Uncovering ECG Changes during Healthy Aging using Explainable AI

This is the official repository for the paper Uncovering ECG Changes during Healthy Aging using Explainable AI accepted by PLOS ONE. The research uncovers healthy age-related ECG changes by analyzing ECG data from diverse age groups using diverse models such as deep learning and tree-based classifiers, as well as model explainability.

Please cite our publication if you found our research to be helpful.

@article{ott2024using,
  title={Using explainable AI to investigate electrocardiogram changes during healthy aging—From expert features to raw signals},
  author={Ott, Gabriel and Schaubelt, Yannik and Lopez Alcaraz, Juan Miguel and Haverkamp, Wilhelm and Strodthoff, Nils},
  journal={Plos one},
  volume={19},
  number={4},
  pages={e0302024},
  year={2024},
  publisher={Public Library of Science San Francisco, CA USA}
}