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dc.contributor.authorLi, Yaoweien
dc.contributor.authorZhang, Yaoen
dc.contributor.authorZhao, Linaen
dc.contributor.authorZhang, Yangen
dc.contributor.authorLiu, Chengyuen
dc.contributor.authorZhang, Lien
dc.contributor.authorZhang, Liuxinen
dc.contributor.authorLi, Zhenshengen
dc.contributor.authorWang, Binhuaen
dc.contributor.authorNg, Eyken
dc.contributor.authorLi, Jianqingen
dc.contributor.authorHe, Zhiqiangen
dc.identifier.citationLi, 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.2018.2855420en
dc.description.abstractCongestive 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 ( ). 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.en
dc.format.extent11 p.en
dc.relation.ispartofseriesIEEE Accessen
dc.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 n for more information.en
dc.subjectCongestive Heart Failureen
dc.subjectConvolutional Neural Networken
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleCombining convolutional neural network and distance distribution matrix for identification of congestive heart failureen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.description.versionPublished versionen
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