Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) 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. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) 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.

翻译：心血管疾病仍是全球最主要的致死原因。年龄作为重要协变量，其影响最宜在健康人群中研究，以正确区分年龄相关变化与疾病相关变化。传统上，此类洞见多源于分析个体衰老过程中的心电图特征变化。然而，这些特征虽具信息价值，却可能掩盖数据中的潜在关联。本文贡献如下：（1）采用深度学习和基于树模型的方法，分析涵盖各年龄段的健康人群强健数据集中的原始信号与心电图特征数据；（2）利用可解释人工智能方法识别跨年龄组最具判别力的心电图特征；（3）基于树模型的分类器分析揭示了推断呼吸频率随年龄增长而下降，并识别出SDANN值异常升高是区分老年人与年轻成年人的显著指标；（4）深度学习模型进一步凸显P波在所有年龄组年龄预测中的关键作用，提示不同P波类型分布可能随年龄变化。这些发现为年龄相关心电图变化提供了新视角，超越了传统基于特征的分析方法。