Next Article in Journal
New Bivariate Pareto Type II Models
Previous Article in Journal
Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
Previous Article in Special Issue
The Solutions to the Uncertainty Problem of Urban Fractal Dimension Calculation
Article Menu

Export Article

Open AccessArticle

Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine

1,2, 1,* and 1
1
Institute of Biomedical and Electromagnetic Engineering, Shenyang University of Technology, Shenyang 110870, China
2
College of Telecommunication and Electronic Engineering, Qiqihar University, Qiqihar 161006, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(5), 472; https://doi.org/10.3390/e21050472
Received: 3 April 2019 / Revised: 28 April 2019 / Accepted: 30 April 2019 / Published: 6 May 2019
(This article belongs to the Collection Wavelets, Fractals and Information Theory)
  |  
PDF [1915 KB, uploaded 6 May 2019]
  |  

Abstract

Heart is an important organ of human beings. As more and more heart diseases are caused by people’s living pressure or habits, the diagnosis and treatment of heart diseases also require technical improvement. In order to assist the heart diseases diagnosis, the heart sound signal is used to carry a large amount of cardiac state information, so that the heart sound signal processing can achieve the purpose of heart diseases diagnosis and treatment. In order to quickly and accurately judge the heart sound signal, the classification method based on Wavelet Fractal and twin support vector machine (TWSVM) is proposed in this paper. Firstly, the original heart sound signal is decomposed by wavelet transform, and the wavelet decomposition coefficients of the signal are extracted. Then the two-norm eigenvectors of the heart sound signal are obtained by solving the two-norm values of the decomposition coefficients. In order to express the feature information more abundantly, the energy entropy of the decomposed wavelet coefficients is calculated, and then the energy entropy characteristics of the signal are obtained. In addition, based on the fractal dimension, the complexity of the signal is quantitatively described. The box dimension of the heart sound signal is solved by the binary box dimension method. So its fractal dimension characteristics can be obtained. The above eigenvectors are synthesized as the eigenvectors of the heart sound signal. Finally, the twin support vector machine (TWSVM) is applied to classify the heart sound signals. The proposed algorithm is verified on the PhysioNet/CinC Challenge 2016 heart sound database. The experimental results show that this proposed algorithm based on twin support vector machine (TWSVM) is superior to the algorithm based on support vector machine (SVM) in classification accuracy and speed. The proposed algorithm achieves the best results with classification accuracy 90.4%, sensitivity 94.6%, specificity 85.5% and F1 Score 95.2%. View Full-Text
Keywords: heart sound; wavelet; energy entropy; fractal; twin support vector machine (TWSVM) heart sound; wavelet; energy entropy; fractal; twin support vector machine (TWSVM)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, J.; Ke, L.; Du, Q. Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine. Entropy 2019, 21, 472.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top