An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains
National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；Department of Cardiology, Massachusetts General Hospital（麻省总医院心内科）；Department of Biomedical Informatics, School of Medicine, Emory University（埃默里大学医学院生物医学信息学系）；Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Harvard Medical School（哈佛医学院）；Department of Neurology, Beth Israel Deaconess Medical Center（贝斯以色列医疗中心神经科）；Department of Computing, Data Science Institute, Imperial College London（伦敦帝国学院计算系、数据科学研究所）；Zhongshan School of Medicine, Sun Yat-sen University（中山医学院，孙逸仙大学）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学技术研究所，北京大学医学部）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Jun Li, Aaron Aguirre, Junior Moura, Che Liu, Lanhai Zhong, Chenxi Sun, Gari Clifford, Brandon Westover, Shenda Hong
Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model faces several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. Additionally, there is a notable performance gap between single-lead and multi-lead ECG analyses. We introduced an ECG Foundation Model (ECGFounder), a general-purpose model that leverages real-world ECG annotations from cardiology experts to broaden the diagnostic capabilities of ECG analysis. ECGFounder was trained on over 10 million ECGs with 150 label categories from the Harvard-Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis through ECG analysis. The model is designed to be both an effective out-of-the-box solution, and a to be fine-tunable for downstream tasks, maximizing usability. Importantly, we extended its application to lower rank ECGs, and arbitrary single-lead ECGs in particular. ECGFounder is applicable to supporting various downstream tasks in mobile monitoring scenarios. Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets, with AUROC exceeding 0.95 for eighty diagnoses. It also shows strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographic analysis, clinical event detection, and cross-modality cardiac rhythm diagnosis. The trained model and data will be publicly released upon publication through the bdsp.io. Our code is available at https://github.com/PKUDigitalHealth/ECGFounder
Laboratoire des Images et des Signaux (LIS) – CNRS UMR 5083（图像与信号实验室（LIS）——法国国家科学研究中心 UMR 5083）；INPG（格勒诺布尔国立理工学院）；UJF（约瑟夫·傅里叶大学（格勒诺布尔第一大学））；Sharif University of Technology（谢里夫理工大学）；Université Catholique de Louvain (UCL)（鲁汶天主教大学（UCL））；Laboratoire Systémes Complexes (LSC) – CNRS FRE 2494（复杂系统实验室（LSC）——法国国家科学研究中心 FRE 2494）
Blind source separation (BSS) techniques have revealed to be promising approaches for, among other, biomedical signal processing applications. Specifically, for the noninvasive extraction of fetal cardiac signals from maternal abdominal recordings, where conventional filtering schemes have failed to extract the complete fetal ECG components. From previous studies, it is now believed that a carefully selected array of electrodes well-placed over the abdomen of a pregnant woman contains the required `information' for BSS, to extract the complete fetal components. Based on this idea, in previous …
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）
HeartVoice Medical Technology（心音医疗科技）；University of Science and Technology of China（中国科学技术大学）
Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al. However, traditional ECG disease detection models show substantial rates of misdiagnosis due to the limitations of the abilities of extracted features. Recent deep learning methods have shown significant advantages, but they do not provide publicly available services for those who have no training data or computational resources. In this paper, we demonstra…
The Ohio State University（俄亥俄州立大学）；Wuhan University of Technology（武汉理工大学）
Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diagnosis of ECG signals has become a hot research topic. Deep learning methods have demonstrated promising results in predictive healthcare tasks. In this paper, we developed a deep neural network for multi-label classification of cardiac arrhythmias in 12-lead ECG recordings. Experiments on a public 12-lead ECG dataset showed the effectiveness of our method. The proposed model ac…
National Tsing Hua University（国立清华大学）；Inventec Corporation（英业达公司）
Electrocardiogram(ECG) is commonly used to detect cardiac irregularities such as atrial fibrillation, bradycardia, and other irregular complexes. While previous studies have achieved great accomplishment classifying these irregularities with standard 12-lead ECGs, there existed limited evidence demonstrating the utility of reduced-lead ECGs in capturing a wide-range of diagnostic information. In addition, classification model's generalizability across multiple recording sources also remained uncovered. As part of the PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC, propo…
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））
