Uncovering ECG Changes during Healthy Aging using Explainable AI
Gabriel Ott Yannik Schaubelt Juan Miguel Lopez Alcaraz Wilhelm Haverkamp Nils Strodthoff
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.