# ECG-FM: An Open Electrocardiogram Foundation Model

arXiv: https://arxiv.org/abs/2408.05178 · HTML: https://mlsanity.com/paper/2408.05178/

## Key facts

- Cited by 90 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (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)
- [Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram](https://mlsanity.com/paper/2402.09450/) (2024)
- [Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding](https://mlsanity.com/paper/2305.12031/) (2023)
- [Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram](https://mlsanity.com/paper/2203.06889/) (2022)
- [Improving Pixel-based MIM by Reducing Wasted Modeling Capability](https://mlsanity.com/paper/2308.00261/) (2023)
- [Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach](https://mlsanity.com/paper/2302.03357/) (2023)
- [ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning](https://mlsanity.com/paper/2306.06340/) (2023)
- [CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains](https://mlsanity.com/paper/2407.07110/) (2024)

## Built on by (most cited first, 30 of 90)

- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://mlsanity.com/paper/2410.20542/) (2024)
- [GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images](https://mlsanity.com/paper/2503.06073/) (2025)
- [Zero-shot forecasting of chaotic systems](https://mlsanity.com/paper/2409.15771/) (2024)
- [An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains](https://mlsanity.com/paper/2410.04133/) (2024)
- [Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2410.08559/) (2024)
- [Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning](https://mlsanity.com/paper/2505.11349/) (2025)
- [Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners](https://mlsanity.com/paper/2410.02131/) (2024)
- [From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining](https://mlsanity.com/paper/2506.21803/) (2025)
- [OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records](https://mlsanity.com/paper/2503.00711/) (2025)
- [Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models](https://mlsanity.com/paper/2409.11302/) (2024)
- [Benchmarking ECG FMs: A Reality Check Across Clinical Tasks](https://mlsanity.com/paper/2509.25095/) (2025)
- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings](https://mlsanity.com/paper/2411.17711/) (2024)
- [Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs](https://mlsanity.com/paper/2502.17900/) (2025)
- [Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning](https://mlsanity.com/paper/2605.00973/) (2026)
- [EchoingECG: An Electrocardiogram Cross-Modal Model for Echocardiogram Tasks](https://mlsanity.com/paper/2509.25791/) (2025)
- [Learning Transferable Sensor Models via Language-Informed Pretraining](https://mlsanity.com/paper/2603.11950/) (2026)
- [TolerantECG: A Foundation Model for Imperfect Electrocardiogram](https://mlsanity.com/paper/2507.09887/) (2025)
- [QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients](https://mlsanity.com/paper/2509.06516/) (2025)
- [Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics](https://mlsanity.com/paper/2605.15465/) (2026)
- [CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation](https://mlsanity.com/paper/2608.12944/) (2026)
- [Sensing Cardiac Health Across Scenarios and Devices: A Multi-Modal Foundation Model Pretrained on Heterogeneous Data from 1.7 Million Individuals](https://mlsanity.com/paper/2507.01045/) (2025)
- [ProbMed: A Probabilistic Framework for Medical Multimodal Binding](https://mlsanity.com/paper/2509.25711/) (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)
- [CODE-II: A large-scale dataset for artificial intelligence in ECG analysis](https://mlsanity.com/paper/2511.15632/) (2025)
- [An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings](https://mlsanity.com/paper/2512.08954/) (2025)
- [CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation](https://mlsanity.com/paper/2604.10420/) (2026)
- [Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks](https://mlsanity.com/paper/2605.02500/) (2026)
- [ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation](https://mlsanity.com/paper/2605.17580/) (2026)
- [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024)

## Abstract (arXiv)

Conventional task-specific electrocardiogram (ECG) analysis models require large annotated datasets to train. Foundation models mitigate this burden by leveraging self-supervised pretraining; however, the scarcity of open-weight ECG foundation models hinders adoption and cross-study comparability. We present ECG-FM, an open foundation model for ECG analysis, and conduct a study using a dataset of 1.5 million ECGs. ECG-FM is a transformer-based model pretrained using a hybrid contrastive and generative self-supervised learning approach. Our downstream tasks include predicting reduced left ventricular ejection fraction (LVEF) and ECG interpretation labels, where we release a benchmark task on the MIMIC-IV-ECG dataset. We affirm that ECG-FM is robust, label-efficient, and functionally discriminative by showcasing data scaling experiments, performing a latent space analysis, and generating saliency maps. ECG-FM markedly outperforms task-specific models in the small-to-medium-scale data regime and demonstrates cross-dataset generalizability, achieving high AUROC on many clinically salient labels such as atrial fibrillation (0.996) and LVEF<=40% (0.929). We release our code, model weights, and benchmark task at https://github.com/bowang-lab/ECG-FM/.