ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912943177728000 |
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| author | Xu, Yuhao Wang, Xiaoda Wu, Yi Jin, Wei Hu, Xiao Yang, Carl |
| author_facet | Xu, Yuhao Wang, Xiaoda Wu, Yi Jin, Wei Hu, Xiao Yang, Carl |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04589 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model Xu, Yuhao Wang, Xiaoda Wu, Yi Jin, Wei Hu, Xiao Yang, Carl Artificial Intelligence 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. |
| title | ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.04589 |