Localization of myocardial infarction with multi-lead ECG based on DenseNet

doi:10.1016/j.cmpb.2021.106024

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Localization of myocardial infarction with multi-lead ECG based on DenseNet
	Xiong, Peng 4,5; Xue, Yanping 4,5; Zhang, Jieshuo 1,4; Liu, Ming 4,5; Du, Haiman 4,5; Zhang, Hong 2; Hou, Zengguang 3; Wang, Hongrui 4,5; Liu, Xiuling 4,5
刊名	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
	2021-05-01
卷号	203 页码:8
关键词	Multi-lead ECG DenseNet Structural characteristics Myocardial infarction
ISSN号	0169-2607
DOI	10.1016/j.cmpb.2021.106024
通讯作者	Liu, Xiuling(liuxiuling121@hotmail.com)
英文摘要	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. (c) 2021 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China[61673158] ; National Natural Science Foundation of China[61703133] ; Natural Science Foundation of Hebei Province[F2018201070] ; Youth Talent Support Program of Hebei Province[BJ2019044]
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
语种	英语
出版者	ELSEVIER IRELAND LTD
WOS记录号	WOS:000639852700010
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province ; Youth Talent Support Program of Hebei Province
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44349]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Liu, Xiuling
作者单位	1.Hebei Univ, Coll Phys Sci & Technol, Baoding 071002, Peoples R China 2.Hebei Univ, Affiliated Hosp, Baoding 071002, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Key Lab Digital Med Engn Hebei Prov, Baoding 071000, Peoples R China 5.Hebei Univ, Coll Elect & Informat Engn, 180 East Wusi Rd, Baoding 071002, Peoples R China
推荐引用方式 GB/T 7714	Xiong, Peng,Xue, Yanping,Zhang, Jieshuo,et al. Localization of myocardial infarction with multi-lead ECG based on DenseNet[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2021,203:8.
APA	Xiong, Peng.,Xue, Yanping.,Zhang, Jieshuo.,Liu, Ming.,Du, Haiman.,...&Liu, Xiuling.(2021).Localization of myocardial infarction with multi-lead ECG based on DenseNet.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,203,8.
MLA	Xiong, Peng,et al."Localization of myocardial infarction with multi-lead ECG based on DenseNet".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 203(2021):8.