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

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 satisfactor...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2021-05, Vol.23 (6), p.642
Main Authors: Liu, Tongtong, Li, Peng, Liu, Yuanyuan, Zhang, Huan, Li, Yuanyang, Jiao, Yu, Liu, Changchun, Karmakar, Chandan, Liang, Xiaohong, Ren, Mengli, Wang, Xinpei
Format: Article
Language:English
Subjects:
Accuracy
Auscultation
Cardiovascular disease
Classification
Coronary artery disease
Coronary vessels
cross entropy
Data collection
Entropy
Feature extraction
Heart
heart sound
multi-channel
Noise
Physiology
Quality assessment
Sample size
Segmentation
Sound
Support vector machines
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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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e23060642