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

TL;DR

This work proposes ECG-MoE, a hybrid architecture that integrates multi-model temporal features with a cardiac period-aware expert module, using a dual-path Mixture-of-Experts to separately model beat-level morphology and rhythm and a hierarchical fusion network using LoRA for efficient inference.

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.

Figures (5)

• Figure 1: Existing foundation models have limitations because different models capture distinct ECG features, hindering multi-task performance within a single model. Furthermore, despite the strong periodicity inherent in ECG signals, this characteristic is often overlooked by existing ECG foundation models. To address these issues, we propose an ensemble learning method based on Period MoE. The effectiveness of our approach is validated across five common downstream tasks.
• Figure 2: ECG-MoE framework: ① Multi-model feature extraction with adaptive downsampling, ② Period-aware gating weight generation via specialized MoE, ③ Multi-Task fusion using LoRA.
• Figure 3: Self-Attention
• Figure 4: Cross Attention
• Figure 5: Hybrid Multi-Head Attention