Electrocardiogram Representation Learning

Also known as ECG

Latest papers

33 papers6 papers in the last four weeks, up 50% on the four weeks before. 0.0% of all new papers.

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
Robust Transfer Learning for Paper ECG Recognition
Sep 30, 2026Yinghao Xie, Zhenbang Dai, Haojun Wang +6Contrastive Learning Electrocardiography
Uppsala University, Sweden · Nanjing Medical University, China · The First Affiliated Hospital with Nanjing Medical University, China
TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care
Sep 28, 2026Lovely Yeswanth Panchumarthi, Andrew Lu, Saurabh Kataria +11Representation Learning Cross-Modal Representation Learning
Department of Computer Science, Emory University, GA, USA · School of Nursing, Emory University, GA, USA · Institute for Medical Engineering and Science, Massachusetts Institute of Technology, MA, USA +3
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
A Hybrid CNN--State-Space--Attention Backbone with Joint-Embedding Predictive Pretraining for 12-Lead ECG Classification
Sep 24, 2026Yakoub Bazi, Sarah Aljuhani, Mohamad M. Al Rahhal +2Electrocardiography Self-Supervised Time Series Representation Learning
Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia · Applied Computer Science Department, College of Applied Computer Science, King Saud University, Riyadh, 11543, Saudi Arabia
From ECG Signals to Representative-Morphology Heatmaps for Biometric Recognition
Sep 23, 2026Athanasios Angelakis, Marta Gomez-BarreroBiometric Identification Electrocardiography
BioML, Research Institute CODE, University of the Bundeswehr Munich, Munich, Germany · Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
On the role of the tokenizer in ECG transformer models
Sep 14, 2026Jiawei Li, Fabio Bonassi, Johan Sundström +2Electrocardiogram Classification Time Series Transformers
Uppsala University, Uppsala, Sweden · Scilifelab, Uppsala, Sweden
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
Beat-Synchronous Tokenization for ECG Transformers
Aug 31, 2026Ahmed Sameh, Nolan Wilson, Max Enderlein +1Electrocardiography Electrocardiogram Classification
Computer Science & Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA.
Graph-Based Pseudo-multimodal Contrastive Learning for 12-Lead ECG Representations
Aug 27, 2026Mengyu Wang, Kozo Okada, Takafumi Goto +4Electrocardiogram Representation Learning Electrocardiogram Classification
Graduate School of Engineering Science Yokohama National University Yokohama, Japan · Division of Cardiology Yokohama City University Medical Center Yokohama, Japan · Technology and Innovation Department Fukuda Denshi Co.,Ltd Tokyo, Japan
Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning
Aug 21, 2026Blaise Delaney, Dominic Dootson, Juan Jose Juan Castella +5Electrocardiogram Representation Learning Self-Supervised Time Series Representation Learning
TimeTrace Labs · University of Cambridge · University of Oxford +1
CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation
Aug 13, 2026Hamza Shafiq, Hung Manh Pham, Bin Zhu +3Cross-Modal Representation Learning Joint-Embedding Predictive Architecture
Eindhoven University of Technology, Netherlands · Singapore Management University, Singapore
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
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
Learning Cardiac Latent Representations in Vectorcardiogram Space
May 29, 2026Bosong Huang, Panzhen Zhao, Zengxiang Li +5Representation Learning Electrocardiography
Griffith University, Australia · SingHealth Duke-NUS AI in Medicine Institute, Singapore · SingHealth AI Office, Singapore +1
Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals
May 26, 2026Phu X. Nguyen, Konstantinos Kontras, Wei Dai +5Representation Learning Cross-Modal Representation Learning
1KU Leuven · 3MIT · 4DFKI +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
How Do Electrocardiogram Models Scale?
May 17, 2026Jiawei Li, Fabio Bonassi, Ming Jin +4Self-Supervised Pre-Training Electrocardiography
Uppsala University · Griffith University
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
Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs
Apr 24, 2026Jose Geraldo Fernandes, Luiz Facury, Pedro Robles Dutenhefner +1Predictive Representation Learning Healthcare
Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte, Brazil
CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy
Apr 13, 2026Zehao Qin, Xiaojian Lin, Ping Zhang +6Electrocardiogram Representation Learning Representation Learning
Department of Electronic Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, PR China · Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, 102218, PR China · Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, PR China +3
Learning ECG Image Representations via Dual Physiological-Aware Alignments
Apr 2, 2026Hung Manh Pham, Jialu Tang, Aaqib Saeed +3Electrocardiogram Representation Learning Medical Image Representation Learning
University of Cambridge · Singapore Management University · Eindhoven University of Technology, Netherlands
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
Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction
Mar 18, 2026Youssef Youssef, Jitin SinglaMulti-View Contrastive Learning Electrocardiography
Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India · Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos
Mar 9, 2026Michelle Espranita Liman, Özgün Turgut, Alexander Müller +3Electrocardiogram Representation Learning Multimodal Learning
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Germany · Department of Cardiology, TUM University Hospital, Germany · Department of Computing, Imperial College London, UK +1
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

Oct 6, 2026J/K move · Enter open · S save

MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning

Robert A. Lewis, I-Min Chiu, Kyle Verrier, Karthik Jayaraman Raghuram+6

Massachusetts Institute of Technology (work done while at Apple). · Apple, Inc. · Division of Cardiology, Johns Hopkins Medicine.+1

Electrocardiogram Foundation Models Electrocardiogram Classification Multimodal Contrastive Learning Electrocardiogram Representation Learning Electrocardiogram Interpretation

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Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation models have demonstrated promising results, but are limited by a reliance on ECG interpretation reports as their sole supervision. Because interpretation reports only capture the subset of waveform information routinely recognized by clinicians, this constrains representation learning to overlook the broader diagnostic signals present in ECG. We introduce a new ECG foundation model --- MS-ECG-FM --- that is trained through contrastive alignment to multiple distinct clinical note types, including ECG, echocardiography, radiology, and discharge reports. We evaluate MS-ECG-FM on an extended set of ECG detection benchmarks, showing that it comprehensively outperforms existing methods on the full span of conditions that ECG can detect, including in reduced-lead configurations. Different reports improve representations for different diagnostic domains, while multi-source alignment captures their complementary information and produces consistently strong representations across clinically diverse tasks.