Abstract
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.
Highlights
Coronary artery disease (CAD) has been the leading cause of death in cardiovascular disease globally [1] and is still increasing at an alarming rate
In the comparison of the features of different domains, cross entropy features accounted for the largest proportion of the features with statistical differences, the number of them was the least
After adding entropy features and cross entropy features, the classification accuracy improved to 90.90%
Summary
Coronary artery disease (CAD) has been the leading cause of death in cardiovascular disease globally [1] and is still increasing at an alarming rate. There is an urgency to develop convenient and accurate options for CAD detection of large-scale populations. Coronary angiography (CAG) [2] is widely regarded as the gold standard for detecting. It is not suitable as a routine examination method for early screening due to its invasive and high price defects. The turbulence can cause murmurs in heart sound signals [3]. As a non-invasive detection method, heart sound analysis has the potential to become a cost-effective screening tool to achieve the early detection of CAD [4]
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