# An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains

arXiv: https://arxiv.org/abs/2410.04133 · HTML: https://mlsanity.com/paper/2410.04133/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 5 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [ECG-FM: An Open Electrocardiogram Foundation Model](https://mlsanity.com/paper/2408.05178/) (2024)
- [Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram](https://mlsanity.com/paper/2402.09450/) (2024)
- [Frozen Language Model Helps ECG Zero-Shot Learning](https://mlsanity.com/paper/2303.12311/) (2023)
- [HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units](https://mlsanity.com/paper/2008.04063/) (2020)
- [Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective](https://mlsanity.com/paper/2212.02801/) (2022)
- [ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text](https://mlsanity.com/paper/2405.19366/) (2024)

## Built on by (most cited first, 27 of 27)

- [OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records](https://mlsanity.com/paper/2503.00711/) (2025)
- [PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection](https://mlsanity.com/paper/2509.19774/) (2025)
- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning](https://mlsanity.com/paper/2605.00973/) (2026)
- [Self-DANA: A Resource-Efficient Channel-Adaptive Self-Supervised Approach for ECG Foundation Models](https://mlsanity.com/paper/2507.14151/) (2025)
- [Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals](https://mlsanity.com/paper/2510.14254/) (2025)
- [ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning](https://mlsanity.com/paper/2503.13570/) (2025)
- [BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals](https://mlsanity.com/paper/2510.02276/) (2025)
- [Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening](https://mlsanity.com/paper/2604.23385/) (2026)
- [How Do Electrocardiogram Models Scale?](https://mlsanity.com/paper/2605.17276/) (2026)
- [Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification](https://mlsanity.com/paper/2606.02605/) (2026)
- [Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology](https://mlsanity.com/paper/2608.13518/) (2026)
- [P2E-VQ: ECG-linked representation augmentation for PPG via discrete patch retrieval](https://mlsanity.com/paper/2608.14656/) (2026)
- [uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm](https://mlsanity.com/paper/2506.11238/) (2025)
- [Reconstructing 12-Lead ECG from 3-Lead ECG using Variational Autoencoder to Improve Cardiac Disease Detection of Wearable ECG Devices](https://mlsanity.com/paper/2510.11442/) (2025)
- [Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI](https://mlsanity.com/paper/2601.00014/) (2025)
- [Looking Beyond Accuracy: A Holistic Benchmark of ECG Foundation Models](https://mlsanity.com/paper/2601.21830/) (2026)
- [Task- and Metric-Specific Signal Quality Indices for Medical Time Series](https://mlsanity.com/paper/2602.12478/) (2026)
- [From Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student](https://mlsanity.com/paper/2603.27269/) (2026)
- [Bit-Identical Medical Deep Learning via Structured Orthogonal Initialization](https://mlsanity.com/paper/2603.28040/) (2026)
- [Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons](https://mlsanity.com/paper/2605.16975/) (2026)
- [VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data](https://mlsanity.com/paper/2605.29483/) (2026)
- [BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing](https://mlsanity.com/paper/2606.07692/) (2026)
- [Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection](https://mlsanity.com/paper/2607.03009/) (2026)
- [LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification](https://mlsanity.com/paper/2607.10784/) (2026)
- [The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning](https://mlsanity.com/paper/2608.12695/) (2026)
- [Beat-Synchronous Tokenization for ECG Transformers](https://mlsanity.com/paper/2608.30367/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model faces several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. Additionally, there is a notable performance gap between single-lead and multi-lead ECG analyses. We introduced an ECG Foundation Model (ECGFounder), a general-purpose model that leverages real-world ECG annotations from cardiology experts to broaden the diagnostic capabilities of ECG analysis. ECGFounder was trained on over 10 million ECGs with 150 label categories from the Harvard-Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis through ECG analysis. The model is designed to be both an effective out-of-the-box solution, and a to be fine-tunable for downstream tasks, maximizing usability. Importantly, we extended its application to lower rank ECGs, and arbitrary single-lead ECGs in particular. ECGFounder is applicable to supporting various downstream tasks in mobile monitoring scenarios. Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets, with AUROC exceeding 0.95 for eighty diagnoses. It also shows strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographic analysis, clinical event detection, and cross-modality cardiac rhythm diagnosis. The trained model and data will be publicly released upon publication through the bdsp.io. Our code is available at https://github.com/PKUDigitalHealth/ECGFounder