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Detection of coronary artery disease using multi-domain feature fusion of multi-channel heart sound signals

Liu, Tongtong, Li, Peng, Liu, Yuanyuan, Zhang, Huan, Li, Yuanyang, Jiao, Yu, Liu, Changchun, Karmakar, Chandan, Liang, Xiaohong, Ren, Mengli and Wang, Xinpei 2021, Detection of coronary artery disease using multi-domain feature fusion of multi-channel heart sound signals, Entropy, vol. 23, no. 6, pp. 1-18, doi: 10.3390/e23060642.

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Title Detection of coronary artery disease using multi-domain feature fusion of multi-channel heart sound signals
Author(s) Liu, Tongtong
Li, Peng
Liu, Yuanyuan
Zhang, Huan
Li, Yuanyang
Jiao, Yu
Liu, Changchun
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Liang, Xiaohong
Ren, Mengli
Wang, Xinpei
Journal name Entropy
Volume number 23
Issue number 6
Article ID 642
Start page 1
End page 18
Total pages 18
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2021-06
ISSN 1099-4300
1099-4300
Keyword(s) coronary artery disease
cross entropy
entropy
heart sound
multi-channel
Summary Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.
Language eng
DOI 10.3390/e23060642
Indigenous content off
Field of Research 01 Mathematical Sciences
02 Physical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152166

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.