Localization of myocardial infarction with multi-lead ECG based on DenseNet. (May 2021)

• Record Type: Journal Article
• Authors: Xiong, Peng; Xue, Yanping; Zhang, Jieshuo; Liu, Ming; Du, Haiman; Zhang, Hong; Hou, Zengguang; Wang, Hongrui; Liu, Xiuling
• Is Part Of: Computer methods and programs in biomedicine. Volume 203(2021)
• Publication Date: 2021-05
• Subjects: Myocardial infarction -- Multi-lead ECG -- DenseNet -- Structural characteristics; 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.2021.106024
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : highlights: A novel densely connected convolutional network for multi-lead MI localization. The 12-lead ECG is processed in parallel for taking correlation of multi-lead. The dense connection makes the reuse of pathological features in MI localization. The MI localization achieves a great performance with accuracy of 99.87%. Abstract: Background and Objective: Myocardial infarction (MI) is a critical acute ischemic heart disease, which can be early diagnosed by electrocardiogram (ECG). However, the most research of MI localization pay more attention on the specific changes in every ECG lead independent. In our study, the research envisages the development of a novel multi-lead MI localization approach based on the densely connected convolutional network (DenseNet). Methods: Considering the correlation of the multi-lead ECG, the method using parallel 12-lead ECG, systematically exploited the correlation of the inter-lead signals. In addition, the dense connection of DenseNet enhanced the reuse of the feature information between the inter-lead and intra-lead signals. The proposed method automatically captured the effective pathological features, which improved the identification of MI. Results: The experimental results based on PTB diagnostic ECG database showed that the accuracy, sensitivity and specificity of the proposed method was 99.87%, 99.84% and 99.98% for 11 types of MI localization. Conclusions: The proposed method has achieved superior results compared to other localization methods, which can be introduced into the clinical practice to assist the diagnosis of MI.