Cardiovascular diseases remain the leading global cause of mortality. This
necessitates a profound understanding of heart aging processes to diagnose
constraints in cardiovascular fitness. Traditionally, most of such insights
have been drawn from the analysis of electrocardiogram (ECG) feature changes of
individuals as they age. However, these features, while informative, may
potentially obscure underlying data relationships. In this paper, we employ a
deep-learning model and a tree-based model to analyze ECG data from a robust
dataset of healthy individuals across varying ages in both raw signals and ECG
feature format. Explainable AI techniques are then used to identify ECG
features or raw signal characteristics are most discriminative for
distinguishing between age groups. Our analysis with tree-based classifiers
reveal age-related declines in inferred breathing rates and identifies notably
high SDANN values as indicative of elderly individuals, distinguishing them
from younger adults. Furthermore, the deep-learning model underscores the
pivotal role of the P-wave in age predictions across all age groups, suggesting
potential changes in the distribution of different P-wave types with age. These
findings shed new light on age-related ECG changes, offering insights that
transcend traditional feature-based approaches.

心血管疾病是全球死因的主要原因。本研究使用深度学习模型和基于树的模型分析了健康个体的心电图数据，发现年龄组之间的呼吸速率与 SDANN 值等特征有所差异，并且强调了 P 波在年龄预测中的关键作用，揭示了与年龄相关的心电图变化，并提供了超越传统特征方法的新见解。