Peng Xiong1, Yanping Xue1, Jieshuo Zhang2, Ming Liu1, Haiman Du1, Hong Zhang3, Zengguang Hou4, Hongrui Wang1, Xiuling Liu5. 1. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China. 2. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Physics Science and Technology, Hebei University, Baoding 071002, China. 3. Affiliated Hospital of Hebei University, Baoding 071002, China. 4. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. 5. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China. Electronic address: liuxiuling121@hotmail.com.
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.
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.