We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The first architecture is a deep convolutional neural network (CNN) with averaging-based feature aggregation across time. The second architecture combines convolutional layers for feature extraction with long-short term memory (LSTM) layers for temporal aggregation of features. As a key ingredient of our training procedure we introduce a simple data augm…
Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；National University of Singapore（新加坡国立大学）
Atrial Fibrillation (AF) is a common cardiac arrhythmia affecting a large number of people around the world. If left undetected, it will develop into chronic disability or even early mortality. However, patients who have this problem can barely feel its presence, especially in its early stage. A non-invasive, automatic, and effective detection method is therefore needed to help early detection so that medical intervention can be implemented in time to prevent its progression. Electrocardiogram (ECG), which records the electrical activities of the heart, has been widely used for detecting the p…
ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning
Institute of Medical Informatics, University Münster, Germany（医学信息学研究所，穆斯特大学，德国）；Clinic for Cardiology II: Electrophysiology, University Hospital Münster, Germany（心脏病学二诊所：电生理学，穆斯特大学医院，德国）
Virgen Del Rocío University Hospital（罗西奥圣母大学医院）；Universidad San Jorge（圣豪尔赫大学）；Pompeu Fabra University（庞培法布拉大学）
Obtaining per-beat information is a key task in the analysis of cardiac electrocardiograms (ECG), as many downstream diagnosis tasks are dependent on ECG-based measurements. Those measurements, however, are costly to produce, especially in recordings that change throughout long periods of time. However, existing annotated databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically compos…
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）
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction…
International Institute of Information Technology（国际信息技术研究所）
Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progressed in recent years to reach cardiologist-level performance. In clinical settings, a cardiologist makes a diagnosis based on the standard 12-channel ECG recording. Automatic analysis of ECG recordings from a multiple-channel perspective has not been given enough attention, so it is essential to analyze an ECG recording from a multiple-channel perspective. We propose a model tha…
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。
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）
Electrocardiogram (ECG) detection and delineation are key steps for numerous tasks in clinical practice, as ECG is the most performed non-invasive test for assessing cardiac condition. State-of-the-art algorithms employ digital signal processing (DSP), which require laborious rule adaptation to new morphologies. In contrast, deep learning (DL) algorithms, especially for classification, are gaining weight in academic and industrial settings. However, the lack of model explainability and small databases hinder their applicability. We demonstrate DL can be successfully applied to low interpretati…
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）
With the development of deep learning-based methods, automated classification of electrocardiograms (ECGs) has recently gained much attention. Although the effectiveness of deep neural networks has been encouraging, the lack of information given by the outputs restricts clinicians' reexamination. If the uncertainty estimation comes along with the classification results, cardiologists can pay more attention to "uncertain" cases. Our study aims to classify ECGs with rejection based on data uncertainty and model uncertainty. We perform experiments on a real-world 12-lead ECG dataset. First, we es…
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）
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）
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…
Geisinger（盖辛格医疗系统）；Biomedical and Translational Informatics Institute, Geisinger（盖辛格生物医学与转化信息学研究所）；Bucknell University（巴克内尔大学）；Heart Institute, Geisinger（盖辛格心脏研究所）；University of Kentucky（肯塔基大学）
The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart. Here we hypothesize that a deep neural network can predict an important future clinical event (one-year all-cause mortality) from ECG voltage-time traces. We show good performance for predicting one-year mortality with an average AUC of 0.85 from a model cross-validated on 1,775,926 12-lead resting ECGs, that were collected over a 34-year period in a large regional health system. Even within the large subset of ECGs interpreted as 'n…
Due to the multiple imperfections during the signal acquisition, Electrocardiogram (ECG) datasets are typically contaminated with numerous types of noise, like salt and pepper and baseline drift. These datasets may contain different recordings with various types of noise [1] and thus, denoising may not be the easiest task. Furthermore, usually, the number of labeled bio-signals is very limited for a proper classification task.
