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arXiv · 2603.04589

ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model

Yuhao Xu·Xiaoda Wang·Yi Wu·Wei Jin·Xiao Hu·Carl Yang
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Abstract

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.

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Yuhao Xu, Xiaoda Wang, Yi Wu, Wei Jin, Xiao Hu, Carl Yang. 2026-03-04. ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model. https://arxiv.org/abs/2603.04589

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