Heart Sound Signal Classification Algorithm: A Combination of Wavelet Scattering Transform and Twin Support Vector Machine

Abstract

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 timefrequency 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). INDEX TERMS Wavelet scattering transform, multidimensional scaling (MDS), twin support vector machine (TWSVM), signal classification.

A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

“…Other approaches achieved an acceptable accuracy using clustering techniques for cardiac sound classification such as the k-nearest neighbors (kNN) algorithm [17], threshold-based methods, and decision trees [18]. Support vector machine (SVM) had been also proved its capability having different kernel functions for HS classification [19][20][21]. Some HS features were generated from phase components of Fourier spectrum [22 and 23].…”

A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions

“…Furthermore, in many approaches very short recordings are included in their database, for example~1 s by Renna et al [14], or in total 87 heart sounds by Chen et al [7]. Moreover, many researchers use a database like PhysioNet and do not declare their study population or recording length [6,9,39]. Other researchers have conducted their measurements under optimal conditions (apart from [39]) and no variation of the posture, auscultation point, physical stress and breathing was considered within their studies.…”

“…The feature-based methods have the best performance parameters [6,9]. However, since featurebased methods have a strong dependency on their training datasets and a high computational effort, they are not the favourable methods for a low complex wearable sensor platform to monitor daily activities in real-time.…”

“…Feature-based methods, as e.g., proposed by [6][7][8][9][10], extract features such as the Shannon entropy, discrete wavelet transform (DWT), continuous wavelet transform (CWT) or mel-frequency cepstral coefficients (MFCC) out of the PCG signal. With the use of classifiers (for example support vector machine (SVM), twin support vector machine (TWSVM) or deep neural networks (DNN)), it can be determined, if the features correspond to a heart sound [5].…”

Bearing Fault Diagnosis Based on Natural Adaptive Moment Estimation Algorithm and Improved Octave Convolution

“…The proposed method can distinguish damaged bearings from normal bearings. Li et al [14] proposed a feature extraction and classification method combining wavelet scattering transform and twin support vector machines by using scattering transform to perform time-domain analysis and SVM to classify training data.…”