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Detection of coronary artery disease using multi-domain feature fusion of multi-channel heart sound signals

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Version 2 2024-06-19, 03:30
Version 1 2021-06-07, 08:26
journal contribution
posted on 2024-06-19, 03:30 authored by T Liu, P Li, Y Liu, H Zhang, Y Li, Y Jiao, C Liu, Chandan KarmakarChandan Karmakar, X Liang, M Ren, X Wang
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.

History

Journal

Entropy

Volume

23

Article number

ARTN 642

Pagination

1 - 18

Location

Switzerland

Open access

  • Yes

ISSN

1099-4300

eISSN

1099-4300

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

6

Publisher

MDPI

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