A Robust Method for ECG-Based Estimation of the Respiratory Frequency During Stress Testing

TLDR

ECG recordings during stress testing are highly nonstationary and exhibit QRS morphology changes, rendering conventional respiratory frequency estimation methods unreliable. The study proposes a robust ECG‑based method to estimate respiratory frequency during stress testing. The method extracts rotation angles of the heart’s electrical axis from ECGs, applies power spectral analysis, and incorporates robust techniques to handle nonstationarity, validated on simulated data and ECG/airflow recordings from volunteers and patients. The method achieves a mean error of 0.5 % (±0.2 %) on simulated signals and 5.9 % (±4 %) compared to airflow measurements, indicating high suitability for noisy ECGs during stress testing.

Abstract

A robust method is presented for electrocardiogram (ECG)-based estimation of the respiratory frequency during stress testing. Such ECGs contain highly nonstationary noise and exhibit changes in QRS morphology which, when combined with the dynamic nature of the respiratory frequency, make most existing methods break down. The present method exploits the oscillatory pattern of the rotation angles of the heart's electrical axis as induced by respiration. The series of rotation angles, obtained from least-squares loop alignment, is subject to power spectral analysis and estimation of the respiratory frequency. Robust techniques are introduced to handle the nonstationary properties of exercise ECGs. The method is evaluated by means of both simulated signals, and ECG/airflow signals recorded from 14 volunteers and 20 patients during stress testing. The resulting respiratory frequency estimation error is, for simulated signals, equal to 0.5% /spl plusmn/ 0.2%, mean /spl plusmn/ SD (0.002 /spl plusmn/ 0.001 Hz), whereas the error between respiratory frequencies of the ECG-derived method and the airflow signals is 5.9% /spl plusmn/ 4% (0.022 /spl plusmn/ 0.016 Hz). The results suggest that the method is highly suitable for analysis of noisy ECG signals recorded during stress testing.