Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88148
Title: Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
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
Keywords: Congestive Heart Failure
Convolutional Neural Network
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: 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
Series/Report no.: IEEE Access
Abstract: 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.
URI: https://hdl.handle.net/10356/88148
http://hdl.handle.net/10220/45646
DOI: 10.1109/ACCESS.2018.2855420
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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