TolerantECG: A Foundation Model for Imperfect Electrocardiogram

• URL: http://arxiv.org/abs/2507.09887v2
• Date: Tue, 29 Jul 2025 06:12:37 GMT
• Title: TolerantECG: A Foundation Model for Imperfect Electrocardiogram
• Authors: Huynh Dang Nguyen, Trong-Thang Pham, Ngan Le, Van Nguyen,
• Abstract summary: TolerantECG is a foundation model for ECG signals that is robust to noise and capable of functioning with arbitrary subsets of the standard 12-lead ECG.<n>TolerantECG training combines contrastive and self-supervised learning frameworks to jointly learn ECG signal representations.<n> benchmarking results demonstrate that TolerantECG consistently ranks as the best or second-best performer across various ECG signal conditions.
• Score: 6.8878798499351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The electrocardiogram (ECG) is an essential and effective tool for diagnosing heart diseases. However, its effectiveness can be compromised by noise or unavailability of one or more leads of the standard 12-lead recordings, resulting in diagnostic errors or uncertainty. To address these challenges, we propose TolerantECG, a foundation model for ECG signals that is robust to noise and capable of functioning with arbitrary subsets of the standard 12-lead ECG. TolerantECG training combines contrastive and self-supervised learning frameworks to jointly learn ECG signal representations alongside their corresponding knowledge-retrieval-based text report descriptions and corrupted or lead-missing signals. Comprehensive benchmarking results demonstrate that TolerantECG consistently ranks as the best or second-best performer across various ECG signal conditions and class levels in the PTB-XL dataset, and achieves the highest performance on the MIT-BIH Arrhythmia Database.

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