Automated Detection and Localization of Myocardial Infarction With staked Sparse Autoencoder and TreeBagger

doi:10.1109/ACCESS.2019.2919068

	Automated Detection and Localization of Myocardial Infarction With staked Sparse Autoencoder and TreeBagger
	Zhang, Jieshuo 1; Lin, Feng 2; Xiong, Peng 1; Du, Haiman 1; Zhang, Hong 3; Liu, Ming 1; Hou, Zengguang4 ; Liu, Xiuling 1
发表期刊	IEEE ACCESS
ISSN	2169-3536
	2019
卷号	7 页码:70634-70642
通讯作者	Lin, Feng(asflin@ntu.edu.sg) ; Liu, Xiuling(liuxiuling121@hotmail.com)
摘要	Novel techniques in deep learning networks are proposed for the staked sparse autoencoder (SAE) and the bagged decision tree (TreeBagger), achieving significant improvement in detection and localization of myocardial infarction (MI) from single-lead electrocardiograph (ECG) signals. With our layer-wise training strategies, the SAE-based diagnostic feature extraction network can automatically and steadily extract the deep distinguishing diagnostic features of the single-lead ECG signals and avoid the vanishing gradient problem. This feature extraction network is formed by stacking shallow SAEs. In addition, to automatically learn the stable distinctive feature expression of the label-less input ECG signals, this feature extraction network adopts unsupervised learning. Moreover, TreeBagger classifier can optimize the results of multiple decision trees to more accurately detect and localize MI. The experiment and verification datasets include healthy controls, various types of MI with anterior, anterior lateral, anterior septal, anterior septal lateral, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral, from PTB diagnostic ECG database. The evaluation results show that the new techniques can effectively and accurately detect and localize the MI pathologies. For MI detection, the accuracy, the sensitivity, and the specificity rates achieve as high as 99.90%, 99.98%, and 99.52%, respectively. For MI localization, we obtain consistent results with the accuracy of 98.88%, sensitivity 99.95%, and specificity 99.87%. The comparative studies are conducted with the state-of-the-art techniques, and significant improvements by our methods are presented in the context. Success in the development of the accurate and comprehensive tool greatly helps the cardiologists in detection and localization of the single-lead ECG signals of MI.
关键词	Electrocardiograph myocardial infarction sparse autoencoder bagged decision tree deep learning networks
DOI	10.1109/ACCESS.2019.2919068
关键词[WOS]	CONVOLUTIONAL NEURAL-NETWORK ; ECG SIGNALS ; CLASSIFICATION ; SELECTION
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] ; Funds for Distinguished Young Scientists of Hebei Province[F2016201186] ; Hundreds of Outstanding Innovative Talent Support Plans for Colleges and Universities in Hebei Province[SLRC2017022] ; Natural Science Foundation of Hebei Province[F2017201222] ; Natural Science Foundation of Hebei Province[F2018201070] ; National Natural Science Foundation of China[61673158] ; National Natural Science Foundation of China[61703133] ; Funds for Distinguished Young Scientists of Hebei Province[F2016201186] ; Hundreds of Outstanding Innovative Talent Support Plans for Colleges and Universities in Hebei Province[SLRC2017022] ; Natural Science Foundation of Hebei Province[F2017201222] ; Natural Science Foundation of Hebei Province[F2018201070]
项目资助者	National Natural Science Foundation of China ; Funds for Distinguished Young Scientists of Hebei Province ; Hundreds of Outstanding Innovative Talent Support Plans for Colleges and Universities in Hebei Province ; Natural Science Foundation of Hebei Province
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000471856400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：33[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/173211/26040
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Lin, Feng; Liu, Xiuling
作者单位	1.Hebei Univ, Coll Elect & Informat Engn, Key Lab Digital Med Engn Hebei Prov, Baoding 071000, Peoples R China 2.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore 3.Hebei Univ, Affiliated Hosp, Baoding 071000, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Jieshuo,Lin, Feng,Xiong, Peng,et al. Automated Detection and Localization of Myocardial Infarction With staked Sparse Autoencoder and TreeBagger[J]. IEEE ACCESS,2019,7:70634-70642.
APA	Zhang, Jieshuo.,Lin, Feng.,Xiong, Peng.,Du, Haiman.,Zhang, Hong.,...&Liu, Xiuling.(2019).Automated Detection and Localization of Myocardial Infarction With staked Sparse Autoencoder and TreeBagger.IEEE ACCESS,7,70634-70642.
MLA	Zhang, Jieshuo,et al."Automated Detection and Localization of Myocardial Infarction With staked Sparse Autoencoder and TreeBagger".IEEE ACCESS 7(2019):70634-70642.