构建首个统一的心电图基础模型评估基准，提出更强基线模型。
BenchECG and xECG: a benchmark and baseline for ECG foundation models
- 设计标准化心电图基准BenchECG，覆盖多数据集与多任务。
- xECG模型在所有任务上表现最优，是首个全任务强模型。
- 适合关注医疗影像表征学习的研究者和开发者。
心电图（ECG）成本低、应用广，适合深度学习。近年来，研究者致力于开发心电图基础模型，使其能泛化到多种下游任务。但现有评估缺乏一致性：以往工作常选用任务过窄、数据集不统一，导致比较不公平。为此，本文提出BenchECG，一个标准化基准，包含多个公开心电图数据集及多样化任务。同时，提出基于xLSTM的 recurrent 模型 xECG，采用 SimDINOv2 自监督学习训练，在所有任务中均优于公开可用的先进模型，是首个在全部数据集和任务上表现优异的模型。通过统一评估标准，BenchECG 支持严谨对比，推动心电图表示学习发展。xECG 的卓越性能确立了新的基线，为未来研究提供参考。
原文摘要 · Abstract (English)
Electrocardiograms (ECGs) are inexpensive, widely used, and well-suited to deep learning. Recently, interest has grown in developing foundation models for ECGs - models that generalise across diverse downstream tasks. However, consistent evaluation has been lacking: prior work often uses narrow task selections and inconsistent datasets, hindering fair comparison. Here, we introduce BenchECG, a standardised benchmark comprising a comprehensive suite of publicly available ECG datasets and versatile tasks. We also propose xECG, an xLSTM-based recurrent model trained with SimDINOv2 self-supervised learning, which achieves the best BenchECG score compared to publicly available state-of-the-art models. In particular, xECG is the only publicly available model to perform strongly on all datasets and tasks. By standardising evaluation, BenchECG enables rigorous comparison and aims to accelerate progress in ECG representation learning. xECG achieves superior performance over earlier approaches, defining a new baseline for future ECG foundation models.
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