Models operating on dynamic physiologic signals must distinguish benign, label-preserving variability from true concept change. Existing concept-drift frameworks are largely distributional and provide no principled guidance on how much a model's internal representation may move when the underlying signal undergoes physiologically plausible fluctuations in energy. As a result, deep models often misinterpret harmless changes in amplitude, rate, or morphology as concept drift, yielding unstable predictions, particularly in multimodal fusion settings.
This study introduces Physiologic Energy Conservation Theory (PECT), an energy-based framework for concept stability in dynamic signals. PECT posits that under virtual drift, normalized latent displacement should scale proportionally with normalized signal energy change, while persistent violations of this proportionality indicate real concept drift. We operationalize this principle through Energy-Constrained Representation Learning (ECRL), a lightweight regularizer that penalizes energy-inconsistent latent movement without modifying encoder architectures or adding inference-time cost.
Although PECT is formulated for dynamic signals in general, we instantiate and evaluate it on multimodal ECG across seven unimodal and hybrid models. Experiments show that in the strongest trimodal hybrid (1D+2D+Transformer), clean accuracy is largely preserved (96.0% to 94.1%), while perturbed accuracy improves substantially (72.6% to 85.5%) and fused representation drift decreases by over 45%. Similar trends are observed across all architectures, providing empirical evidence that PECT functions as an energy-drift law governing concept stability in continuous physiologic signals.
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）
ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification
Department of Computer Engineering, University of Kurdistan（库尔德斯坦大学计算机工程系）；Department of Mathematics and Operational Research, University of Mons（蒙斯大学数学与运筹学系）
An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Bakar Computational Health Sciences Institute, University of California, San Francisco（加州大学旧金山分校巴卡计算健康科学研究所）；Center for Data Science, School of Nursing, Emory University（埃默里大学护理学院数据科学中心）；Department of Computer Science, University of Southern California（南加州大学计算机科学系）
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）
Masked Transformer for Electrocardiogram Classification
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家心血管病中心阜外医院）
本文提出 MTECG 方法，将掩码自编码器应用于 ECG 时间序列的自监督表示学习，并在多个数据集上显著提升了分类的宏 F1 分数。
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；University of Zurich（苏黎世大学）；School of Medicine, Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学雷赫特-伊萨尔医院医学院）；Imperial College London（伦敦帝国学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
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）
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）
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
The Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology, Peng Cheng Laboratory（南方科技大学鹏城实验室）；Huazhong University of Science and Technology（华中科技大学）
Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）
MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
The Ohio State University（俄亥俄州立大学）；Imperial College London（伦敦帝国理工学院）；The University of Hong Kong（香港大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Convolutional Recurrent Neural Networks for Electrocardiogram Classification
Dept. IT & EE ETH Zurich（苏黎世联邦理工学院信息与电气工程系）；Switzerland（瑞士（注：此处Switzerland为国家名，非机构，原HTML中该字段可能为作者地址的一部分，按要求不应提取，但根据文本结构，“Dept. IT & EE ETH Zurich”明确为机构，“Switzerland”为国家，非机构，故仅提取前者））
CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms
Yale School of Medicine（耶鲁医学院）；Yale New Haven Hospital（耶鲁-纽黑文医院）；Yale School of Public Health（耶鲁公共卫生学院）
ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；University of the Chinese Academy of Sciences（中国科学院大学）；Department of Cardiology, Peking University People’s Hospital（北京大学人民医院心内科）；Department of Computer Science, Tianjin University of Technology（天津理工大学计算机科学与工程学院）
ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks
Universitat Pompeu Fabra（庞培法布拉大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物工程、生物材料与纳米医学生物医学研究网络中心（CIBER-BBN））；Aragón Institute of Engineering Research（阿拉贡工程研究所）；University of Zaragoza（萨拉戈萨大学）
Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection
Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院阜外医院）；National Center for Cardiovascular Diseases（国家心血管病中心）；National Clinical Research Center for Cardiovascular Diseases（国家心血管疾病临床医学研究中心）
ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion
IRD（法国发展研究院）；Sorbonne Université（索邦大学）；University Paris-Saclay（巴黎-萨克雷大学）；INSERM（法国国家健康与医学研究院）；Pitié-Salpêtrière University Hospital（皮蒂-萨尔皮特里大学医院）；Vanderbilt University Medical Center（范德堡大学医学中心）；AP-HP（巴黎公共医疗集团）；Hôpital Pitié-Salpêtrière（皮蒂-萨尔皮特里医院）
