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MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning — arXiv:2610.07662

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07662
License (as archived): CC0-1.0
Archived: 2026-10-07T11:41:19.527Z
SHA-256 of the archived text: c6ee47588f97d1de427749cf56bdb7f09d9cf35dada69f46e29f0584597ceb6d
Stable URL of this page: https://metraipolis.com/documento/dc_4f3104f104ee

How to cite

MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning — arXiv:2610.07662 — arXiv (en), https://arxiv.org/abs/2610.07662 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4f3104f104ee (Metraipolis archive dc_4f3104f104ee, sha256 c6ee47588f97d1de427749cf56bdb7f09d9cf35dada69f46e29f0584597ceb6d)

BibTeX

@misc{dc4f3104f104ee,
  title = {MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning — arXiv:2610.07662},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07662},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4f3104f104ee; sha256 c6ee47588f97d1de427749cf56bdb7f09d9cf35dada69f46e29f0584597ceb6d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07662. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation models have demonstrated promising results, but are limited by a reliance on ECG interpretation reports as their sole supervision. Because interpretation reports only capture the subset of waveform information routinely recognized by clinicians, this constrains representation learning to overlook the broader diagnostic signals present in ECG. We introduce a new ECG foundation model --- MS-ECG-FM --- that is trained through contrastive alignment to multiple distinct clinical note types, including ECG, echocardiography, radiology, and discharge reports. We evaluate MS-ECG-FM on an extended set of ECG detection benchmarks, showing that it comprehensively outperforms existing methods on the full span of conditions that ECG can detect, including in reduced-lead configurations. Different reports improve representations for different diagnostic domains, while multi-source alignment captures their complementary information and produces consistently strong representations across clinically diverse tasks.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.07662 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:19:24.779Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).