Electrocardiogram Foundation Models

Also known as ECG

Electrocardiogram foundation models learn patterns from large collections of heart recordings so they can support many diagnostic and prediction tasks, including when labeled examples are scarce. Common approaches include self-supervised masked-signal prediction, contrastive learning that aligns ECGs with clinical reports, and fine-tuning pretrained encoders for specific tasks. Evaluations span benchmarks such as PTB-XL and MIMIC-IV-ECG, as well as ICU, wearable, long-recording, and limited-lead settings. Pretraining often helps, but gains can be inconsistent across hospitals and rare diseases, leaving transfer reliability, privacy, interpretability, and computational efficiency as open challenges.

Latest papers

29 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram
Oct 20, 2026Huynh Dang Nguyen, Trong-Thang Pham, Anh-Thuan Do +4Electrocardiogram Foundation Models Electrocardiogram Interpretation
Quantum AI & Cyber Security Institute (QACI) - FPT Corporation, Vietnam · University of Arkansas, USA · Hospital Haiphong Medical University, Vietnam +2
MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning
Oct 6, 2026Robert A. Lewis, I-Min Chiu, Kyle Verrier +7Electrocardiogram Foundation Models Electrocardiogram Classification
Massachusetts Institute of Technology (work done while at Apple). · Apple, Inc. · Division of Cardiology, Johns Hopkins Medicine. +1
PhysioTRACE: Provenance-Aware Stress Tests for Physiological Foundation Models
Sep 28, 2026Ayana Mussabayeva, Anuar Aimoldin, Olivier Oullier +2Data Provenance Electrocardiogram Foundation Models
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE · McGill University, Montreal, Canada · Carnegie Mellon University, Pittsburgh, USA
BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment
Sep 25, 2026Mohammad Nur Hossain Khan, M. S. Krafczyk, Beverly G. Bolster +3Electrocardiogram Representation Learning Electrocardiography
University of Massachusetts Amherst · University of Illinois Urbana-Champaign
Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar
Sep 4, 2026Shayan Sharifi, Riccardo Treu, Ilaria Gandin +3Electrocardiogram Interpretation Electrocardiogram Representation Learning
Department of Mathematics, Informatics, and Geosciences, University of Trieste, Italy · Centre of Diagnosis and Management of Cardiomyopathies; Azienda Sanitaria Universitaria Giuliano Isontina; University of Trieste, Italy; Member of Ern Guard-Heart · Department of Medical, Surgical and Health Sciences, University of Trieste, Italy
The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning
Aug 13, 2026Ahmed Sameh, Ramzi Al-Sharawi, Yogatheesan VaratharajahSelf-Supervised Time Series Representation Learning Electrocardiogram Foundation Models
Computer Science & Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455. · Robotics, University of Minnesota Twin Cities, Minneapolis, MN 55455.
LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics
Aug 4, 2026Edoardo Coppola, Stefano Fiorini, Pietro Liò +2Electrocardiography Electrocardiogram Foundation Models
Department of Computer Science and Technology, University of Cambridge · Independent Researcher · Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia
FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection
Aug 4, 2026Amirhossein Taleshinosrati, Yangyang Wang, Atitaya Phoemsuk +5Atrial Fibrillation Detection Electrocardiogram Foundation Models
SDU Health Informatics and Technology University of Southern Denmark Odense, 5230, Denmark · Department of Computer Science University of Helsinki Helsinki, 00100, Finland · School of Computer Science and Electronic Engineering University of Essex Colchester, CO4 3SQ, UK +1
ECG-InterpBench: Benchmarking the Interpretability of ECG Foundation Models with Matched-Scale Sparse Autoencoders
Jul 29, 2026Yixuan Duan, Wei QiuElectrocardiogram Foundation Models Neural Network Interpretability
Department of Electrical and Computer Engineering Rice University Houston, Texas, USA
CADENCE: A Cardiac Atom Dictionary for Interpretable Neural Concept Extraction from ECG Foundation Models
Jul 28, 2026Yixuan Duan, Arjun Naik, Sadeer Al-Kindi +1Electrocardiography Electrocardiogram Foundation Models
Department of Electrical and Computer Engineering Rice University Houston, Texas, USA · Department of Computer Science Rice University Houston, Texas, USA · Department of Cardiology Houston Methodist Hospital Houston, Texas, USA
NeuroECG: ECGFounder-Based Deep ECG Representation for EEG-Free Neurological Prognostication After Cardiac Arrest
Jul 13, 2026Jiajun Gao, Yi Zhao, Chenyang Xu +2Representation Learning Healthcare
School of Economics and Management Xidian University Xi’an, China · School of Mechano-Electronic Engineering Xidian University Xi’an, China · School of Cyber Engineering Xidian University Xi’an, China +1
MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms
Jul 3, 2026Saiyang Feng, Yuanyun Zhang, Shi LiSelf-Supervised Time Series Representation Learning Cross-Modal Representation Learning
University of the Chinese Academy of Sciences · Columbia University
Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection
Jul 3, 2026Beatrice Zanchi, Giuliana Monachino, Alvise Dei Rossi +4Domain Generalization Electrocardiogram Foundation Models
University of Applied Sciences and Arts of Southern Switzerland, Department of Innovative Technologies, Via La Santa 1, Lugano, 6900, Ticino, Switzerland · University of Zurich, Department of Quantitative Biomedicine, Winterthurerstrasse 190, Zurich, 8057, Zurich, Switzerland · Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, Movement Disorder Research Group, Neurology Department, Via Tesserete 46, Lugano, 6900, Ticino, Switzerland +5
EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation
May 28, 2026Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu PhamEfficient Neural Network Inference Electrocardiogram Foundation Models
Hanoi University of Science and Technology, Hanoi, Vietnam · VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam +1
A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography
May 25, 2026Ziqing Yu, Yuhui Tao, Jiayu Huo +19Electrocardiogram Classification Multimodal Contrastive Learning
Department of Cardiology, Zhongshan Hospital of Fudan University, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai, China · Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China. · Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China. +8
CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment
May 21, 2026Amir Mousavi, Erfan Nourbakhsh, Mohammad Sadegh Sirjani +5Electrocardiography Electrocardiogram Foundation Models
Department of Computer Science, College of AI, Cyber and Computing, The University of Texas at San Antonio · Department of Neuroscience, Developmental and Regenerative Biology, College of Sciences, The University of Texas at San Antonio · Department of Educational Psychology, College of Education and Human Development, The University of Texas at San Antonio
How Do Electrocardiogram Models Scale?
May 17, 2026Jiawei Li, Fabio Bonassi, Ming Jin +4Self-Supervised Pre-Training Electrocardiography
Uppsala University · Griffith University
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
May 16, 2026Wei Tang, Jinpei Han, Kangning Cui +10Electrocardiography Electrocardiogram Foundation Models
1City University of Hong Kong · 2Imperial College London · 3Wake Forest University +3
Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model
May 13, 2026Riccardo Cavarra, Lupo Lovatelli, Shaheim Ogbomo-Harmitt +4Electrocardiography Clinical Outcome Prediction
King’s College London, St Thomas’ Hospital, London, UK · North Bristol NHS Trust, Bristol, UK
Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
May 12, 2026M A Al-Masud, Nils StrodthoffElectrocardiogram Foundation Models Self-Supervised Time Series Representation Learning
AI4Health Division Carl von Ossietzky Universität Oldenburg Oldenburg, Lower Saxony, Germany
Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
Apr 25, 2026Duc N. Do, Minh N. Do, Dang Nguyen +14Unsupervised Domain Adaptation Echocardiography
1PASSIO Laboratory, North Carolina A&T State University, Greensboro, NC, USA · Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada · 3Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA +4
Membership Inference Attacks Expose Participation Privacy in ECG Foundation Encoders
Apr 12, 2026Ziyu Wang, Elahe Khatibi, Ankita Sharma +4Membership Inference Attacks Privacy Auditing
University of California, Irvine Irvine, California, United States · Arizona State University Tempe, Arizona, United States · California State University, Dominguez Hills Carson, California, United States
From Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student
Mar 28, 2026Giovanni dos Santos Franco, Felipe Mahlow, Ellison Fernando Cardoso +1Electrocardiogram Classification Knowledge Distillation
Faculty of Sciences, S˜ao Paulo State University (Unesp), Bauru, Brazil. · Hospital Israelita Albert Einstein, S˜ao Paulo, Brazil. · Laboratory of Magnetic Resonance in Neuroradiology (LIM-44), University of S˜ao Paulo Faculty of Medicine Clinics Hospital, S˜ao Paulo, Brazil.
Foundation Model for Cardiac Time Series via Masked Latent Attention
Mar 27, 2026Moritz Vandenhirtz, Samuel Ruipérez-Campillo, Simon Böhi +6Electrocardiogram Representation Learning Electrocardiogram Foundation Models
Department of Computer Science, ETH Zurich, Switzerland · Department of Biomedical Engineering, University of Basel, Switzerland
SignalMC-MED: A Multimodal Benchmark for Evaluating Biosignal Foundation Models on Single-Lead ECG and PPG
Mar 10, 2026Fredrik K. Gustafsson, Xiao Gu, Mattia Carletti +3Electrocardiogram Foundation Models Multimodal Model Evaluation
Department of Engineering Science, University of Oxford · NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance · Big Data Institute, Nuffield Department of Population Health, University of Oxford +2
ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
Mar 4, 2026Yuhao Xu, Xiaoda Wang, Yi Wu +3Electrocardiogram Foundation Models Electrocardiography
Department of Computer Science, Emory University, Atlanta, GA 30307 · School of Computer Science, University of Oklahoma, Norman, OK 73019 · Center of Data Science, Emory University, Atlanta, GA 30307
RhythmBERT: A Self-Supervised Language Model Based on Latent Representations of ECG Waveforms for Heart Disease Detection
Feb 26, 2026Xin Wang, Burcu Ozek, Aruna Mohan +3Electrocardiogram Representation Learning Electrocardiogram Foundation Models
Virginia Tech, VA, USA · Walkky LLC, Boston, MA, USA · Northeastern University, Boston, MA, USA +1
CAMEL: An ECG Language Model for Forecasting Cardiac Events
Feb 17, 2026Neelay Velingker, Alaia Solko-Breslin, Mayank Keoliya +9Electrocardiogram Foundation Models Electrocardiogram Interpretation
University of Pennsylvania
A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients
Dec 19, 2025Sarah Nassar, Nooshin Maghsoodi, Sophia Mannina +7Atrial Fibrillation Detection Electrocardiogram Foundation Models
Department of Electrical and Computer Engineering at Queen’s University, Kingston, ON, Canada · School of Computing at Queen’s University, Kingston, ON, Canada · Smith School of Business at Queen’s University, Kingston, ON, Canada +5

