Analyzing cardiac physiology: ECG ensemble averaging and morphological features under treadmill-induced stress in LabVIEW

This study uses a LabVIEW-based platform to analyze ECG signals in-depth in order to examine the long-term effects of exercise-induced stress on cardiac function. About 25 human subjects participated in a standardized treadmill exercise program that was continued until voluntary exertion. Blood pressure (BP) and heart rate (HR) were measured three times: while at rest, right after exercise, and five minutes after recovery. To assess myocardial workload, the rate-pressure product (RPP) was computed at each stage.Under all circumstances, continuous ECG data were recorded, and a specially created LabVIEW interface was used to analyze the waveforms. Important morphological characteristics, such as intervals and segments, as well as P-wave, QRS complex, and T-wave amplitudes, were extracted. R-R interval detection was used to segment each ECG cycle, and multiple cardiac cycles were aligned before being averaged as a group. This method made precise morphological analysis possible by greatly improving R-peak clarity and lowering noise.R-peak amplitude, QRS duration stability, and T-wave morphology all showed steady improvements over the course of a five-week observational period, suggesting improved cardiac efficiency and recovery adaptation. Waveform variability was significantly reduced, according to amplitude variance analysis conducted before and after averaging. In order to evaluate repolarization abnormalities, derived ratios like R-Q/S-Q/HR and T-Q/R-Q/HR were also examined; trends indicated that exercise conditioning caused normalized repolarization. The signal processing approach demonstrated its dependability in ECG analysis with an overall feature detection accuracy of 90 to 93%.Particularly in the contexts of cardiac rehabilitation, exercise physiology, and preventive cardiovascular screening, the suggested methodology provides a reliable, non-invasive way to track changes in cardiac function. Its use could include ongoing health monitoring in practical contexts and customized healthcare systems.