🌟 Overview

The electrocardiogram~(ECG) is a key diagnostic tool in cardiovascular health. Single-lead ECG recording is integrated into both clinical-grade and consumer wearables. We propose CLEF, the first foundation model for single-lead ECG, leveraging metadata–derived risk scores for each patient as guided supervisory signals. CLEF was pretrained on 161K patients from MIMIC-IV-ECG using 12-lead ECGs. We evaluated on 18 clinical classification and regression tasks across 7 held-out datasets, and benchmarked against 5 foundation model baselines and 3 self-supervised learning algorithms. Overall, out method achieves an ≥ 2.6% improvement in average AUROC for classification, and ≥ 3.2% reduction in MAE for regression, outperforming all self-supervised foundation model baselines. Beyond accuracy, CLEF advances multifacet and robust single-lead ECG analysis, enabling next-generation remote health monitoring and wearable intelligence.

🚀 News

• [Dec 2025] Paper released on arXiv
• [Nov 2025] Code and pretrained weights released

✨ Introduction

CLEF is an ECG foundation model trained with clinically-guided contrastive learning. This repository contains the implementation for our paper "CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models".

Key features:

• Clinical-informed contrastive pretraining for better representation
• Pretrained models on 3 sizes: small, base, large
• Easy single-lead ECG representation extraction and downstream task evaluation

📚 Documentation

• Environment Setup — Docker and conda installation
• Pretraining — Run contrastive pretraining
• Downstream Tasks — Evaluation on diagnostic tasks
• Pretrained Weights — Download model checkpoints
• Dataset Preparation — Prepare PTB-XL, ICENTIA, etc.
• Example Notebooks — Jupyter tutorials

🧭 Quick Start

• Launch the interactive quickstart notebook: notebooks/clef_quickstart.ipynb

📦 Available Models

🙏 Acknowledgements

We gratefully acknowledge the contributions of the following projects, which were instrumental in the evaluation of CLEF:

• Moment: moment-timeseries-foundation-model/moment
• Moirai: SalesforceAIResearch/uni2ts
• ECGFounder: PKUDigitalHealth/ECGFounder
• KED: control-spiderman/ECGFM-KED
• ST-MEM: bakqui/ST-MEM
• SimCLR: sthalles/SimCLR
• BYOL: lucidrains/byol-pytorch
• MoCo: facebookresearch/moco

📝 Citation

If you use CLEF in your research, please cite:

@article{clef2024,
  title={CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models},
  author={Yuxuan Shu, Peter Charlton, Fahim Kawsar, Jussi Hernesniemi, Mohammad Malekzadeh},
  journal={arXiv preprint arXiv:2512.02180},
  year={2025}
}