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OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records

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arxiv 2503.00711 v1 pith:4J73IFXG submitted 2025-03-02 cs.LG cs.AI

Zhijiang Wan , Qianhao Yu , Jia Mao , Wenfeng Duan , Cheng Ding This is my paper
classification cs.LGcs.AI
keywords databyoldatasetssimclranalysisecg-fmsexperimentsfoundation
verification ladder T0 review T1 audit T2 compute T3 formal
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This study introduces OpenECG, a large-scale benchmark of 1.2 million 12-lead ECG recordings from nine centers, to evaluate ECG foundation models (ECG-FMs) trained on public datasets. We investigate three self-supervised learning methods (SimCLR, BYOL, MAE) with ResNet-50 and Vision Transformer architectures, assessing model generalization through leave-one-dataset-out experiments and data scaling analysis. Results show that pre-training on diverse datasets significantly improves generalization, with BYOL and MAE outperforming SimCLR, highlighting the efficacy of feature-consistency and generative learning over contrastive approaches. Data scaling experiments reveal that performance saturates at 60-70% of total data for BYOL and MAE, while SimCLR requires more data. These findings demonstrate that publicly available ECG data can match or surpass proprietary datasets in training robust ECG-FMs, paving the way for scalable, clinically meaningful AI-driven ECG analysis.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study

    eess.SP 2026-05 unverdicted novelty 7.0 of 10

    Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.

  2. Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection

    cs.LG 2026-07 conditional novelty 6.5 of 10

    For Brugada syndrome detection, ECG foundation-model pre-training mainly stabilizes optimization rather than encoding transferable clinical knowledge, and fails to improve zero-shot cross-site generalization.

  3. Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    Sleep-only contrastive pretraining improves results on non-sleep EEG and ECG tasks relative to training from scratch and matches or exceeds some specialized models.

  4. Position: Evaluation of ECG Representations Must Be Fixed

    cs.LG 2026-02 conditional novelty 6.0 of 10

    Current ECG representation benchmarks overstate the benefits of pretraining and produce unstable method rankings; a random encoder with linear probing is competitive on many tasks.

  5. Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons

    cs.LG 2026-05 unverdicted novelty 5.0 of 10

    A parameter-efficient plug-in framework adds structurally compatible long-sequence processing and semantically informed temporal modeling to extend pretrained 10-second ECG foundation models to longer variable-length inputs.

  6. QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients

    cs.LG 2025-09 conditional novelty 5.0 of 10

    QualityFM, a multimodal ECG/PPG foundation model using self-distillation from clean to noisy signals, outperforms task-specific baselines on three ICU monitoring tasks.

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