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

• URL: http://arxiv.org/abs/2603.04589v1
• Date: Wed, 04 Mar 2026 20:36:05 GMT
• Title: ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
• Authors: Yuhao Xu, Xiaoda Wang, Yi Wu, Wei Jin, Xiao Hu, Carl Yang,
• Abstract summary: ECG-MoE is a hybrid architecture that integrates multi-model temporal features with a cardiac period-aware expert module.<n>It achieves state-of-the-art performance with 40% faster inference than multi-task baselines.
• Score: 22.753790262338185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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