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

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Computer Science > Machine Learning

arXiv:2608.03690 (cs)
[Submitted on 4 Aug 2026]

Title:LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics

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Abstract: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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2608.03690 [cs.LG]
  (or arXiv:2608.03690v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.03690
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Edoardo Coppola [view email]
[v1] Tue, 4 Aug 2026 13:58:10 UTC (1,068 KB)
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