Research on heart sound recognition based on support vector machine

The present status of heart sound recognition is introduced in the paper. In order to improve the performance of heart sound recognition, a new model based on SVM is proposed. Firstly, the wavelet transform is used to reduce the noise of the heart sound, and then MFCC feature is extracted from heart sound. On this basis, the Support Vector Machine is used to build the classification model. In the experiment, the best performance of the classifier is given by further optimizing the kernel function and penalty factor. The experimental result shows that the model's average recognition rate is more than 93%, and the result of recognition is superior to neural network model.