Estimating the real-time respiratory rate from the ECG with a bank of notch filters

The respiratory rate is an important vital sign that needs to be monitored continuously in clinical and non-clinical health monitoring applications. It is commonly estimated from electrocardiogram (ECG)-derived respiratory waveforms such as the respiratory sinus arrhythmia (RSA) and the ECG R peak amplitudes (RPA). Current methods combine respiratory information from these two waveforms but produce large delays in estimating the respiratory rate. In this work, the powers of the outputs of a bank of order-3 FIR notch filters were used in a recursive scheme to estimate in real-time, and with a small delay, the respiratory rate from the RSA and the RPA waveforms simultaneously. The algorithm was tested on the public Physionet Fantasia data set and compared to the state-of-the-art in terms of estimation accuracy and delay. It was shown that the proposed method provides more accurate estimates with smaller delays than those of the state-of-the-art.