ECG foundation model that reads any subset of the 12 standard leads natively, encoding recordings as variable-size spatiotemporal graphs.
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Point-of-care cardiac devices — smartwatches, chest patches, handheld recorders — capture one or two ECG leads, while almost every ECG foundation model is built around the clinical 12-lead standard. The usual workaround is zero-padding: flat lines stand in for the missing leads so the input matches the fixed interface a model expects. That conflates a lead that was never recorded with a detached electrode, and it spends compute proportional to the full sensor array however few leads are present. LAEF (Lead-Agnostic ECG Foundation) treats the mismatch as an architectural problem rather than an input-formatting one.
Developed by researchers at the University of Cambridge and the University of Brescia, LAEF represents a recording as a variable-size spatiotemporal graph instead of a fixed-channel tensor. Each lead is partitioned into contiguous temporal segments that become graph nodes; intra-lead edges connect every segment within a lead, and inter-lead edges connect only time-aligned segments across leads. A Graph Attention Network processes whatever graph the available leads produce, so both the node count and the message-passing cost scale with the active lead count.
At 7.1M parameters LAEF is an order of magnitude smaller than the transformer-based ECG foundation models it is benchmarked against, among them ECG-JEPA, ECGFounder, and ECGFM-KED. Its central claim is that a model fine-tuned only on 12-lead recordings can run directly on one or two leads with no lead-specific retraining.
Pretraining draws on 11 12-lead ECG collections spanning five countries and 9.2M recordings, including CODE, MIMIC-IV, PTB-XL, CPSC, Chapman, Ningbo, and SPH. Signals are band-pass filtered to 0.05–47 Hz, downsampled to 100 Hz, and rescaled to [-1, 1]; each lead is cut into 20 segments embedded by a shared 1D convolutional module. An encoder ablation on a held-out 220k-subject subset favours the Graph Attention Network at 0.937 macro AUROC over a graph isomorphism network at 0.907 and a graph transformer at 0.900, and the spatiotemporal topology over a fully connected one (0.937 versus 0.854).
Evaluation covers 18 downstream datasets scored by macro AUROC after supervised fine-tuning on 12-lead data. At full lead availability no single model dominates and LAEF places in the top three on most datasets despite being up to 12× smaller than the largest baselines. Under a single randomly sampled lead it ranks first on 17 of 18 datasets, with a peak gain of +10.6 AUROC points; with two leads it ranks first on 14 of 18, and the average gain over zero-padded alternatives is +3.2 points. A lead-importance study across 164 cardiovascular conditions finds per-lead macro AUROC stable within 0.003 across all 12 leads while recovering established lead–condition associations, such as elevated importance of leads III and V5 for ischemia and infarction.
LAEF is aimed at diagnostic screening on consumer and ambulatory hardware, where recording geometry is fixed by the device rather than by the clinic. A single model can be fine-tuned on abundant 12-lead hospital data and then deployed on a smartwatch or handheld recorder without a separate reduced-lead architecture — useful precisely because labelled point-of-care datasets remain scarce. The small parameter count, low memory footprint, and short fine-tuning time — 30 minutes on the largest evaluation corpus — also suit teams adapting the model to local cohorts on modest hardware.
LAEF is a preprint awaiting peer review, and its results are self-reported. Code and checkpoints have not been released; the authors state they will follow acceptance. They also note that the model was evaluated on reduced-lead subsets of clinical 12-lead recordings rather than on signals acquired by point-of-care devices, leaving noise profiles and non-standard electrode placement untested, and that the lead-importance findings do not substitute for clinically established lead-specific protocols. What the work does establish is a concrete alternative to zero-padding for ECG foundation models: making the architecture itself indifferent to how many leads arrive, rather than forcing every recording into a 12-lead interface.
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