MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs
National University of Singapore（新加坡国立大学）；HeartVoice Medical Technology（心声医疗科技）；University of Science and Technology of China（中国科学技术大学）；Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院附属邵逸夫医院）；Peking University（北京大学）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）
Ventricular arrhythmias (VA) are the main causes of sudden cardiac death. Developing machine learning methods for detecting VA based on electrocardiograms (ECGs) can help save people's lives. However, developing such machine learning models for ECGs is challenging because of the following: 1) group-level diversity from different subjects and 2) individual-level diversity from different moments of a single subject. In this study, we aim to solve these problems in the pre-training and fine-tuning stages. For the pre-training stage, we propose a novel model agnostic meta-learning (MAML) with curriculum learning (CL) method to solve group-level diversity. MAML is expected to better transfer the knowledge from a large dataset and use only a few recordings to quickly adapt the model to a new person. CL is supposed to further improve MAML by meta-learning from easy to difficult tasks. For the fine-tuning stage, we propose improved pre-fine-tuning to solve individual-level diversity. We conduct experiments using a combination of three publicly available ECG datasets. The results show that our method outperforms the compared methods in terms of all evaluation metrics. Ablation studies show that MAML and CL could help perform more evenly, and pre-fine-tuning could better fit the model to training data.
Combining ECG Foundation Model and XGBoost to Predict In-Hospital Malignant Ventricular Arrhythmias in AMI Patients
National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Fuwai Central China Cardiovascular Hospital（阜外华中心血管病医院）；HeartVoice Medical Technology（心音医疗科技）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）
A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings
Zhejiang University（浙江大学）；The First Affiliated Hospital of University of Science and Technology of China（中国科学技术大学附属第一医院）；HeartVoice Medical Technology（心声医疗科技）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification
Dipartimento di Informatica, Università di Verona, Verona, Italy（信息学院，威尼斯大学，威尼斯，意大利）；Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration, Karachi, Pakistan（计算机科学系，数学与计算机科学学院，商学院，卡拉奇，巴基斯坦）；Faculty of Information Technology, Salim Habib University, Karachi, Pakistan（信息技术学院，萨利姆·哈比大学，卡拉奇，巴基斯坦）
uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm
Faculty of Biomedical Engineering, Technion-IIT（生物医学工程学院，技术ion-IIT）；Ruth and Bruce Rappaport Faculty of Medicine, Technion-IIT（鲁思和布鲁斯·拉帕波特医学学院，技术ion-IIT）
Towards Automated Diagnosis of Inherited Arrhythmias: Combined Arrhythmia Classification Using Lead-Aware Spatial Attention Networks
University of Alberta（阿尔伯塔大学）；University of British Columbia（不列颠哥伦比亚大学）；Montreal Heart Institute（蒙特利尔心脏研究所）；London Health Sciences Center（伦敦健康科学中心）；Quebec Heart and Lung Institute（魁北克心肺研究所）；Children’s Heart Centre（儿童心脏中心）；University of Toronto（多伦多大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）
TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection
University of Notre Dame（圣母大学）；South Central Minzu University（中南民族大学）；Guangdong Cardiovascular Institute（广东省心血管病研究所）；Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences)（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）；Singular Medical Co., Ltd（奇点医疗有限公司）
Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy(HCM-VAr-Risk Model)
University of Delaware（特拉华大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Taipei Veterans General Hospital（台北荣民总医院）；National Yang-Ming University（阳明大学）；University of California San Francisco（加利福尼亚大学旧金山分校）
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）
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）
Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation
Shenzhen International Graduate School（深圳国际研究生院）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Beijing Tsinghua Changgung Hospital（北京清华长庚医院）；School of Medicine（医学院）