Oct 20, 2026J/K move · Enter open · S save

IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram

Huynh Dang Nguyen, Trong-Thang Pham, Anh-Thuan Do, Phuong-Nhi Dinh+3

Quantum AI & Cyber Security Institute (QACI) - FPT Corporation, Vietnam · University of Arkansas, USA · Hospital Haiphong Medical University, Vietnam+2

Electrocardiogram Foundation Models Electrocardiogram Interpretation Electrocardiogram Classification Weakly Supervised Learning

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Electrocardiography (ECG) is a cornerstone of cardiac diagnosis, yet most machine learning methods for ECG analysis lack interpretability, limiting their clinical adoption. We present IECG, a weakly-supervised ECG foundation model that jointly performs diagnostic classification and abnormality localization without requiring manual abnormal region annotations, overcoming a long-standing bottleneck in interpretable ECG learning. Central to IECG is a weakly-supervised attention strategy that explicitly incorporates clinical priors by deriving abnormality heatmap pseudo-labels from established ECG clinical knowledge. These pseudo-labels guide the model's CLS-token attention through a KL-divergence loss, encouraging it to focus on diagnostically relevant waveform regions. During pretraining, IECG is jointly optimized with clinically guided attention supervision, ECG-text contrastive alignment, and report captioning objectives, enabling it to learn rich multimodal representations while producing localized waveform evidence. By explicitly aggregating features from clinically guided attention regions, IECG grounds its diagnostic predictions in interpretable waveform evidence without sacrificing classification performance. Extensive experiments demonstrate that IECG achieves state-of-the-art abnormal region localization while maintaining strong classification performance, advancing trustworthy foundation models for ECG analysis.