Diagnosing pre-existing heart diseases early in life is important as it helps prevent complications such as pulmonary hypertension, heart rhythm problems, blood clots, heart failure and sudden cardiac arrest. To identify such diseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms convey important information. Therefore, effectively using these two modalities of data has the potential to improve the disease screening process. We evaluate this hypothesis on a subset of the PhysioNet Challenge 2016 Dataset which contains simultaneously acquired PCG and ECG recordings. Our novel Dual-Convolutional Neural Network based approach uses transfer learning to tackle the problem of having limited amounts of simultaneous PCG and ECG data that is publicly available, while having the potential to adapt to larger datasets. In addition, we introduce two main evaluation frameworks named record-wise and sample-wise evaluation which leads to a rich performance evaluation for the transfer learning approach. Comparisons with methods which used single or dual modality data show that our method can lead to better performance. Furthermore, our results show that individually collected ECG or PCG waveforms are able to provide transferable features which could effectively help to make use of a limited number of synchronized PCG and ECG waveforms and still achieve significant classification performance.
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）
Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
PASSIO Laboratory, North Carolina A&T State University（北卡罗来纳阿塔州立大学PASSIO实验室）；Department of Computer Science, University of Manitoba（曼尼托巴大学计算机科学系）；Harvard T.H. Chan School of Public Health, Harvard University（哈佛大学T.H. Chan公共卫生学院）；Department of Medicine, Jordan University of Science and Technology（约旦科学与技术大学医学院）；Department of Biomedical Engineering, Duke University（达特茅斯大学生物医学工程系）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝塞斯达以色列德acons医疗中心）
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
Faculty of Computer Engineering, K. N. Toosi University of Technology（法尔科夫计算机工程学院，卡·努·托西技术大学）；School of Electrical and Computer Engineering, University of Tehran（电气与计算机工程学院，德黑兰大学）
Personalized Heart Disease Detection via ECG Digital Twin Generation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Guangdong Provincial People’s Hospital, Southern Medical University（南方医科大学广东省人民医院）；Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院广东省心血管病研究所、广东省医学科学院）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）
Method of diagnosing heart disease based on deep learning ECG signal
China Academy of Electronics and Information Technology（中国电子科技集团公司电子科学研究院）
对心电信号进行心脏病诊断的传统方法是人工观察。有些人尝试将专业知识和信号处理结合起来，按心脏病类型对ECG信号进行分类。然而，货币并不足以用于医疗应用。我们开发了一种结合信号处理和深度学习的算法，将ECG信号分类为正常AF其他节律和噪声，这有助于我们解决这个问题。证明了通过小波变换可以得到心电信号的时频图，并利用DNN对时频图进行分类，从而发现信号采集者可能患有的心脏疾病。总体而言，在验证集上实现了94%的准确度。根据2017年PhysioNet/Computing in Cardiology（CinC）的评价标准，该方法的F1评分为0.957，高于2017年竞赛第一名。
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）
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
Walkky（沃基公司）；Chobanian & Avedisian School of Medicine, Boston University（波士顿大学乔巴尼亚与阿夫迪西安医学院）；Clemson University（克莱姆森大学）；Northwell Health Physician Partners（诺斯韦尔健康医师伙伴）；Lenox Hill Hospital（莱诺克斯山医院）
Smartwatch Photoplethysmography-Derived Heart Age via ECG-Guided Cross-Modal Pretraining as a Digital Biomarker of Vascular Aging
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据科学国家研究院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医药技术学院）；OPPO Health Lab（OPPO健康实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学学院）；Xidian University（西安电子科技大学）；Shenzhen Technology University（深圳职业技术大学）
ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
Zhejiang University School of Medicine（浙江大学医学院）；The First Affiliated Hospital（第一附属医院）；Liangzhu Laboratory（良渚实验室）；Shandong Jianzhu University（山东建筑大学）；FUXI AI Lab（伏羲人工智能实验室）；Beijing Normal University（北京师范大学）；Southern University of Science and Technology（南方科技大学）；China University of Geosciences (Wuhan)（中国地质大学（武汉））；Hangzhou Baorun Biotechnology Co., Ltd.（杭州宝润生物科技有限公司）
Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification
Ivannikov Institute for System Programming of the Russian Academy of Sciences（俄罗斯科学院伊万尼科夫系统编程研究所）；Russian-Armenian University（俄罗斯-亚美尼亚大学）；Novgorod State University（诺夫哥罗德国立大学）
Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）；Imperial College London（伦敦帝国学院）；University of Sheffield（谢菲尔德大学）；Novo Nordisk Research Centre Oxford (NNRCO)（牛津诺和硕研究中心（NNRCO））；Royal Society（皇家学会）
