Respiratory Rate Estimation from a Single Lead ECG Obtained During Dental Surgery

Respiratory rates were estimated from single lead ECG measurements, obtained on 30 patients during dental surgery, by using the Toolbox of Respiratory Rate Algorithms, from the Respiratory Rate Estimation (RRest) project. The purpose was to find the best performing among more than 300 algorithms implemented in the toolbox. Since the dataset does not contain a reference respiratory signal, respiratory rate extraction algorithms were compared with respect to the mean respiratory rate calculated from applying all the algorithms. The investigation found one algorithm that estimates respiratory rates within tolerance of $\pm \mathbf{2}$ breaths/min in at least 50% of the recordings’ lengths, for 27 of the total 30 measurements. The selected algorithm has not satisfied the criteria on 3 patients, possibly because of the big variation in the shape of the recorded ECGs, but also because the patch ECG device was not in the position where ECG amplitude modulation, caused by respiration, is expected. The selected algorithm is the smart fusion of respiratory rates obtained by peak detection and zero-crossings of respiratory signals, that are calculated from ECG features.