ECG-derived respiration for ambulatory monitoring

Respiration is an important physiological signal for the monitoring and diagnosis of different conditions. However, a respiratory sensor is rarely included in ambulatory systems. Hence, several studies have focused on the computation of the so-called ECG-derived respiration (EDR). This research evaluates four different EDR algorithms on ECG signals that contain non-stationarities and noise. Two of these algorithms are based on the amplitude of the R-peak, and two are based on principal component analysis. To evaluate how well each of these algorithms estimates the respiration, three physionet datasets were used, and correlation, coherence, and a measure of cardiorespiratory coupling were used as indices for this evaluation. It was found that the simplest algorithm, namely the R-peak amplitude, was less sensitive to noise. In addition, no significant differences were found between the cardiorespiratory coupling derived with this easy-to-compute EDR and the real respiratory signal. This is great news for ambulatory applications, since the simplest algorithm can accurately estimate respiratory information.