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Article

Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals

1
School of Control Science and Engineering, Shandong University, Jinan 250061, China
2
Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA 02115, USA
3
Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
4
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
5
Department of Medical Engineering, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
6
School of Information Technology, Deakin University, Geelong, VIC 3225, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Luca Faes
Entropy 2021, 23(6), 642; https://doi.org/10.3390/e23060642
Received: 29 April 2021 / Revised: 14 May 2021 / Accepted: 15 May 2021 / Published: 21 May 2021
(This article belongs to the Special Issue Entropy in Data Analysis)
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. View Full-Text
Keywords: heart sound; coronary artery disease; multi-channel; entropy; cross entropy heart sound; coronary artery disease; multi-channel; entropy; cross entropy
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MDPI and ACS Style

Liu, T.; Li, P.; Liu, Y.; Zhang, H.; Li, Y.; Jiao, Y.; Liu, C.; Karmakar, C.; Liang, X.; Ren, M.; Wang, X. Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals. Entropy 2021, 23, 642. https://doi.org/10.3390/e23060642

AMA Style

Liu T, Li P, Liu Y, Zhang H, Li Y, Jiao Y, Liu C, Karmakar C, Liang X, Ren M, Wang X. Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals. Entropy. 2021; 23(6):642. https://doi.org/10.3390/e23060642

Chicago/Turabian Style

Liu, Tongtong, Peng Li, Yuanyuan Liu, Huan Zhang, Yuanyang Li, Yu Jiao, Changchun Liu, Chandan Karmakar, Xiaohong Liang, Mengli Ren, and Xinpei Wang. 2021. "Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals" Entropy 23, no. 6: 642. https://doi.org/10.3390/e23060642

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