mlsanityData to 7 Oct 2026

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

arXiv 2603.04589 ↗First posted 4 Mar 2026cs.AIcited by 1

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 14 of its 32 references that are in the map. First posted on arXiv 4 Mar 2026.

Builds on

14 of 32 references.

References · 14

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  5. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  6. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  7. 2024ECG-FM: An Open Electrocardiogram Foundation Modelcited by 89
  8. 2017Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networkscited by 52
  9. 2024MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generationcited by 29
  10. 2025Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networkscited by 14
  11. 2025Cultivating Multidisciplinary AI Workforce Development on iTiger GPU Cluster: Practices and Challengescited by 8
  12. 2025PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillationcited by 6
2 more
  1. 2025Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecastingcited by 5
  2. 2017Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordingscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulationcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model
  2. 2026ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography
  3. 2025ALFRED: Ask a Large-language model For Reliable ECG Diagnosis
  4. 2026ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation
  5. 2025GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images
  6. 2026ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning
  7. 2025Benchmarking ECG FMs: A Reality Check Across Clinical Tasks
  8. 2026TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care