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Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine
Jinghui Li1,2, Li Ke1, Qiang Du1
1Institute of Biomedical and Electromagnetic Engineering, Shenyang University of Technology, Shenyang 110870, China.
Insights
This study introduces a novel Wavelet Fractal and twin support vector machine (TWSVM) method for accurate heart disease diagnosis using heart sound signals. The approach significantly improves classification accuracy and speed compared to traditional methods.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Machine Learning
Background:
- Heart diseases necessitate improved diagnostic techniques due to rising prevalence linked to lifestyle factors.
- Heart sound signals contain vital information for assessing cardiac health.
- Current diagnostic methods require enhancement for speed and accuracy.
Purpose of the Study:
- To develop an advanced method for heart sound signal classification to aid in heart disease diagnosis.
- To improve the accuracy and efficiency of heart disease detection using signal processing and machine learning.
Main Methods:
- Heart sound signals were decomposed using wavelet transform to extract coefficients.
- Feature extraction included calculating two-norm eigenvectors and energy entropy from wavelet coefficients.
- Fractal dimension, specifically box dimension, was computed to quantify signal complexity.
- A twin support vector machine (TWSVM) was employed for signal classification.
Main Results:
- The proposed Wavelet Fractal and TWSVM algorithm demonstrated superior performance over standard support vector machine (SVM) methods.
- Achieved high classification accuracy of 90.4%, sensitivity of 94.6%, specificity of 85.5%, and F1 Score of 95.2%.
- The method proved effective on the PhysioNet/CinC Challenge 2016 heart sound database.
Conclusions:
- The integrated Wavelet Fractal and TWSVM approach offers a robust and efficient solution for heart sound signal analysis.
- This method significantly enhances the accuracy and speed of heart disease diagnosis.
- The findings support the clinical utility of advanced signal processing and machine learning in cardiology.
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%.
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