karmakar-detectionofcoronary-2021.pdf (1 MB)
Detection of coronary artery disease using multi-domain feature fusion of multi-channel heart sound signals
journal contribution
posted on 2021-06-01, 00:00 authored by T Liu, P Li, Y Liu, H Zhang, Y Li, Y Jiao, C Liu, Chandan KarmakarChandan Karmakar, X Liang, M Ren, X WangHeart 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.
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Journal
EntropyVolume
23Issue
6Article number
642Pagination
1 - 18Publisher
MDPI AGLocation
Basel, SwitzerlandPublisher DOI
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1099-4300eISSN
1099-4300Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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