ECG Morphological Changes Due to Age and Heart Rate Variability

The electrocardiogram (ECG) constitutes one of the most useful diagnostic tools for evaluating the overall health of the heart. ECG interpretation involves the assessment of various heartbeat interval features, as well as ECG wave morphological patterns and variations. However, electrocardiographic alterations are associated with a multitude of factors, which sometimes are difficult to differentiate. In this paper, we propose a system identification based methodology that quantifies the dynamic effects of heart rate variability (HRV) and age on the ECG morphology. Specifically, samples of the ECG waveform of healthy young and elderly subjects were modeled as linear and nonlinear autoregressive processes (AR). The effects of HRV were investigated by considering the HRV time-series as an exogenous input to the system. Age-related ECG wave alterations were also examined by statistically comparing the differences in the predictive performance of the AR models in the two groups.