The electrocardiogram (ECG) is a key diagnostic tool in cardiovascular health. Single-lead ECG recording is integrated into both clinical-grade and consumer wearables. While self-supervised pretraining of foundation models on unlabeled ECGs improves diagnostic performance, existing approaches do not incorporate domain knowledge from clinical metadata. We introduce a novel contrastive learning approach that utilizes an established clinical risk score to adaptively weight negative pairs: clinically-guided contrastive learning. It aligns the similarities of ECG embeddings with clinically meaningful differences between subjects, with an explicit mechanism to handle missing metadata. On 12-lead ECGs from 161K patients in the MIMIC-IV dataset, we pretrain single-lead ECG foundation models at three scales, collectively called CLEF, using only routinely collected metadata without requiring per-sample ECG annotations. We evaluate CLEF on 18 clinical classification and regression tasks across 7 held-out datasets, and benchmark against 5 foundation model baselines and 3 self-supervised algorithms. When pretrained on 12-lead ECG data and tested on lead-I data, CLEF outperforms self-supervised foundation model baselines: the medium-sized CLEF achieves average AUROC improvements of at least 2.6% in classification and average reductions in MAEs of at least 3.2% in regression. Comparing with existing self-supervised learning algorithms, CLEF improves the average AUROC by at least 1.8%. Moreover, when pretrained only on lead-I data for classification tasks, CLEF performs comparably to the state-of-the-art ECGFounder, which was trained in a supervised manner. Overall, CLEF enables more accurate and scalable single-lead ECG analysis, advancing remote health monitoring. Code and pretrained CLEF models are available at: github.com/Nokia-Bell-Labs/ecg-foundation-model.
CLEF: EEG Foundation Model for Learning Clinical Semantics
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布里奇沃特医院）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）
Angular Gaussian Supervised Contrastive Learning for Long-Tailed Electrocardiogram Arrhythmia Diagnosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Department of Neurology and Neurological Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University（同济大学医学院附属上海养志康复医院神经科与神经康复科）
ECG-FM: An Open Electrocardiogram Foundation Model
Toronto General Hospital Research Institute（多伦多总医院研究所在）；University Health Network（大学健康网络）；Peter Munk Cardiac Centre（彼得·蒙克心脏中心）；UHN AI Hub（UHN人工智能中心）；Vector Institute for Artificial Intelligence（人工智能向量研究所）；Department of Laboratory Medicine and Pathobiology（实验室医学与病理学系）；Department of Medical Biophysics（医学生物物理系）；Department of Computer Science（计算机科学系）；Department of Medicine（医学系）
Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms
National Heart and Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Chelsea and Westminster Hospital NHS Foundation Trust（切尔西和威斯敏斯特医院NHS基金会信托）；Zhongshan Hospital of Fudan University（复旦大学附属中山医院）；Shanghai Institute of Cardiovascular Diseases（上海市心血管病研究所）；National Clinical Research Centre for Interventional Medicine（国家介入医学临床研究中心）；Uppsala University（乌普萨拉大学）；Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center（范德堡大学医学中心临床与转化研究所）；Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center（范德堡大学医学中心数字基因组医学中心）；Division of Cardiovascular Medicine, Vanderbilt University Medical Center（范德堡大学医学中心心血管内科）
We develop a multi-task convolutional neural network (CNN) to classify multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset comprised of over 40,000 ECGs, with labels derived from cardiologist clinical interpretations. Since many clinically important classes can occur in low frequencies, approaches are needed to improve performance on rare classes. We compare the performance of several single-class classifiers on rare classes to a multi-headed classifier across all available classes. We demonstrate that the addition of common classes can significantly improve CNN performan…
A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis
KIOS Research and Innovation Center of Excellence (KIOS CoE)（KIOS研究与创新中心（KIOS CoE））；University of Cyprus（塞浦路斯大学）；MEDTL Medical Technologies Ltd.（MEDTL医疗技术有限公司）；Department of Computer Science, University of Cyprus（塞浦路斯大学计算机科学系）
TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care
Emory University（埃默里大学）；Massachusetts Institute of Technology（麻省理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Rochester（罗切斯特大学）
Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors
Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（信息中心部门，阜外医院，中国医学科学院和北京协和医学院）；The Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（心血管疾病临床医学研究中心，阜外医院，中国医学科学院和北京协和医学院）
Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses
State Key Laboratory of Digital Medical Engineering, Southeast University（数字医疗工程国家重点实验室，东南大学）；School of Instrument Science and Engineering, Southeast University（仪器科学与工程学院，东南大学）；School of Computer Science and Engineering, Southeast University（计算机科学与工程学院，东南大学）