针对 ECG 标注昂贵及房颤特征捕获不足的问题，提出融合 RR 间期与 P 波知识的周期间-周期内自监督表示学习方法，并在多个数据集取得优异房颤检测性能。
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
University of Engineering and Technology Taxila（塔克西拉工程技术大学）；University of Central Florida（中佛罗里达大学）；Hankuk University of Foreign Studies（韩国外国语大学）；Xiamen University Malaysia（厦门大学马来西亚分校）
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）
A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography
Department of Cardiology, Zhongshan Hospital of Fudan University（复旦大学中山医院心内科）；Shanghai Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine（上海心血管病研究所，国家介入医学临床研究中心）；Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学研究院数字医疗研究中心）；Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention（上海医学影像计算与计算机辅助手术重点实验室）；National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road（伦敦帝国学院国家心肺研究所，哈马舍姆医院，杜肯路）；Department of Cardiology, Shanghai Geriatric Medical Center（上海老年医学中心心内科）；Cardiac Rhythm Management, Medtronic Technology Center, Medtronic (Shanghai) Ltd.（美敦力技术中心，美敦力（上海）有限公司，心律管理部）；Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院比尔·德·阿克谢心脏结局研究中心，贝斯以色列·德aconess医疗中心）；Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛-托尔恩迪克电生理研究所，贝斯以色列·德aconess医疗中心，哈佛医学院）；Department of Cardiology, Imperial College Healthcare NHS Trust（伦敦帝国学院医疗信托心内科部）；Department of Cardiology, Chelsea and Westminster NHS Foundation Trust（切尔西和温斯洛医院 NHS 基础信托心内科部）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）；Georgia Institute of Technology & Emory University（佐治亚理工学院与埃默里大学）
Characterising cardiac tissue properties with graph neural networks
Imperial College London（帝国理工学院）；Chonnam National University College of Medicine（全南大学医学院）；St. George’s University Hospital（圣乔治大学医院）；Cardiovascular & Genomics Research Institute（心血管与基因组学研究所）；City St George’s University of London（伦敦城市圣乔治大学）；National Heart & Lung Institute（国家心肺研究所）
HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院查尔斯·布朗夫曼个性化医学研究所）；Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山临床智能中心）；Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院遗传学与基因组科学系）；The Hasso Plattner Institute for Digital Health at Mount Sinai（西奈山哈索·普拉特纳数字健康研究所）；Department of Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院医学系）；Mount Sinai Heart, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山心脏中心）；Department of Cardiology, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院心脏病学系）；Department of Medicine, NYU Langone Health（纽约大学朗格尼健康中心医学系）；The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院帕梅拉·斯克拉精神病基因组学部门）；Department of Psychiatry, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院精神病学系）；Department of Biomedical Engineering, Tel Aviv University（特拉维夫大学生物医学工程系）；Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院医学系肾脏病科）
IKrNet: A Neural Network for Detecting Specific Drug-Induced Patterns in Electrocardiograms Amidst Physiological Variability
IRD, Sorbonne Université, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO（IRD，索邦大学，数学与复杂系统建模单元，UMMISCO）；University of Evry - Paris-Saclay, IBISC Laboratory（埃弗里-巴黎萨克雷大学，IBISC实验室）；Clinical Investigation Center Paris-Est, CIC-1901, INSERM, Department of Pharmacology Pitié-Salpêtrière University Hospital, Sorbonne Université（巴黎-伊夫里临床研究中心，CIC-1901，INSERM，皮蒂埃-萨尔佩特里耶大学医院药理学部，索邦大学）；Department of Medicine, Vanderbilt University Medical Center（医学系，范德比尔特大学医学中心）；Sorbonne Université, INSERM, Nutrition et Obesities（索邦大学，INSERM，营养与肥胖；系统方法，NutriOmique，AP-HP皮蒂埃-萨尔佩特里耶医院）；systemic approaches, NutriOmique, AP-HP Hôpital Pitié-Salpêtrière
Compact Neural Network Algorithm for Electrocardiogram Classification
Centro de Física Aplicada y Tecnología Avanzada（应用物理与先进技术中心）；Instituto de Ciencias Nucleares（核科学研究所）；Instituto Tecnológico y de Estudios Superiores de Monterrey（蒙特雷技术与高等研究学院）
Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals
Department of Mechanical Engineering, University of Washington（华盛顿大学机械工程系）；Division of Biophysics and Medical Physics, Gottfried Schatz Research Center, Medical University of Graz（格拉茨医科大学生物物理与医学物理系）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Department of Biomedical Engineering, University of Utah（犹他大学生物医学工程系）
