Real-World Challenges in Leveraging Electrocardiograms for Coronary Artery Disease Classification
Abstract
This work investigates coronary artery disease (CAD) prediction from electrocardiogram (ECG) data taking into account different windows with respect to the time of diagnosis. We report that ECG waveform measurements automatically collected during ECG recordings contain sufficient features for good classification of CAD using machine learning models up to five years before diagnosis. On the other hand, convolutional neural networks trained on the ECG signals themselves appear to best extract CAD related features when processing data collected one year after a diagnosis is made. Through this work we demonstrate that the type of ECG data and the time window with respect to diagnosis should guide model selection.
Cite
Text
De Freitas et al. "Real-World Challenges in Leveraging Electrocardiograms for Coronary Artery Disease Classification." NeurIPS 2022 Workshops: TS4H, 2022.Markdown
[De Freitas et al. "Real-World Challenges in Leveraging Electrocardiograms for Coronary Artery Disease Classification." NeurIPS 2022 Workshops: TS4H, 2022.](https://mlanthology.org/neuripsw/2022/freitas2022neuripsw-realworld/)BibTeX
@inproceedings{freitas2022neuripsw-realworld,
title = {{Real-World Challenges in Leveraging Electrocardiograms for Coronary Artery Disease Classification}},
author = {De Freitas, Jessica Karina and Charney, Alexander and Landi, Isotta},
booktitle = {NeurIPS 2022 Workshops: TS4H},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/freitas2022neuripsw-realworld/}
}