ArticlePloS one2024
Using explainable AI to investigate electrocardiogram changes during healthy aging-From expert features to raw signals.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Artificial intelligence-predicted ECG age gap as a biomarker: bias-adjusted correlation with mortality and cardiovascular risk factors.European heart journal. Digital health · 2026Article
- Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning.Scientific reports · 2025Article
- AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients.European heart journal. Digital health · 2025Article
- Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study.Cardio-oncology (London, England) · 2025Article
- Explainable AI in early autism detection: a literature review of interpretable machine learning approaches.Discover mental health · 2025Review
- Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach.EClinicalMedicine · 2025Article
- Explainable and externally validated machine learning for neurocognitive diagnosis via ECGs.General psychiatry · 2025Article
- Depression diagnosis from patient interviews using multimodal machine learning.Frontiers in psychiatry · 2025Article
- Association between deep neural network-derived electrocardiographic-age and incident stroke.Frontiers in cardiovascular medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.