Electrocardiography

Also known as ECG

Electrocardiography analyzes electrical waveforms recorded from the heart to identify rhythm disorders, measure cardiac intervals, and screen for conditions such as heart failure or myocardial infarction. Common methods include signal processing and waveform delineation, supervised CNN and Transformer classifiers, and self-supervised foundation models; multimodal approaches also align ECGs with clinical text or signals such as PPG. Researchers evaluate these systems on benchmarks including PTB-XL and MIT-BIH, as well as clinical datasets such as MIMIC-IV-ECG, for tasks ranging from beat classification to long-term monitoring and ECG reconstruction. A central challenge is making strong benchmark results hold across hospitals, patient groups, devices, and recording conditions while providing explanations and evidence that clinicians can trust.

Latest papers

108 papers11 papers in the last four weeks, down 31% on the four weeks before. 0.1% of all new papers.

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
Holtercare-Bench: A Multimodal Benchmark for Evaluating Long-Term Dynamic ECG Analysis
Aug 19, 2026Yihan Xie, Hanwen Cui, Runze Ye +8Electrocardiography Electrocardiogram Interpretation
Zhejiang University · Beijing Institute of Technology · University of Electronic Science and Technology of China
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
Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations
Aug 10, 2026Hongxiang Gao, He-yang Xu, Yuwen Li +6Electrocardiography Electrocardiogram Classification
State Key Laboratory of Digital Medical Engineering, Southeast University · School of Instrument Science and Engineering, Southeast University
CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals
Aug 7, 2026Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire +3Concept Drift Electrocardiography
Computer Science, Morgan State University, Baltimore, USA · Computer Science, Fisk University, Nashville, USA · Electrical Engineering, Morgan State University, Baltimore, USA +1
ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation
Aug 6, 2026Akanta Das, Tasinul Islam Ahon, Ahmed Mahir Sultan Rumi +3Medical Report Generation Electrocardiography
Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · Samsung Research America, USA · National Health Service, UK
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
ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring
Aug 4, 2026Arash Vashagh, Yasmin VashaghAdversarial Attacks Electrocardiography
Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick E3B 5A3, Canada · Farzanegan Amin 2 High School, Isfahan, Iran
Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Management
Jul 28, 2026Sukju Oh, Moo-Yong Rhee, Jae-Sik Jang +1Electrocardiography Clinical Decision Support
Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul, 04620, Republic of Korea · Department of Cardiology, Dongguk University Ilsan Hospital, Goyang, 10326, Republic of Korea
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
Automated ECG Interval Measurement and Wave Delineation Using Fast Fourier Convolution ResNet
Jul 27, 2026Farhan Adam Mukadam, Harshit Mishra, Nachiket Makwana +3Electrocardiography Convolutional Neural Networks
Indian Institute of Science, Bengaluru, India · Gauze, India · Waikato District Health Board, Hamilton, New Zealand +1
Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction
Jul 26, 2026Jie Lin, Weijie Sun, Sunil V. Kalmady +4Electrocardiography Clinical Outcome Prediction
Department of Information Management, National Taiwan University, Taiwan · Department of Computing Science, University of Alberta, Canada · Canadian VIGOUR Center, University of Alberta, Canada +2
Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs
Jul 23, 2026Hai-Nam Duy Vuong, Duy-Anh Bui, Trong-Nghia Nguyen +5Multimodal Grounding Medical Report Generation
Business AI Lab, College of Technology, National Economics University, Vietnam · FPT University, Hanoi, Vietnam · A2I Lab, Phenikaa School of Computing, Phenikaa University, Hanoi, Vietnam +2
Knowledge-Guided Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Label-Conditioned Contrastive Alignment
Jul 17, 2026Xinran Liu, Yuwen Li, Hongxiang Gao +4Electrocardiography Cross-Modal Alignment
School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China · Nanjing Medical University, Nanjing, 211166, China · Zhengzhou University, Zhengzhou, 450001, China
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
Cross-Modal Generative Framework for Signal Translation from Fetal-Maternal Electrocardiograms to Fetal Doppler Waveforms
Jul 9, 2026Tongli Su, Alireza Rafiei, Marly van Assen +4Electrocardiography Multimodal Generation
Emory University, Atlanta, GA, USA · Georgia Institute of Technology, Atlanta, GA, USA · Monash University, Clayton, VIC, Australia
ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening
Jul 8, 2026Shreyasvi Natraj, Cyrus Achtari, Felice Gragnano +3Electrocardiography Electrocardiogram Classification
Spinal Cord and Artificial Intelligence (SCAI) Lab, ETH Zürich, Zürich, Switzerland · Swiss Paraplegic Research, Nottwil, Luzern, Switzerland · Department of Translational Medical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy +1
REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality
Jul 7, 2026Taerin Ki, Sunghwan Park, Junyoung Park +1Biometric Identification Electrocardiography
Chung-Ang University Seoul, South Korea
ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment
Jul 6, 2026Xiaocheng Fang, Haoyu Wang, Jieyi Cai +14Time Series Imputation Incomplete Data Imputation
National Institute of Health Data Science, Peking University, Beijing, China · School of Intelligence Science and Technology, Peking University, Beijing, China · Institute of Medical Technology, Peking University Health Science Center, Beijing, China +6
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
Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis
Jul 1, 2026Siwon KimElectrocardiography Self-Supervised Time Series Representation Learning
Research Institute of Basic Sciences, Seoul National University, Seoul, Korea
Domain Knowledge Based Temporal-Spatial Graph Convolution Network for ECG Recognition
Jul 1, 2026Wenting Ma, Zhipeng Zhang, Xiaohang Yuan +6Electrocardiography Electrocardiogram Classification
China Mobile Research Institute, Beijing, China · China Mobile GBA (Greater Bay Area) Innovation Institute, Guangzhou, China · Beijing Jiaotong University, Beijing, China +2
Computational Analysis of Heart Rate Variability in Healthy Adults
Jun 25, 2026María J. Lado, Arturo J. Méndez, Leandro Rodriguez-Liñares +4Electrocardiography Scientific Reproducibility
Escola Superior de Enxeñaría Informática, IFCAE - Universidade de Vigo, Campus As Lagoas, Ourense, 32004, Spain. · Universidad Politécnica de Madrid, Escuela Tecn Super Ingn Telecomunicac, Informat Proc & Telecommun Ctr, Speech Technol & Machine Learning Grp, Madrid, 28040, Spain
Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study
Jun 22, 2026Khalil Ahammad, Derek Abbott, Mohsen DorrakiElectrocardiography Multimodal Large Language Models
School of Computer Science and Information Technology, Adelaide University, SA 5005, Australia · Australian Institute for Machine Learning (AIML), Adelaide, Australia · Pi MedTech, Adelaide, Australia +1
SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Jun 18, 2026Feng Wu, Harsh Deep, Eric Lehman +5Electrocardiography Self-Supervised Time Series Representation Learning
Massachusetts Institute of Technology · OpenEvidence, USA · New York University +3
cAPM: Continual AI-Assisted Pace-Mapping with Active Learning
Jun 11, 2026Dylan O'Hara, Pradeep Bajracharya, Casey Meisenzahl +7Neural Surrogate Modeling Electrocardiography
Rochester Institute of Technology, Rochester, New York, USA · Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA · Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA +5
Boosting ECG Classification Performance by Pre-training with Synthesized Data
Jun 9, 2026Naoki Nonaka, Jun SeitaSynthetic Data Pretraining Synthetic-to-Real Domain Adaptation
Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters
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
Motif-based morphology signatures for interpretable ECG screening and monitoring
May 26, 2026Nivedita Bijlani, Mauricio VillarroelElectrocardiography Electrocardiogram Classification
The Podium Institute for Sports Medicine and Technology University of Oxford Oxford, UK
TriDP-PTM: a three-stage distortion-perception tradeoff guides the pre-training model for radar cardiac sensing
May 25, 2026Jinye Li, Aidong Men, Yang Liu +1Electrocardiography Rate-Distortion Optimization
National Institute of Health Data Science, Peking University, Beijing, China · Institute of Medical Technology, Peking University, Beijing, China · Beijing University of Posts and Telecommunications, Beijing, China +3

Aug 21, 2026J/K move · Enter open · S save

Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning

Blaise Delaney, Dominic Dootson, Juan Jose Juan Castella, Salil Patel+4

TimeTrace Labs · University of Cambridge · University of Oxford+1

Electrocardiogram Representation Learning Self-Supervised Time Series Representation Learning Equivariant Representation Learning Electrocardiogram Classification Electrocardiography

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The cyclic structure of physiological processes offers a natural prior for self-supervised representation learning, and the cardiac cycle provides a particularly well-defined setting in which to exploit it. We derive a phase-equivariant self-supervised objective and introduce Winder, a joint-embedding architecture that organises representations into phase-invariant coordinates and phase-rotating harmonic subspaces. Its transport operator is fixed and closed-form, derived from the cycle's geometry rather than learned, and adds no parameters. Evaluated on PTB-XL under a frozen linear-probe protocol, Winder attains diagnostic accuracy within the range reported by state-of-the-art self-supervised methods at a ~1 M parameter footprint, while exhibiting phase-equivariant latent geometry. These findings demonstrate that explicitly encoding cardiac-phase symmetry can preserve diagnostically useful information while yielding a latent geometry that is legible, parameter-efficient, and directly tied to a measurable physiological quantity.