这个LAEF模型专治心电图设备导联不全的痛点，手表或手持记录仪只用1-2个导联也能获得接近12导联的诊断效果，比同类小模型强不少。
LAEF是一个仅7M参数的心电图基础模型，能原生处理任意导联组合，无需零填充或修改架构。它在920万份12导联ECG上通过掩码节点建模和随机导联采样预训练。在18个下游数据集中，全导联可用时，LAEF与专用12导联基线相当，而后者规模大12倍。在1-2导联的即时诊断场景下，随机单导联时17/18数据集、双导联时14/18数据集上超过零填充替代方案，平均AUROC提升3.2点。
LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics
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 count.Pre-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.