated on two databases of simultaneously recorded ECG and reference respiration signals. The spectral fusion technique is found to correctly estimate respiratory rate 90% of the time in the case of non-ambulatory data and 86% of the time in the case of ambulatory data with a root mean square error of 0.92 and 1.40 breaths per minute, respectively.