An optimized method for the estimation of the respiratory rate from electrocardiographic signals: implications for estimating minute ventilation

It is well-known that respiratory activity influences electrocardiographic (ECG) morphology. In this article we present a new algorithm for the extraction of respiratory rate from either intracardiac or body surface electrograms. The algorithm optimizes selection of ECG leads for respiratory analysis, as validated in a swine model. The algorithm estimates the respiratory rate from any two ECG leads by finding the power spectral peak of the derived ratio of the estimated root-mean-squared amplitude of the QRS complexes on a beat-by-beat basis across a 32-beat window and automatically selects the lead combination with the highest power spectral signal-to-noise ratio. In 12 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the right ventricle, coronary sinus, left ventricle, and epicardial surface, as well as body surface electrograms, while the ventilation rate was varied between 7 and 13 breaths/min at tidal volumes of 500 and 750 ml. We found excellent agreement between the estimated and true respiratory rate for right ventricular ( R2 = 0.97), coronary sinus ( R2 = 0.96), left ventricular ( R2 = 0.96), and epicardial ( R2 = 0.97) intracardiac leads referenced to surface lead ECGII. When applied to intracardiac right ventricular-coronary sinus bipolar leads, the algorithm exhibited an accuracy of 99.1% ( R2 = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% ( R2 = 0.93). In conclusion, the proposed algorithm provides an accurate estimation of the respiratory rate using either intracardiac or body surface signals without the need for additional hardware.