Electrocardiogram Representation Learning

Also known as ECG

Latest papers

36 papers4 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

ECG2Gene: Deep Learning-Based Germline Tumor Susceptibility Gene Risk Assessment Using Electrocardiograms
Oct 31, 2026Shanwei Zhang, Qinghao Zhao, Yize Zhao +16Clinical Risk Prediction Electrocardiogram Representation Learning
Department of Computer Science, Tianjin University of Technology, Tianjin, China · National Institute of Health Data Science, Peking University, Beijing, China · Department of Cardiology, Peking University People’s Hospital, Beijing, China +7
TransECG-SSL: Hybrid Self-Supervised Representation Learning for ECG Arrhythmia Classification
Oct 26, 2026Ahtisham Ayyub, Fasih Haider, Muhammad Rehan Usman +3Electrocardiogram Representation Learning Electrocardiogram Classification
School of Computer Science and Mathematics, Kingston University, London, United Kingdom · Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
QECGBERT: A Scalable ECG Foundation Model Based on Self-Supervised Language Learning via VQ-VAE Waveform Tokenization
Oct 25, 2026Aruna Mohan, Burcu Ozek, Or Zilbershot +1Electrocardiogram Foundation Models Electrocardiogram Representation Learning
Walkky LLC, Boston, MA, USA · Clemson University, Clemson, SC, USA
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

Oct 31, 2026J/K move · Enter open · S save

ECG2Gene: Deep Learning-Based Germline Tumor Susceptibility Gene Risk Assessment Using Electrocardiograms

Shanwei Zhang, Qinghao Zhao, Yize Zhao, Jun Li+15

Department of Computer Science, Tianjin University of Technology, Tianjin, China · National Institute of Health Data Science, Peking University, Beijing, China · Department of Cardiology, Peking University People’s Hospital, Beijing, China+7

Clinical Risk Prediction Electrocardiogram Representation Learning

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Electrocardiograms (ECGs) may contain physiological information related to hereditary cancer susceptibility, but its relationship with future cancer risk remains unclear. Here, we present ECG2Gene, a two-stage deep-learning framework that derives digital phenotypes related to germline tumor susceptibility from routine 12-lead ECGs and predicts future cancer risk. In Stage 1, carrier status for pathogenic or likely pathogenic variants in ATM, BRIP1, CHEK2, PALB2, BRCA1, and BRCA2 supervises a multitask model that generates six continuous ECG-derived gene scores. In Stage 2, XGBoost integrates these scores with clinical variables to predict subsequent breast and ovarian cancer in women and prostate cancer in men. We also introduce ECG-Wide Association Analysis (EWAS) to characterize associations between ECG-derived gene scores and ECG waveforms. Across the clinical feature combinations evaluated in UK Biobank, the best-performing sex-specific models achieve AUROCs of 0.683, 0.633, and 0.660 for breast, ovarian, and prostate cancer, respectively. In external evaluations, models combining ECG-derived gene scores with age achieve prostate cancer AUROCs of 0.709 and 0.579 among men in the overall China Kadoorie Biobank and MIMIC-IV-ECG cohorts, respectively. ECG2Gene links genetically supervised ECG phenotyping and waveform association analysis to future cancer outcomes, demonstrating the potential of routine ECGs combined with clinical information for non-invasive, opportunistic cancer risk assessment.