A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction

doi:10.1016/j.engappai.2020.104092

	A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction
	Zhang, Jieshuo 1,2; Liu, Ming 1; Xiong, Peng 1; Du, Haiman 1; Zhang, Hong 3; Lin, Feng 4; Hou, Zengguang5 ; Liu, Xiuling 1
发表期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
ISSN	0952-1976
	2021
卷号	97 页码:9
通讯作者	Liu, Ming(liuxiuling121@hotmail.com) ; Liu, Xiuling(liuxiuling121@hotmail.com)
摘要	Developing an accurate and automatic algorithm for detection and localization of myocardial infarction (MI) remains a great challenge for multi-lead electrocardiograph (ECG) signals. The core is a novel technique of multi-dimensional association information analysis for a multi-lead ECG tensor. Tensorization based on Discrete Wavelet Transform is investigated to construct an effective ECG tensor containing multi-dimensional association information from 12-lead ECG signals. The multi-lead feature extraction algorithm based on Parallel Factor Analysis is developed to automatically extract the low-dimensional and highly recognizable lead characteristic features of the tensor. After that a bagged decision tree is constructed to categorize 12 types of heartbeats, healthy controls and 11 kinds of MI, from the lead features. Using the PTB database, we compare with the existing MI diagnosis methods. For MI detection, significant improvement of the accuracy, sensitivity and specificity are achieved; as high as 99.88%, 99.98% and 99.39% respectively. Furthermore, an experiment with 36-dimensional features obtained from the ECG tensor is conducted for the localization of 11 kinds of MI, and our proposed method achieved an accuracy of 99.40%, sensitivity of 99.86%, and specificity of 99.89%. The proposed algorithm can effectually accomplish the localization of 11 categories of MI by using the lead features extracted from the multi-dimensional association ECG tensor, which has not been achieved in literature. The accurate and comprehensive tool development will greatly help cardiologists diagnose 12-lead ECG signals of MI.
关键词	Myocardial infarction Electrocardiograph Multi-dimensional association tensor Parallel factor analysis Bagged decision tree
DOI	10.1016/j.engappai.2020.104092
关键词[WOS]	CONVOLUTIONAL NEURAL-NETWORK ; ECG ; ELECTROCARDIOGRAM ; CLASSIFICATION ; DIAGNOSIS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61673158] ; National Natural Science Foundation of China[61703133] ; Hundreds of outstanding innovative talent support plans for colleges and universities in Hebei Province, China[SLRC2017022] ; Natural Science Foundation of Hebei Province, China[F2017201222] ; Natural Science Foundation of Hebei Province, China[F2018201070] ; National Key Research and Development Program of China[2017YFB1401200] ; personnel training project of Hebei Province, China[A2016002012]
项目资助者	National Natural Science Foundation of China ; Hundreds of outstanding innovative talent support plans for colleges and universities in Hebei Province, China ; Natural Science Foundation of Hebei Province, China ; National Key Research and Development Program of China ; personnel training project of Hebei Province, China
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS记录号	WOS:000596371100001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42834
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Liu, Ming; Liu, Xiuling
作者单位	1.Hebei Univ, Coll Elect & Informat Engn, Key Lab Digital Med Engn Hebei Prov, Baoding 071002, Peoples R China 2.Hebei Univ, Coll Phys Sci & Technol, Baoding 071002, Peoples R China 3.Hebei Univ, Affiliated Hosp, Baoding 071002, Peoples R China 4.Nanyang Technol Univ, Coll Comp Engn, Singapore 639798, Singapore 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Jieshuo,Liu, Ming,Xiong, Peng,et al. A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2021,97:9.
APA	Zhang, Jieshuo.,Liu, Ming.,Xiong, Peng.,Du, Haiman.,Zhang, Hong.,...&Liu, Xiuling.(2021).A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,97,9.
MLA	Zhang, Jieshuo,et al."A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 97(2021):9.