Modern wearable devices are embedded with a range of noninvasive biomarker sensors that hold promise for improving detection and treatment of disease. One such sensor is the single-lead electrocardiogram (ECG) which measures electrical signals in the heart. The benefits of the sheer volume of ECG measurements with rich longitudinal structure made possible by wearables come at the price of potentially noisier measurements compared to clinical ECGs, e.g., due to movement. In this work, we develop a statistical model to simulate a structured noise process in ECGs derived from a wearable sensor, d…
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）
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）
Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combi…
School of Computer Science and Technology, Harbin Institute of Technology (HIT)（哈尔滨工业大学计算机科学与技术学院）；School of Physics and Astronomy, The University of Manchester（曼彻斯特大学物理与天文学院）；SPACEnter Space Science and Technology Institute（SPACEnter空间科学与技术研究院）；Northeastern University（东北大学）；Beijing Electric Power Hospital（北京电力医院）
Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for de…
OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records
The First Affiliated Hospital, Jiangxi Medical College, Nanchang University（南昌大学江西医学院第一附属医院）；Nanchang University（南昌大学）；Georgia Institution of Technology（佐治亚理工学院）
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）
Decoding individual words from non-invasive brain recordings across 723 participants
Meta AI；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；CEA（法国原子能和替代能源委员会）；Neurospin center（神经自旋中心）；École Normale Supérieure（巴黎高等师范学院）；PSL University（巴黎文理研究大学）；Laboratoire des Systèmes Perceptifs（感知系统实验室）
University of Alberta（阿尔伯塔大学）；Canadian VIGOUR Centre（加拿大VIGOUR中心）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient's first in-hospital …
Stanford Online High School（斯坦福在线高中）；AliveCor, Inc.（AliveCor公司）
Mobile electrocardiogram (ECG) recording technologies represent a promising tool to fight the ongoing epidemic of cardiovascular diseases, which are responsible for more deaths globally than any other cause. While the ability to monitor one's heart activity at any time in any place is a crucial advantage of such technologies, it is also the cause of a drawback: signal noise due to environmental factors can render the ECGs illegible. In this work, we develop convolutional neural networks (CNNs) to automatically label ECGs for noise, training them on a novel noise-annotated dataset. By reducing …
BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Diagnosis
School of Information Technology（信息科技学院）；Monash University Malaysia（墨尔本大学马来西亚分校）；Faculty of Biomedical Engineering（生物医学工程学院）；Shenzhen University of Advanced Technology（深圳先进技术大学）
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）
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
City University of Hong Kong（香港城市大学）；Imperial College London（伦敦帝国学院）；Wake Forest University（威克森林大学）；University of Nottingham（诺丁汉大学）；Lingnan University（岭南大学）；University of Oxford（牛津大学）
University of Moratuwa（莫拉图瓦大学）；Lady Ridgeway Hospital for Children（里奇韦夫人儿童医院）
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 Dua…
HSE University（高等经济大学）；Sber AI Lab（俄联邦储蓄银行人工智能实验室）；Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））
Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting abnormalities in cardiac activities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel method to detect the disease early could prevent death and complication. The paper presents novel various approaches for classifying cardiac diseases from ECG recordings. The first approach suggests the Poincare representation of ECG signal and deep-learning-based image classifiers …
Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings
University College London（伦敦大学学院）；Universidade dos Açores（亚速尔大学）；Institute of Marine Sciences, CSIC（西班牙国家研究委员会海洋科学研究所）；University of St Andrews（圣安德鲁斯大学）；Instituto do Mar (IMAR)（海洋研究所（IMAR））
IBM Research - Africa（IBM非洲研究院）；University of Oxford（牛津大学）；Guangdong Cardiovascular Institute（广东省心血管病研究所）
Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enable timely interventions that may improve patient outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram (ECG) recordings are currently used to screen MI patients. However, manual inspection of ECGs is time-consuming and prone to subjective bias. Machine learning methods have been adopted for automated ECG diagnosis, but most approaches require extraction of ECG beats or consider leads independentl…
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many research labs for automatic interpretation of ECG signals to identify potential abnormalities in patient hearts. Studies have shown that given a sufficiently large amount of data, the classification accuracy of DNNs could reach human-expert cardiologist level. However, despite of the excellent performance in classification accuracy, it has been shown that DNNs are highly vulnerable to adversarial noises which are subtle …
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Uppsala University（乌普萨拉大学）；Hospital das Clínicas da Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学临床医院）；Glasgow University（格拉斯哥大学）
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electroca…
CardioRAG: A Retrieval-Augmented Generation Framework for Multimodal Chagas Disease Detection
Department of Electrical and Electronic Engineering Imperial College London（帝国理工学院电子与电气工程系）；Department of Civil and Environmental Engineering Imperial College London（帝国理工学院土木与环境工程系）；Institute of Biomedical Engineering Department of Engineering Science University of Oxford（牛津大学生物医学工程研究所）
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Uppsala University（乌普萨拉大学）；Telehealth Center from Hospital das Clínicas da Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学医院临床中心远程医疗中心）；Hospital das Clínicas da Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学医院临床中心（注：此处原文本中“Telehealth Center from Hospital das Clínicas da Universidade Federal de Minas Gerais”中的“Hospital das Clínicas da Universidade Federal de Minas Gerais”是更基础的机构，按要求提取到大学级别或包含的独立机构，此处直接提取该医院作为机构名））
We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large telehealth network, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consist of 12-lead ECGs recorded during standard i…
