OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records

• URL: http://arxiv.org/abs/2503.00711v1
• Date: Sun, 02 Mar 2025 03:26:14 GMT
• Title: OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records
• Authors: Zhijiang Wan, Qianhao Yu, Jia Mao, Wenfeng Duan, Cheng Ding,
• Abstract summary: 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.<n>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.<n>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 contrast
• Score: 2.3942438969883906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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