Electrocardiogram Classification

Also known as ECG

Electrocardiogram classification maps ECG signals to rhythm labels or cardiac diagnoses, from beat-level arrhythmias to multi-label screening for structural disease. Methods include convolutional and recurrent networks, Transformers and state-space models, engineered morphology and heart-rate features, and self-supervised foundation models often adapted with ECG reports or other clinical data. Models are evaluated on benchmarks such as PTB-XL and MIT-BIH, with growing attention to patient-held-out and cross-site tests, reduced-lead recordings, and wearable settings. A central challenge is whether strong benchmark results generalize across patients and devices, especially for rare or ambiguous labels, while remaining calibrated, interpretable, and efficient enough for clinical use.

Latest papers

108 papers9 papers in the last four weeks, down 25% on the four weeks before. 0.1% of all new papers.

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
Manifold-Informed Neural Networks for Low-Label ECG Electrolyte Inference
Oct 20, 2026Juntang Wang, Hao Wu, Yihan Wang +2Electrocardiogram Classification Electrocardiogram Interpretation
Duke University, USA
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
Transport What Shares the Conduction Pathway: Normal-Anchored SPD Geometric Transport for Source-Free Patient Adaptation of Holter and Patch ECG
Oct 17, 2026JinKook KimElectrocardiogram Classification Test-Time Adaptation
AI Division, HUINNO Co., Ltd., Seoul, Republic of Korea
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution
Oct 13, 2026Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng +8Language Model Self-Improvement Electrocardiogram Classification
Zhejiang University · Ant Group · Shanghai Artificial Intelligence Laboratory +1
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
Few-Shot Prototype Head Adaptation for On-Device ECG Personalization on PSoC~6
Oct 5, 2026Guilherme Silva, Pedro Silva, Gladston Moreira +1Prototype-Based Learning On-Device Training
Computing Department, Universidade Federal de São João del-Rei – São João del-Rei, Brazil · Computing Department, Federal University of Ouro Preto – Ouro Preto, Brazil
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
Logic Gate Networks and Lookup Table Networks as Lightweight Hardware Classifiers for Inter-patient ECG Arrhythmia Classification
Sep 26, 2026Wout Mommen, Lars Keuninckx, Siddharth Patil +3Differentiable Logic Gate Networks Electrocardiogram Classification
Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, Leuven, 3001, Belgium · Vrije Universiteit Brussel, Pleinlaan 2, Elsene, 1050, Belgium · imec Holst Centre, High Tech Campus 31, Eindhoven, 5656 AE, the Netherlands
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
The Entropy Triangle Method (ETM): A novel framework for the prevention of cardiac arrhythmia with a review of more than 10,000 patients
Sep 24, 2026Arman daliriClass-Imbalanced Learning Electrocardiography
Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
TRACE: Tractable Routing Autoencoder for Clinical ECG
Sep 20, 2026Shunbo Jia, Runze Ma, Haonan Lyu +3Concept Bottleneck Models Autoencoders
Department of Bioelectronics, Faculty of Biomedical Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518107, China · Faculty of Information Technology, Monash University, Clayton, Victoria, 3800, Australia · Faculty of Science, University of Queensland, Brisbane, 4072, Australia +1
X-Beat: An Explainable Framework for ECG Image Classification
Sep 18, 2026Mohammad Sadman Tahsin, Haitham Y. Adarbah, Afzel NooreElectrocardiogram Classification Explainable Image Classification
AI-Based Autonomous System Research Lab Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville (TAMUK) Kingsville, TX, USA
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
Bridging the Gap in ECG-Based Emotion Recognition: A Unified Evaluation of Deep Learning Models
Sep 14, 2026Timothy C Sweeney-Fanelli, Ajan Ahmed, Masudul ImtiazElectrocardiogram Classification Emotion Recognition
Clarkson University, USA · Affects AI LLC, USA
Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
Sep 8, 2026Athanasios Papastathopoulos-Katsaros, Alexandra Stavrianidi, Zhandong LiuElectrocardiogram Classification Physics-Informed ML
Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA · Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA · Institute for Analysis and Numerics, University of Münster, Germany +1
Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings
Sep 8, 2026Laura Alvarez-Florez, Daniel Uyterlinde, Samuel Ruipérez-Campillo +3Cross-Modal Representation Learning Medical Diagnosis
Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, The Netherlands · Quantitative Healthcare Analysis Group, University of Amsterdam, The2026 Netherlands · Department of Clinical and Experimental Cardiology, Amsterdam University Medical Center, The Netherlands +2
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
Evaluator-Dependent Patient-Adaptive ECG Lead-Channel Allocation
Aug 27, 2026Xiaoyang Li, Zeyan TaoElectrocardiography Policy Evaluation
College of Medicine and Biological Information Engineering Northeastern University, Shenyang, China
Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency
Aug 24, 2026Wenhan Jiang, Zhipeng Deng, Jiale Zhou +3Test-Time Adaptation Electrocardiogram Classification
Westlake University, Hangzhou, China · Hokkaido University, Sapporo, Japan · RIKEN, 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
Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework
Aug 19, 2026Hongtao Li, Jia Wei, Guoyao Li +6Atrial Fibrillation Detection Electrocardiogram Classification
Information Center, The People’s Hospital of Baoan Shenzhen, Shenzhen, 518101, Guangdong, China · School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China · Department of Cardiology, The People’s Hospital of Baoan Shenzhen, Shenzhen, 518101, Guangdong, China +1
Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning
Aug 14, 2026Chun-Hua Lin, Samuel Yen-Chi Chen, Yu-Chao Hsu +7Electrocardiogram Classification Kolmogorov-Arnold Networks
Department of Physics and Center for Theoretical Physics, National Taiwan University, Taipei, Taiwan · National Center for High-Performance Computing, National Institutes of Applied Research, Hsinchu, Taiwan · Brookhaven National Laboratory, Upton, NY, USA +5
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.
A Vision Transformer for ECG-Based Detection of Left Ventricular Systolic Dysfunction Across Multiple Clinical Sites
Aug 12, 2026Burcu Ozek, Aruna Mohan, David Vorchheimer +5Electrocardiogram Classification Left Ventricular Ejection Fraction Estimation
Walkky LLC, Boston, MA, USA · Northwell Cardiovascular Institute, Lenox Hill Hospital, New York, NY, USA · Weiss Consulting Groups, Boca Raton, FL, USA +2

