Research on heart sound signals classification using hybrid CNN-SVM
摘要
Aiming at the problems of poor detection accuracy of current heart sound recognition algorithms, a new heart sound recognition method based on hybrid convolutional neural network-support vector machine model (CNN-SVM) is proposed. In order to verify the effectiveness of the method，two kinds of normal and abnormal heart sound signal databases based on PASCAL challenge experiment data are sorted out. Through preprocessing, MFCC feature extraction and PCA dimension reduction, CNN-SVM model has been input for training. The performance of the proposed method is evaluated in terms of accuracy, recall, specificity and F score.In order to verify the effectiveness of this algorithm, the hybrid CNN-SVM model is compared with the single SVM model and the CNN model respectively.Five groups of the experimental results show that the proposed method can distinguish the two different heart sound with a high average recognition rate of 99%, which is 2.48% higher than the single CNN method. It is also higher than the single SVM algorithm.