Multi-branch fusion network for Myocardial infarction screening from 12-lead ECG images
Multi-branch fusion network for Myocardial infarction screening from 12-lead ECG images
复制标题
用于从 12 导联心电图图像筛查心肌梗死的多分支融合网络
DOI：
10.1016/j.cmpb.2019.105286
复制
发表时间：
2020-02-01
影响因子：
6.1
通讯作者：
Bai, Cong
中科院分区：
文献类型：
--
作者：
Hao, Pengyi;Gao, Xiang;Bai, Cong
背景和目的：心肌梗死（MI）是由严重心血管阻塞引起的心肌缺氧性失能，可导致不可逆的损伤甚至死亡。在医学领域，心电图（ECG）是诊断心肌梗死的一种常用而有效的方法，这往往需要丰富的医学知识。方法：本文提出了一种基于多分支网络、特征融合和分类网络的12导联心电图MI自动筛查框架。首先，我们使用文本检测和位置对齐自动分离十二导联心电图图像。然后将这12个导联输入到由浅层神经网络构造的多分支网络中，得到12个特征图。通过深度融合将这些特征图连接起来，然后进行分类以判断给定的ECG是否为MI。结果：基于ECG图像数据集的大量实验，分析了不同结构组合的性能。将该网络与其他网络进行了比较，并与实际使用中的医生进行了比较。实验结果表明，本文提出的方法是一种有效的基于心电图的心肌梗死筛查方法，其准确率、灵敏度、特异度和F1值分别达到94.73%、96.41%、95.94%和93.79%.结论：本文提出的方法不使用典型的一维心电图信号，而是通过分析12导联心电图信号，建立了一种有效的心肌梗死筛查模型。从相应的心电图中提取和分析这12个导联是在心肌梗死筛查应用中的一个很好的尝试。(C)2019由Elsevier B.V.出版
Background and Objective: Myocardial infarction (MI) is a myocardial anoxic incapacitation caused by severe cardiovascular obstruction that can cause irreversible injury or even death. In medical field, the electrocardiogram (ECG) is a common and effective way to diagnose myocardial infarction, which often requires a wealth of medical knowledge. It is necessary to develop an approach that can detect the MI automatically.Methods: In this paper, we propose a multi-branch fusion framework for automatic MI screening from 12-lead ECG images, which consists of multi-branch network, feature fusion and classification network. First, we use text detection and position alignment to automatically separate twelve leads from ECG images. Then, those 12 leads are input into the multi-branch network constructed by a shallow neural network to get 12 feature maps. After concatenating those feature maps by depth fusion, classification is explored to judge the given ECG is MI or not.Results: Based on extensive experiments on an ECG image dataset, performances of different combinations of structures are analyzed. The proposed network is compared with other networks and also compared with physicians in the practical use. All the experiments verify that the proposed method is effective for MI screening based on ECG images, which achieves accuracy, sensitivity, specificity and F1-score of 94.73%, 96.41%, 95.94% and 93.79% respectively.Conclusions: Rather than using the typical one-dimensional electrical ECG signal, this paper gives an effective model to screen MI by analyzing 12-lead ECG images. Extracting and analyzing these 12 leads from their corresponding ECG images is a good attempt in the application of MI screening. (C) 2019 Published by Elsevier B.V.