Assessment of Respiratory Dynamics from ECG during Physical Exertion

Abstract

Background: Monitoring respiratory dynamics during physical exertion is crucial for assessing cardiopulmonary performance, particularly in fields like exercise physiology, sports science, and clinical rehabilitation. Traditional methods of monitoring respiration typically depend on specialized sensors, which can be inconvenient during active movement. ECG-Derived Respiration (EDR) provides a non-invasive option by obtaining respiratory data from electrocardiogram signals.
Purpose/Objective: This study intends to evaluate respiratory dynamics at the onset of physical activity through the use of EDR. Important respiratory metrics, including respiration rate, variations in signal amplitude, and rhythm, are evaluated from ECG recordings.
Methods: ECG signals were collected from healthy subjects during the first grade of treadmill exercise. A hybrid signal processing approach was applied, combining wavelet transform for decomposing the ECG into relevant frequency bands and central moment analysis for capturing respiratory-induced morphological variations. The derived respiratory signals were used to estimate key parameters and were validated against reference data from a thermistor based respiratory sensor.
Results: The derived respiratory signals exhibit a steady increase in respiratory rate and noticeable ECG waveform modulation while active. The central moment method is superior to the wavelet approach at capturing fine-grained respiratory signal changes, especially during low-intensity stress. We have shown that the proposed method works well, based on the strong correlation of predicted parameters with reference measurements.
Conclusion: This study presents evidence for the application of ECG-derived respiration for non-invasive monitoring of respiration during strenuous activity. The central moment method performed the best in the evaluation process and is likely the best method for real-time processing situations where accuracy and clarity of the signal will benefit from the additional frequency of the signal. This technique will be applicable to wearable health monitoring devices, sports and exercise physiology settings, early detection of cardiopulmonary stress, and all requiring minimal sensor hardware.