Multi-branch fusion network for Myocardial infarction screening from 12-lead ECG images. (February 2020)

• Record Type: Journal Article
• Authors: Hao, Pengyi; Gao, Xiang; Li, Zhihe; Zhang, Jinglin; Wu, Fuli; Bai, Cong
• Is Part Of: Computer methods and programs in biomedicine. Volume 184(2020)
• Publication Date: 2020-02
• Subjects: Myocardial infarction -- ECG Images -- Multi-branch fusion network; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105286
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new model named multi branch fusion network was proposed to analyze 12 lead ECG images like printed ECGs and screenshots of ECGs, which is not limited by different ECG devices and their different sampling rates. Rather than using the typical time domain electrical ECG signal, analyzing ECG images is an efficient way for MI screening, which is much similar to the physician's diagnosis process. The proposed arch itecture enables features of ECG images to be extracted effectively and reaches human level performance with the trade off between high performance and simple prepossessing of the limited data. Abstract: 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.