Oct 26, 2026J/K move · Enter open · S save

TransECG-SSL: Hybrid Self-Supervised Representation Learning for ECG Arrhythmia Classification

Ahtisham Ayyub, Fasih Haider, Muhammad Rehan Usman, Saturnino Luz+2

School of Computer Science and Mathematics, Kingston University, London, United Kingdom · Usher Institute, University of Edinburgh, Edinburgh, United Kingdom

Electrocardiogram Representation Learning Electrocardiogram Classification Cross-Domain Transfer Learning Self-Supervised Time Series Representation Learning

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Cross-dataset generalisation remains a major challenge for automated electrocardiogram (ECG) arrhythmia classification because of differences in patient populations, acquisition protocols, annotation practices, and class distributions. Learning representations that remain informative across independent datasets could reduce reliance on dataset-specific labelled data. We propose TransECG-SSL, a self-supervised framework for transferable heartbeat representation learning from unlabelled ECG data. A Transformer encoder is pretrained using a hybrid objective combining masked signal reconstruction and contrastive learning, then frozen and evaluated by linear probing under patient-disjoint evaluation. Experiments across three public ECG databases and seven source--target settings show that Hybrid SSL consistently outperforms randomly initialised Transformers and achieves higher balanced accuracy than architecture-matched source-supervised pretraining. Reconstruction-only SSL remains competitive with Hybrid SSL, whereas contrastive-only pretraining performs substantially worse, highlighting the importance of masked reconstruction for transferability. Cross-dataset transfer nevertheless reduces balanced accuracy relative to target-specific self-supervised pretraining, while minority arrhythmia classes remain challenging under severe class imbalance. These findings demonstrate the potential of label-efficient self-supervised learning for cross-dataset ECG representation transfer while highlighting persistent limitations from domain shift and class imbalance.