Electrocardiogram (ECG) foundation models pretrained on typical diagnostic 10-second ECG segments, have demonstrated strong transferability across a range of clinical applications. However, many real-world applications produce recordings that are typically longer, and are varied in duration during inference time. These 10-second models have no built-in way to combine information across time. Extending them to longer horizons introduces two challenges: structural incompatibilities arising from input-length disparities, and semantic challenges that limit meaningful temporal aggregation. We propose a parameter-efficient framework that extends pretrained ECG foundation models to longer and variable-length ECGs without retraining the backbone. Guided by a frozen pretrained 10-second model, we introduce a lightweight plug-in module that extends the model in two complementary ways: (i) structurally compatible long-sequence processing and (ii) semantically informed temporal modeling. Experiments on multiple long-horizon ECG tasks, datasets, and foundation model backbones demonstrate that our method enables robust long-horizon extension from pretrained snapshot models, consistently outperforming sliding-window and pooling-based baselines with strong parameter efficiency.
@article{arxiv.2605.16975,
title = {Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons},
author = {Wei Tang and Jinpei Han and Kangning Cui and Mattia Carletti and Fredrik K. Gustafsson and Shreyank N Gowda and Patitapaban Palo and Anshul Thakur and Lei Clifton and Jean-michel Morel and Raymond H. Chan and David A. Clifton and Xiao Gu},
journal= {arXiv preprint arXiv:2605.16975},
year = {2026}
}