mlsanityData to 14 Oct 2026

TolerantECG: A Foundation Model for Imperfect Electrocardiogram

arXiv 2507.09887 ↗First posted 14 Jul 2025cs.LG cs.AI eess.SPcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 37 references that are in the map. First posted on arXiv 14 Jul 2025.

Builds on

14 of 37 references.

References · 14

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  11. 2024Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancementcited by 67
  12. 2024ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Textcited by 26
2 more
  1. 2024ECG Signal Denoising Using Multi-scale Patch Embedding and Transformerscited by 3
  2. 2024An Efficient Algorithm Based on Wavelet Transform to Reduce Powerline Noise From Electrocardiogramscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.