Combining Convolutional Neural Network and Distance Distribution Matrix for Identification of Congestive Heart Failure

doi:10.1109/ACCESS.2018.2855420

	Combining Convolutional Neural Network and Distance Distribution Matrix for Identification of Congestive Heart Failure
	Li, Yaowei 1; Zhang, Yao 2,3; Zhao, Lina 4; Zhang, Yang 5; Liu, Chengyu 1; Zhang, Li 6; Zhang, Liuxin 5; Li, Zhensheng 5; Wang, Binhua 7; Ng, Eyk 8; Li, Jianqing 1; He, Zhiqiang 5
	2018
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	6 页码:39734-39744
摘要	Congestive heart failure (CHF) is a serious pathophysiological condition with high morbidity and mortality, which is hard to predict and diagnose in early age. Artificial intelligence and deep learning combining with cardiac rhythms and physiological time series provide a potential to help in solving it. In this paper, we proposed a novel method that combines a convolutional neural network (CNN) and a distance distribution matrix (DDM) in entropy calculation to classify CHF patients from normal subjects, and demonstrated the effectiveness of this combination. Specifically, three entropy methods were used to generate the distribution matrixes from a 300-point RR interval (i.e., the time interval between the successive cardiac cycles) time series, which are Sample entropy, fuzzy local measure entropy, and fuzzy global measure entropy. Then, three high representative CNN models, i.e., AlexNet, DenseNet, and SE_Inception_v4 were chosen to learn the pattern of the data distributions hidden in the generated distribution matrixes. All data used in our experiments were gathered from the MIT-BIH RR Interval Databases (http://www.physionet.org). A total of 29 CHF patients and 54 normal sinus rhythm subjects were included in this paper. The results showed that the combination of FuzzyGMEn-generated DDM and Inception_v4 model yielded the highest accuracy of 81.85% out of all proposed combinations.
关键词	Congestive heart failure (CHF) convolutional neural network (CNN) distance distribution matrix (DDM) heart rate variability (HRV) entropy
DOI	10.1109/ACCESS.2018.2855420
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61571113] ; National Natural Science Foundation of China[61671275] ; Key Research and Development Programs of Jiangsu Province[BE2017735] ; Fundamental Research Funds for the Central Universities[2242018k1G010] ; Southeast-Lenovo Wearable Heart-Sleep-Emotion Intelligent Monitoring Lab
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:000440954600001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：36[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5083
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Chengyu; He, Zhiqiang
作者单位	1.Southeast Univ, Sch Instrument Sci & Engn, State Key Lab Bioelect, Jiangsu Key Lab Remote Measurement & Control, Nanjing 210096, Jiangsu, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 101408, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 4.Shandong Univ, Sch Control Sci & Engn, Jinan 250000, Shandong, Peoples R China 5.Lenovo Res, Beijing 100085, Peoples R China 6.Northumbria Univ, Computat Intelligence Grp, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England 7.Chinese Peoples Liberat Army Gen Hosp, Med Big Data Ctr, Beijing 100039, Peoples R China 8.Nanyang Technol Univ, Coll Engn, Sch Mech & Aerosp Engn, Singapore 639798, Singapore
推荐引用方式 GB/T 7714	Li, Yaowei,Zhang, Yao,Zhao, Lina,et al. Combining Convolutional Neural Network and Distance Distribution Matrix for Identification of Congestive Heart Failure[J]. IEEE ACCESS,2018,6:39734-39744.
APA	Li, Yaowei.,Zhang, Yao.,Zhao, Lina.,Zhang, Yang.,Liu, Chengyu.,...&He, Zhiqiang.(2018).Combining Convolutional Neural Network and Distance Distribution Matrix for Identification of Congestive Heart Failure.IEEE ACCESS,6,39734-39744.
MLA	Li, Yaowei,et al."Combining Convolutional Neural Network and Distance Distribution Matrix for Identification of Congestive Heart Failure".IEEE ACCESS 6(2018):39734-39744.