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

arXiv:2310.07463v2 Announce Type: replace-cross
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 …

abstract age aging arxiv cs.lg disease diseases eess.sp expert explainable ai features global insights mortality raw stat.ml type