In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection. By using Optimal Transport, we augment the ECG disease data from normal ECG beats to balance the data among different categories. We build a Multi-Feature Transformer (MF-Transformer) as our classification model, where different features are extracted from both time and frequency domains to diagnose various heart conditions. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions. Our results demonstrate 1) the classification models' ability to make competitive predictions on five ECG categories; 2) improvements in accuracy and robustness reflecting the effectiveness of our data augmentation method.
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
State Key Laboratory of Transvascular Implantation Devices and TIDRI, Zhejiang University（浙江大学血管介入关键实验室及TIDRI）；School of Public Health, Zhejiang University and Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江大学公共卫生学院及浙江省医学影像人工智能重点实验室）；State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University（浙江大学第二附属医院血管介入关键实验室及梁渚实验室）；Department of Gastroenterology, First Hospital of Quanzhou Affiliated to Fujian Medical University（福建医科大学泉州第一医院消化内科）；School of Medicine, Stanford University（斯坦福大学医学院）
The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. The electrocardiogram (ECG) is the most widely used clinical tool for cardiac diagnosis. Its interpretation is, however, confounded by functional and anatomical variability in heart and torso. In this study, we develop new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography and image-ba…
Peking University（北京大学）；Georgia Institute of Technology（佐治亚理工学院）；IQVIA（艾昆纬）；IBM Research（IBM研究院）
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however they are mostly black-box, thus lack interpretability needed for clinical usage. In this work, we propose MultIlevel kNowledge-guided Attention networks (MINA) that predict heart diseases from ECG signals with intuitive explanation aligned with medical knowledge. By extracting multilevel (beat-, rhythm- and frequency-level) domain knowledge features separately, MINA combines the medical knowledge and ECG data via a mu…
Solarillion Foundation（索拉利昂基金会）；SSN College of Engineering（SSN工程学院）
Continuous monitoring of cardiac activity is paramount to understanding the functioning of the heart in addition to identifying precursors to conditions such as Atrial Fibrillation. Through continuous cardiac monitoring, early indications of any potential disorder can be detected before the actual event, allowing timely preventive measures to be taken. Electrocardiography (ECG) is an established standard for monitoring the function of the heart for clinical and non-clinical applications, but its electrode-based implementation makes it cumbersome, especially for uninterrupted monitoring. Hence …
Cross-Learning Between ECG and PCG: Exploring Common and Exclusive Characteristics of Bimodal Electromechanical Cardiac Waveforms
School of Medicine, Emory University（埃默里大学医学院）；Rollins School of Public Health, Department of Epidemiology, Emory University（埃默里大学罗林斯公共卫生学院流行病学系）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（佐治亚理工学院生物医学工程系）
Ufa State Aviation Technical University（乌法国立航空技术大学）；Bashkir State Pedagogical University（巴什基尔国立师范大学）；Ural Federal University（乌拉尔联邦大学）；Institute of Immunology and Physiology UrB RAS（俄罗斯科学院乌拉尔分院免疫学与生理学研究所）
Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effec…
The University of Tennessee（田纳西大学）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is of critical importance for timely medical treatment to save patients' lives. Routine use of electrocardiogram (ECG) is the most common method for physicians to assess the electrical activities of the heart and detect possible abnormal cardiac conditions. Fully utilizing the ECG data for reliable heart disease detection depends on developing effective analytical models. In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for automatic diag…
ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning
Technical University of Munich (TUM)（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学附属医院）；Imperial College London（帝国理工学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Electromechanical Dynamics of the Heart: A Study of Cardiac Hysteresis During Physical Stress Test
School of Medicine, Emory University（埃默里大学医学院）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（佐治亚理工学院与埃默里大学生物医学工程系）
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息学研究所）；Warsaw University of Technology（华沙理工大学）；Cracow University of Technology（克拉科夫理工大学）；Firat University（菲拉特大学）；Singapore University of Social Sciences（新加坡社会科学大学）；University of Southern Queensland（南昆士兰大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）；Kumamoto University（熊本大学）；National Heart Centre Singapore（新加坡国家心脏中心）
Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and cl…
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
The Chinese University of Hong Kong（香港中文大学）；Institute of Future Cities, The Chinese University of Hong Kong（香港中文大学未来城市研究所）；Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong（香港中文大学李嘉诚健康科学研究所）
Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. Continuously and remotely monitoring the subjects' ECG information unlocks the potentials of prompt pre-diagnosis and timely pre-treatment of AF before the development of any life-threatening conditions/diseases. Ultimately, the CVDs associated mortality could be reduced. In th…
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）
Generative Deep Learning and Signal Processing for Data Augmentation of Cardiac Auscultation Signals: Improving Model Robustness Using Synthetic Audio
School of Electrical Engineering, Computing, and Mathematical Sciences (EECMS), Faculty of Science and Engineering, Curtin University（电气工程、计算与数学科学学院（EECMS），科学与工程学院， Curtin 大学）
Electrocardiography (ECG), an electrical measurement which captures cardiac activities, is the gold standard for diagnosing cardiovascular disease (CVD). However, ECG is infeasible for continuous cardiac monitoring due to its requirement for user participation. By contrast, photoplethysmography (PPG) provides easy-to-collect data, but its limited accuracy constrains its clinical usage. To combine the advantages of both signals, recent studies incorporate various deep learning techniques for the reconstruction of PPG signals to ECG; however, the lack of contextual information as well as the lim…
University College Dublin（都柏林大学学院）；National University of Singapore（新加坡国立大学）
Sudden cardiac death and arrhythmia account for a large percentage of all deaths worldwide. Electrocardiography (ECG) is the most widely used screening tool for cardiovascular diseases. Traditionally, ECG signals are classified manually, requiring experience and great skill, while being time-consuming and prone to error. Thus machine learning algorithms have been widely adopted because of their ability to perform complex data analysis. Features derived from the points of interest in ECG - mainly Q, R, and S, are widely used for arrhythmia detection. In this work, we demonstrate improved perfor…
AnyECG: Evolved ECG Foundation Model for Holistic Health Profiling
National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学技术研究所）；Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology（同济大学同济医学院内科部）；Department of Cardiology, Peking University People’s Hospital（北京大学人民医院心内科）；Department of Epidemiology and Biostatistics, School of Public Health, Peking University（北京大学公共卫生学院流行病与生物统计学系）；Peking University Center for Public Health and Epidemic Preparedness & Response（北京大学公共卫生与突发卫生事件应对中心）；Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education（北京大学主要疾病流行病学重点实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；HeartVoice Medical Technology（心声医疗技术）
End-stage chronic kidney disease (CKD) patients are facing a 30% rise for the risk of lethal cardiac events (LCE) compared to non-CKD patients. At the same time, these patients undergoing dialysis experience shifts in the potassium concentrations. The increased risk of LCE paired with the concentration changes suggest a connection between LCE and concentration disbalances. To prove this link, a continuous monitoring device for the ionic concentrations, e.g. the ECG, is needed. In this work, we want to answer if an optimised signal processing chain can improve the result quantify the influence …
MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosis
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；VinUni-Illinois Smart Health Center, VinUniversity（VinUniversity VinUni-伊利诺伊智能健康中心）
Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological Analysis
Fudan University（复旦大学）；Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University（复旦大学附属中山医院上海市心血管病研究所）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；University of Shanghai for Science and Technology（上海理工大学）
