Key result
Novel heart sound classification algorithm achieves ~98% accuracy, outperforming traditional SVM methods.
Why the study?
Heart sound signals provide valuable diagnostic information for cardiovascular diseases, but their complex characteristics make them difficult to classify and recognize.
Absolute Event Rate: 98.37% vs 86.17%
A novel algorithm combining wavelet scattering transform and twin support vector machine achieves high accuracy and efficiency in classifying heart sound signals.
May aid automated phonocardiogram screening; leaves open prospective clinical validation before practice change.
By classifying the heart sound signals, it can provide very favorable clinical information to the diagnosis of cardiovascular diseases. According to the characteristics of heart sound signals which are complex and difficult to classify and recognize, a new method of feature extraction and classification about heart sound signal is proposed by a combination of wavelet scattering transform and twin support vector machine in this paper. The method is as follows: The heart sound signal data set is firstly divided into two parts, one as a training set and the other as a testing set. Then the wavelet scattering transform is applied to the heart sound signals in the training set and the testing set. The scattering transform is a new time-frequency analysis method. It overcomes the shortcomings of the traditional wavelet transform which has the time-shift changes. It has the advantages of translation invariance and elastic deformation stability. Thus obtain the scattering feature matrix of the heart sound signal. Due to the large dimension of scattering feature matrix, this paper uses multidimensional scaling (MDS) method to reduce the dimension. This method is compared with the classical dimension reduction method-principal component analysis (PCA). Finally, the dimensionality-reduced feature matrix is input into the twin support vector machine (TWSVM) for training. After training the classifier to get the optimal parameters, the dimensionality-reduced scattering feature matrix of the testing signal is input into the classifier for testing. Experimental results show that the classification accuracy of the proposed method can reach 98% or more, and the running time is greatly reduced compared with support vector machine (SVM).
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Du et al. (2019) studied Cardiovascular diseases (Heart sound signal classification) (n=409). Wavelet scattering transform combined with multidimensional scaling (MDS) and twin support vector machine (TWSVM) vs. Principal component analysis (PCA) and support vector machine (SVM) was evaluated on Classification accuracy. A novel heart sound classification algorithm combining wavelet scattering transform, multidimensional scaling, and twin support vector machine achieved a classification accuracy of 98.37%, outperforming traditional SVM methods.
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