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%.
Highlights
Heart sound carries a lot of information about the health of cardiovascular system
Aiming at the classification problem of heart sound signals, this paper proposes the algorithm based on wavelet fractal and twin support vector machine (TWSVM) to classify the heart sound signals which are selected from the PhysioNet/CinC Challenge 2016 heart sound database [32,33]
472the classification problem of heart sound signals, this paper proposes the algorithm based on wavelet fractal and twin support vector machine (TWSVM) to classify the heart sound signals which are selected from the PhysioNet/CinC Challenge 2016 heart sound database [32,33]
Summary
Heart sound carries a lot of information about the health of cardiovascular system. It is an important source of information for diagnosing heart diseases and evaluating heart function. In order to obtain more abundant feature information, the wavelet packet theory is used to solve its coefficient norm and energy entropy as feature vectors This method can represent the abundant feature of heart sound, and the dimension of the extracted feature vectors is not very large. In the constraints of this quadratic programming problem, only positive or negative class of samples appears [16] This characteristic coincides with the classification results of the heart sound signals. Twin Support Vector Machine (TWSVM) can effectively prevent the problem of samples imbalance This method is used to classify the heart sound signals, which achieves good classification results and saves a lot of running time.
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