Paper Info
Title
A fusion framework based on multi-domain features and deep learning features of phonocardiogram for coronary artery disease detection.
Abstract
Phonocardiogram (PCG) signals reflect the mechanical activity of the heart. Previous studies have reported that PCG signals contain heart murmurs caused by coronary artery disease (CAD). However, the murmurs caused by CAD are very weak and rarely heard by the human ear. In this paper, a novel feature fusion framework is proposed to provide a comprehensive basis for CAD diagnosis. A dataset containing PCG signals of 175 subjects was collected and used. A total of 110 features were extracted from multiple domains, and then reduced and selected. Images obtained by Mel-frequency cepstral coefficients were used as the input for the convolutional neural network for feature learning. Then, the selected features and the deep learning features were fused and fed into a multilayer perceptron for classification. The proposed feature fusion method achieved better classification performance than multi-domain features or deep learning features alone, with accuracy, sensitivity, and specificity of 90.43%, 93.67%, and 83.36%, respectively. A comparison with existing studies demonstrated that the proposed method was a promising noninvasive screening tool for CAD under general medical conditions.
Year
DOI
Venue
2020
10.1016/j.compbiomed.2020.103733
Computers in Biology and Medicine
Keywords
DocType
Volume
Feature fusion,Multi-domain features,Deep learning,Phonocardiogram,Coronary artery disease
Journal
120
ISSN
Citations 
PageRank 
0010-4825
2
0.38
References 
Authors
0
10
Name
Order
Citations
PageRank
Han Li131.74
Xinpei Wang2196.78
Changchun Liu3329.39
Zeng Qiang43410.73
Yansong Zheng520.38
Xi Chu620.38
Lianke Yao721.40
Jikuo Wang820.38
Yu Jiao920.72
Chandan Karmakar1014323.65