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 independently of one another. We propose an end-to-end deep learning approach, DeepMI, to classify MI from normal cases as well as identifying the time-occurrence of MI (defined as acute, recent and old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level. In order to minimise computational overhead, we employ transfer learning using existing computer vision networks. Moreover, we use recurrent neural networks to encode the longitudinal information inherent in ECGs. We validated DeepMI on a dataset collected from 17,381 patients, in which over 323,000 samples were extracted per ECG lead. We were able to classify normal cases as well as acute, recent and old onset cases of MI, with AUROCs of 96.7%, 82.9%, 68.6% and 73.8%, respectively. We have demonstrated a multi-lead fusion approach to detect the presence and occurrence-time of MI. Our end-to-end framework provides flexibility for different levels of multi-lead ECG fusion and performs feature extraction via transfer learning.
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…
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,…
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 …
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）
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）
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…
Automatic and accurate segmentation of the ventricles and myocardium from multi-sequence cardiac MRI (CMR) is crucial for the diagnosis and treatment management for patients suffering from myocardial infarction (MI). However, due to the existence of domain shift among different modalities of datasets, the performance of deep neural networks drops significantly when the training and testing datasets are distinct. In this paper, we propose an unsupervised domain alignment method to explicitly alleviate the domain shifts among different modalities of CMR sequences, \emph{e.g.,} bSSFP, LGE, and T2…
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…
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）
Fifth-generation (5G) cellular networks promise higher data rates, lower latency, and large numbers of interconnected devices. Thereby, 5G will provide important steps towards unlocking the full potential of the Internet of Things (IoT). In this work, we propose a lightweight IoT platform for continuous vital sign analysis. Electrocardiography (ECG) is acquired via textile sensors and continuously sent from a smartphone to an edge device using cellular networks. The edge device applies a state-of-the art deep learning model for providing a binary end-to-end classification if a myocardial infar…
