Daily Arxiv

This page organizes papers related to artificial intelligence published around the world.
This page is summarized using Google Gemini and is operated on a non-profit basis.
The copyright of the paper belongs to the author and the relevant institution. When sharing, simply cite the source.

TolerantECG: A Foundation Model for Imperfect Electrocardiogram

Created by
  • Haebom

Author

Huynh Dang Nguyen, Trong-Thang Pham, Ngan Le, Van Nguyen

TolerantECG: ECG Foundation Model that is robust to noise and supports various lead sets

Outline

Electrocardiograms (ECGs) are essential tools for diagnosing cardiac disease, but diagnostic errors can occur due to noise or missing leads in standard 12-lead recordings. To address these issues, we propose TolerantECG, a foundation model for ECG signals that is robust to noise and can operate on any subset of the standard 12-lead ECG. TolerantECG training combines contrastive learning and self-supervised learning frameworks to simultaneously learn ECG signal representations, knowledge retrieval-based text report descriptions, and signals with damaged or missing leads.

Takeaways, Limitations

Takeaways:
It is robust to noise and shows excellent performance under various ECG signal conditions.
It showed the best or second best performance across various ECG signal conditions and class levels on the PTB-XL dataset.
Achieved the best performance on the MIT-BIH arrhythmia database.
It can also respond to missing leads in a 12-lead ECG.
Limitations:
The specific Limitations is not specified in the paper (however, further research and verification are required).
👍