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Cardiovascular diseases remain the leading global cause of mortality. Age isan important covariate whose effect is most easily investigated in a healthycohort to properly distinguish the former from disease-related changes.Traditionally, most of such insights have been drawn from the analysis ofelectrocardiogram (ECG) feature changes in individuals as they age. However,these features, while informative, may potentially obscure underlying datarelationships. In this paper we present the following contributions: (1) Weemploy a deep-learning model and a tree-based model to analyze ECG data from arobust dataset of healthy individuals across varying ages in both raw signalsand ECG feature format. (2) We use explainable AI methods to identify the mostdiscriminative ECG features across age groups.(3) Our analysis with tree-basedclassifiers reveals age-related declines in inferred breathing rates andidentifies notably high SDANN values as indicative of elderly individuals,distinguishing them from younger adults. (4) Furthermore, the deep-learningmodel underscores the pivotal role of the P-wave in age predictions across allage groups, suggesting potential changes in the distribution of differentP-wave types with age. These findings shed new light on age-related ECGchanges, offering insights that transcend traditional feature-based approaches.

Using explainable AI to investigate electrocardiogram changes during healthy aging -- from expert features to raw signals