Inventec Corporation（英业达公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）；Chang Gung University of Science and Technology（长庚科技大学）
Survival modeling in healthcare relies on explainable statistical models; yet, their underlying assumptions are often simplistic and, thus, unrealistic. Machine learning models can estimate more complex relationships and lead to more accurate predictions, but are non-interpretable. This study shows it is possible to estimate hospitalization for congestive heart failure by a 30 seconds single-lead electrocardiogram signal. Using a machine learning approach not only results in greater predictive power but also provides clinically meaningful interpretations. We train an eXtreme Gradient Boosting …
A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms
Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运营研究中心）；Wisconsin School of Business, University of Wisconsin–Madison（威斯康星大学麦迪逊分校商学院）；Heart and Vascular Institute, Hartford Hospital（哈特福德医院心脏与血管研究院）
Carnegie Mellon University（卡内基梅隆大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Colorado Denver - Anschutz Medical Campus（科罗拉多大学丹佛分校安舒茨医学校区）；Allegheny Health Network（阿勒格尼健康网络）
There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data, where labeling at scale can be time-consuming and costly in terms of the specialized expertise and human effort required. Moreover, deep learning classifiers may be vulnerable to adversarial examples and perturbations, which could have catastrophic consequences, for example, whe…
ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms
IRD（法国发展研究院）；Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）；Pitié-Salpêtrière University Hospital（皮蒂-萨尔皮特里耶大学医院）；Vanderbilt University Medical Center（范德堡大学医学中心）
Texas A&M University（得克萨斯农工大学）；Dell Inc.（戴尔公司）；Heart Artery and Vein Center of Fresno（弗雷斯诺心脏动脉与静脉中心）
Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diagnostics remains limited. This is because clinical diagnosis of many cardiac pathologies is rooted in gleaning patterns from synchronous 12-lead ECG. If synchronous 12-lead signals of clinical quality can be synthesized from these single-lead devices, it can transform cardiac care…
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与医学中的研究中心，技术大学慕尼黑（TUM）及慕尼黑技术大学医院）；School of Medicine, Klinikum rechts der Isar, TUM, Germany（医学院，右岸克里克医院，TUM，德国）；Department of Computing, Imperial College London, UK（计算学院，伦敦帝国学院，英国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），慕尼黑，德国）
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Harvard Medical School（哈佛医学院）；Stanford University（斯坦福大学）；Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）
DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information
State Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；VCIP, CS, Nankai University（VCIP、CS、南开大学）；HeartVoice Medical Technology（HeartVoice医疗科技）
Explainable Parallel CNN-LSTM Model for Differentiating Ventricular Tachycardia from Supraventricular Tachycardia with Aberrancy in 12-Lead ECGs
Cardiac Rehabilitation Research Center（心脏康复研究中心）；Cardiovascular Research Institute（心血管研究所）；Department of Cardiology（心内科部）；School of Medicine（医学院）；University of Isfahan（伊斯法罕大学）；Department of Computer Science（计算机科学系）；Faculty of Engineering（工程学院）；Department of Electrical, Computer and Biomedical Engineering（电气、计算机与生物医学工程系）；Toronto Metropolitan University（多伦多 Metropolitan 大学）；Faculty of Electrical Engineering（电气工程学院）；K.N. Toosi University of Technology（K.N. Toosi 技术大学）
Rochester Institute of Technology（罗切斯特理工学院）；Dalhousie University（达尔豪斯大学）
The increasing availability of electrocardiogram (ECG) data has motivated the use of data-driven models for automating various clinical tasks based on ECG data. The development of subject-specific models are limited by the cost and difficulty of obtaining sufficient training data for each individual. The alternative of population model, however, faces challenges caused by the significant inter-subject variations within the ECG data. We address this challenge by investigating for the first time the problem of learning representations for clinically-informative variables while disentangling othe…
Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution
Department of Human-Centered Artificial Intelligence, Institute of Applied Computer Science, Jagiellonian University（人类中心人工智能系，应用计算机科学研究所，雅盖隆大学）；Faculty of Medicine, Jagiellonian University Medical College（医学系，雅盖隆大学医学院）
Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools
VUNO Inc.（VUNO公司）；Ajou University（亚洲大学）；C&Thoth Co., Ltd.（C&Thoth有限公司）；Chonnam National University Graduate School（全南大学研究生院）；Chonnam National University Hospital（全南大学医院）；Chonnam National University Medical School（全南大学医学院）
Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images
Tianjin University of Technology（天津理工大学）；Peking University（北京大学）；HeartVoice Medical Technology（心语医疗科技）；Capital Medical University（首都医科大学）；Tianjin Medical University（天津医科大学）；Tsinghua University（清华大学）
