Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals
| العنوان: | Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals |
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| المؤلفون: | Yu Jiao, Changchun Liu, Huan Zhang, Yuanyang Li, Tongtong Liu, Yuanyuan Liu, Xiaohong Liang, Xinpei Wang, Peng Li, Chandan Karmakar, Mengli Ren |
| المصدر: | Entropy, Vol 23, Iss 642, p 642 (2021) Entropy Volume 23 Issue 6 |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, Science, QC1-999, 0206 medical engineering, General Physics and Astronomy, Feature selection, CAD, 02 engineering and technology, 030204 cardiovascular system & hematology, Astrophysics, Article, heart sound, Coronary artery disease, 03 medical and health sciences, 0302 clinical medicine, medicine, Entropy (energy dispersal), cross entropy, Audio signal, business.industry, Physics, Pattern recognition, multi-channel, medicine.disease, 020601 biomedical engineering, Data segment, Support vector machine, QB460-466, Cross entropy, Artificial intelligence, business, entropy, coronary artery disease |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1099-4300 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f51ff3895e442cb74fe1642e0a3d6958 https://www.mdpi.com/1099-4300/23/6/642 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....f51ff3895e442cb74fe1642e0a3d6958 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10994300 |
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