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

TL;DR

This study uses explainable AI on ECG data to uncover age-related cardiac changes, highlighting the importance of raw signals and features like P-wave and SDANN in healthy aging.

Contribution

It introduces a combined deep-learning and tree-based analysis of ECG data across ages, emphasizing explainability and revealing novel age-related ECG insights.

Findings

Decline in inferred breathing rates with age

High SDANN values linked to elderly individuals

P-wave distribution changes across age groups

Abstract

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.