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LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics — arXiv:2608.03690

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.03690
License (as archived): CC0-1.0
Archived: 2026-10-31T12:42:30.217Z
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How to cite

LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics — arXiv:2608.03690 — arXiv (en), https://arxiv.org/abs/2608.03690 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_156b4b341b44 (Metraipolis archive dc_156b4b341b44, sha256 fd19a1d304a92423c46a0ed2fa36a2069c6c4910f3073b76a251c28fee9fcb1a)

BibTeX

@misc{dc156b4b341b44,
  title = {LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics — arXiv:2608.03690},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.03690},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_156b4b341b44; sha256 fd19a1d304a92423c46a0ed2fa36a2069c6c4910f3073b76a251c28fee9fcb1a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.03690. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Point-of-care cardiac devices such as smartwatches and handheld ECG recorders typically capture 1--2 leads, yet existing ECG foundation models are architecturally constrained to fixed 12-lead inputs, degrading or failing under these reduced configurations. We introduce LAEF (Lead-Agnostic ECG Foundation), a 7M-parameter ECG foundation model that can natively process any lead subset without zero-padding or architectural modification. LAEF represents ECGs as variable-size spatiotemporal graphs with physiologically motivated intra- and inter-lead connectivity, processed by a Graph Attention Network that scales naturally with active lead this http URL -trained on 9.2M 12-lead ECGs via masked node modelling with stochastic lead sampling, LAEF learns representations robust to lead configuration. Across 18 downstream datasets, LAEF is on par with specialized 12-lead baselines over 12$\times$ larger at full lead availability. Under direct point-of-care-oriented diagnostics (1--2 leads), it outperforms all zero-padded alternatives on 17 out of 18 datasets with with a single randomly sampled lead and on 14 out of 18 with 2 leads, with an average AUROC gain of +3.2 points. Representation analysis links this advantage to architectural lead-agnosticism, and a lead-importance study across 164 cardiovascular conditions shows population-level performance is stable across single standard input leads while still recovering established clinically lead-condition associations.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-05 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.03690 — 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 2026-10-31T12:31:30.366Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).