针对心电图分析难题，提出分周期建模的专家混合模型，提升诊断精度与推理速度。
ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
- 采用双路径专家混合架构，分别捕捉心跳形态与节律特征
- 在5个公开任务上达到顶尖性能，推理速度比多任务基线快40%
- 适合临床心电图智能诊断系统研发者使用
心电图分析对心脏疾病诊断至关重要，但现有基础模型难以捕捉周期性及多样特征以应对不同临床任务。我们提出ECG-MoE，一种融合多模态时序特征与心脏周期感知专家模块的混合架构。该方法通过双路径混合专家结构分别建模心跳层面的形态与节律，并结合基于LoRA的分层融合网络实现高效推理。在五个公开临床任务上的评估显示，ECG-MoE达到当前最优性能，推理速度比多任务基线快40%。
原文摘要 · Abstract (English)
Electrocardiography (ECG) analysis is crucial for cardiac diagnosis, yet existing foundation models often fail to capture the periodicity and diverse features required for varied clinical tasks. We propose ECG-MoE, a hybrid architecture that integrates multi-model temporal features with a cardiac period-aware expert module. Our approach uses a dual-path Mixture-of-Experts to separately model beat-level morphology and rhythm, combined with a hierarchical fusion network using LoRA for efficient inference. Evaluated on five public clinical tasks, ECG-MoE achieves state-of-the-art performance with 40% faster inference than multi-task baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。