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ecg-aging

Source code repository for the study: "Uncovering ECG Changes during Healthy Aging using Explainable AI"

https://github.com/ai4healthuol/ecg-aging

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: plos.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.2%) to scientific vocabulary

Keywords

ecg ecg-classification healthy-aging
Last synced: 6 months ago · JSON representation ·

Repository

Source code repository for the study: "Uncovering ECG Changes during Healthy Aging using Explainable AI"

Basic Info
  • Host: GitHub
  • Owner: AI4HealthUOL
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 2.44 MB
Statistics
  • Stars: 13
  • Watchers: 1
  • Forks: 3
  • Open Issues: 0
  • Releases: 0
Topics
ecg ecg-classification healthy-aging
Created over 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

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.

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

```bibtex @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} }

```

Owner

  • Name: AI4HealthUOL
  • Login: AI4HealthUOL
  • Kind: organization
  • Location: Germany

Public repositories of the AI4Health Division at Oldenburg University

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Ott"
  given-names: "Gabriel"
- family-names: "Schaubelt"
  given-names: "Yannik"
- family-names: "Lopez Alcaraz"
  given-names: "Juan Miguel"
- family-names: "Haverkamp"
  given-names: "Wilhelm"
- family-names: "Strodthoff"
  given-names: "Nils"
title: "ECG-aging"
version: 1.0.0
doi: 10.5281/zenodo.1234
date-released: 2023-10-11
url: "https://github.com/AI4HealthUOL/ECG-aging"

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