Hao Pengyi, Gao Xiang, Li Zhihe, Zhang Jinglin, Wu Fuli, Bai Cong
College of Computer Science and Technology, Zhejiang University of Technology, hangzhou, China.
School of Atmospheric Science, Nanjing University of Information Science, China.
Comput Methods Programs Biomed. 2020 Feb;184:105286. doi: 10.1016/j.cmpb.2019.105286. Epub 2019 Dec 17.
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.
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.
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.
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.
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见且有效的方法，但这通常需要丰富的医学知识。因此，开发一种能够自动检测心肌梗死的方法很有必要。
本文提出了一种用于从12导联心电图图像中自动筛选心肌梗死的多分支融合框架，该框架由多分支网络、特征融合和分类网络组成。首先，我们使用文本检测和位置对齐从心电图图像中自动分离出12个导联。然后，将这12个导联输入由浅层神经网络构建的多分支网络中，得到12个特征图。通过深度融合将这些特征图拼接后，进行分类以判断给定的心电图是否为心肌梗死。
基于在一个心电图图像数据集上进行的大量实验，分析了不同结构组合的性能。将所提出的网络与其他网络进行了比较，并且在实际应用中与医生进行了比较。所有实验均验证了所提出的基于心电图图像筛选心肌梗死的方法是有效的，其准确率、灵敏度、特异度和F1分数分别达到了94.73%、96.41%、95.94%和93.79%。
本文并非使用典型的一维心电图电信号，而是通过分析12导联心电图图像给出了一种筛选心肌梗死的有效模型。从相应的心电图图像中提取并分析这12个导联是心肌梗死筛选应用中的一次良好尝试。
