Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure

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 solvin...

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Main Authors: Li, Yaowei, Zhang, Yao, Zhao, Lina, Zhang, Yang, Liu, Chengyu, Zhang, Li, Zhang, Liuxin, Li, Zhensheng, Wang, Binhua, Ng, Eyk, Li, Jianqing, He, Zhiqiang
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2018
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Online Access:https://hdl.handle.net/10356/88148
http://hdl.handle.net/10220/45646
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-881482023-03-04T17:16:16Z Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure Li, Yaowei Zhang, Yao Zhao, Lina Zhang, Yang Liu, Chengyu Zhang, Li Zhang, Liuxin Li, Zhensheng Wang, Binhua Ng, Eyk Li, Jianqing He, Zhiqiang School of Mechanical and Aerospace Engineering Congestive Heart Failure Convolutional Neural Network DRNTU::Engineering::Electrical and electronic engineering 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. Published version 2018-08-23T04:39:22Z 2019-12-06T16:57:06Z 2018-08-23T04:39:22Z 2019-12-06T16:57:06Z 2018 Journal Article Li, Y., Zhang, Y., Zhao, L., Zhang, Y., Liu, C., Zhang, L., . . . He, Z. (2018). Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure. IEEE Access, 6, 39734-39744. doi:10.1109/ACCESS.2018.2855420 https://hdl.handle.net/10356/88148 http://hdl.handle.net/10220/45646 10.1109/ACCESS.2018.2855420 en IEEE Access © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See nhttp://www.ieee.org/publications_standards/publications/rights/index.html for more information. 11 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Congestive Heart Failure
Convolutional Neural Network
DRNTU::Engineering::Electrical and electronic engineering
spellingShingle Congestive Heart Failure
Convolutional Neural Network
DRNTU::Engineering::Electrical and electronic engineering
Li, Yaowei
Zhang, Yao
Zhao, Lina
Zhang, Yang
Liu, Chengyu
Zhang, Li
Zhang, Liuxin
Li, Zhensheng
Wang, Binhua
Ng, Eyk
Li, Jianqing
He, Zhiqiang
Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
description 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.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Li, Yaowei
Zhang, Yao
Zhao, Lina
Zhang, Yang
Liu, Chengyu
Zhang, Li
Zhang, Liuxin
Li, Zhensheng
Wang, Binhua
Ng, Eyk
Li, Jianqing
He, Zhiqiang
format Article
author Li, Yaowei
Zhang, Yao
Zhao, Lina
Zhang, Yang
Liu, Chengyu
Zhang, Li
Zhang, Liuxin
Li, Zhensheng
Wang, Binhua
Ng, Eyk
Li, Jianqing
He, Zhiqiang
author_sort Li, Yaowei
title Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
title_short Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
title_full Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
title_fullStr Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
title_full_unstemmed Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
title_sort combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
publishDate 2018
url https://hdl.handle.net/10356/88148
http://hdl.handle.net/10220/45646
_version_ 1759855972398923776