ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction
Nathan T. Riek, Murat Akcakaya, Zeineb Bouzid, Tanmay Gokhale, Stephanie Helman, Karina Kraevsky-Philips, Rui Qi Ji, Ervin Sejdic, Jessica K. Zègre-Hemsey, Chri… 展开作者
Nathan T. Riek, Murat Akcakaya, Zeineb Bouzid, Tanmay Gokhale, Stephanie Helman, Karina Kraevsky-Philips, Rui Qi Ji, Ervin Sejdic, Jessica K. Zègre-Hemsey, Christian Martin-Gill, Clifton W. Callaway, Samir Saba, Salah Al-Zaiti
Objective: In this paper we develop and evaluate ECG-SMART-NET for occlusion myocardial infarction (OMI) identification. OMI is a severe form of heart attack characterized by complete blockage of one or more coronary arteries requiring immediate referral for cardiac catheterization to restore blood flow to the heart. Two thirds of OMI cases are difficult to visually identify from a 12-lead electrocardiogram (ECG) and can be potentially fatal if not identified quickly. Previous works on this topic are scarce, and current state-of-the-art evidence suggests both feature-based random forests and convolutional neural networks (CNNs) are promising approaches to improve ECG detection of OMI. Methods: While the ResNet architecture has been adapted for use with ECG recordings, it is not ideally suited to capture informative temporal features within each lead and the spatial concordance or discordance across leads. We propose a clinically informed modification of the ResNet-18 architecture. The model first learns temporal features through temporal convolutional layers with 1xk kernels followed by a spatial convolutional layer, after the residual blocks, with 12x1 kernels to learn spatial features. Results: ECG-SMART-NET was benchmarked against the original ResNet-18 and other state-of-the-art models on a multisite real-word clinical dataset that consists of 10,393 ECGs from 7,397 unique patients (rate of OMI =7.2%). ECG-SMART-NET outperformed other models in the classification of OMI with a test AUC of 0.953 [0.921, 0.978]. Conclusion and Significance: ECG-SMART-NET can outperform the state-of-the-art random forest for OMI prediction and is better suited for this task than the original ResNet-18 architecture.
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Purpose: A new method for magnetic resonance (MR) imaging water-fat separation using a convolutional neural network (ConvNet) and deep learning (DL) is presented. Feasibility of the method with complex and magnitude images is demonstrated with a series of patient studies and accuracy of predicted quantitative values is analyzed. Methods: Water-fat separation of 1200 gradient-echo acquisitions from 90 imaging sessions (normal, acute and chronic myocardial infarction) was performed using a conventional model based method with modeling of R2* and off-resonance and a multi-peak fat spectrum. A U-N…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Beckman Institute for Advanced Science and Technology（贝克曼先进科学技术研究所）；Carle Illinois College of Medicine（卡尔伊利诺伊医学院）
Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from the v6, vz, and ii leads are critical to correctly identify myocardial infarction. Second, we use this finding and adapt the ConvNetQuake neural network model--originally designed to identify earthquakes--to attain state-of-the-art classification results for myocardial infarction,…
Self-Alignment Learning to Improve Myocardial Infarction Detection from Single-Lead ECG
National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Data Science Institute, Imperial College London（伦敦帝国学院数据科学学院）
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
Tampere University（坦佩雷大学）；VTT Technical Research Centre of Finland（芬兰VTT技术研究中心）；Haltian（哈尔蒂安公司）；Qatar University（卡塔尔大学）；Hamad Medical Corporation（哈马德医疗公司）
A comprehensive survey revealed that many patients of Acute Myocardial Infarction reach to the hospital so late to deliver an effective treatment. This leads to poor treatment outcome which increases the mortality rates. Multiple reasons such as lack of diagnostic facilities in the rural health care centers and absence of cardiologists in Emergency Rooms inhibit the timely treatment. In this study, we aimed to develop an effective system to pre-hospital management of Acute Myocardial Infarction. The effectiveness of early thrombolysis in Myocardial Infarction is well established, particularly …
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration
Department of Computing, and Mathematics, Manchester Metropolitan University（计算机与数学系，曼彻斯特 Metropolitan 大学）；Blackpool Teaching Hospitals NHS Foundation Trust（布莱克浦教学医院 NHS 基础信托）；Northern Care Alliance, NHS Foundation Trust（北方护理联盟，NHS 基础信托）；Department of Engineering, Manchester Metropolitan University（工程系，曼彻斯特 Metropolitan 大学）；Faculty of Business and Law, Manchester Metropolitan University（商业与法律学院，曼彻斯特 Metropolitan 大学）；Faculty of Medical Sciences, Newcastle University（医学科学学院，纽卡斯尔大学）
Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data
École de technologie supérieure, Université du Québec（魁北克大学高等技术学院）；K. N. Toosi University of Technology（K. N. 图什理工大学）；Centre de recherche de l’Institut universitaire de gériatrie de Montréal（蒙特利尔大学老年学研究所）
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…
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）
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）
University of Birjand（比尔詹德大学）；Mazandaran University of Science and Technology（马赞达兰科技大学）；University of Qom（库姆大学）；University of Genova（热那亚大学）；Deakin University（迪肯大学）；Bu-Ali Sina University（布-阿里·西纳大学）；Maragheh Faculty of Medical Sciences（马拉盖医学院）；Obuda University（奥布达大学）；Singapore University of Social Sciences（新加坡社会科学大学）；National Heart Centre Singapore（新加坡国家心脏中心）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other bio…
University of Jeddah（吉达大学）；Menoufia University（米努菲亚大学）；University of Bradford（布拉德福德大学）；Sejong University（世宗大学）；Misr Higher Institute of Commerce and Computers（米斯尔高等商业与计算机学院）；Nile University（尼罗河大学）；Harbin Institute of Technology（哈尔滨工业大学）
. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG…
Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the stateof-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fus…
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature extraction and classification and thus employ end to end deep learning. We perfo…
University of Southern California（南加州大学）；Universität für Musik und darstellende Kunst Graz（格拉茨音乐与表演艺术大学）
Deep learning methods have shown suitability for time series classification in the health and medical domain, with promising results for electrocardiogram data classification. Successful identification of myocardial infarction holds life saving potential and any meaningful improvement upon deep learning models in this area is of great interest. Conventionally, data augmentation methods are applied universally to the training set when data are limited in order to ameliorate data resolution or sample size. In the method proposed in this study, data augmentation was not applied in the context of …
MEETI: A Multimodal ECG Dataset from MIMIC-IV-ECG with Signals, Images, Features and Interpretations
HeartVoice Medical Technology（HeartVoice医疗科技）；Saw Swee Hock School of Public Health and Institute of Data Science（Saw Swee Hock公共卫生学院和数据科学研究所）；National University of Singapore（新加坡国立大学）；Department of Cardiology, Peking University People’s Hospital（北京大学人民医院心内科）；College of Integrative Chinese and Western Medicine, Anhui University of Chinese Medicine（安徽中医药大学整合中西医学学院）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
MedalCare-XL: 16,900 healthy and pathological 12 lead ECGs obtained through electrophysiological simulations
Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院生物医学工程研究所）；Physikalisch-Technische Bundesanstalt, National Metrology Institute（德国联邦物理技术研究院）；King’s College London（伦敦国王学院）；University of Edinburgh（爱丁堡大学）
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）
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）；Charité Universitätsmedizin Berlin（柏林夏里特医学院）；Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学）
National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Department of Cardiology, Peking University People’s Hospital（北京大学人民医院心内科）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术学院）；Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University（天津医科大学第二医院心脏研究所心内科离子-分子心血管功能天津市重点实验室）
