关键词: 心音分类, 功率谱密度, 卷积神经网络, 双线性插值法, 心动周期检测

Abstract:

The classification of normal and abnormal heart sounds plays an important role in the screening of cardiovascular diseases. On the basis of no heart sound segmentation, this paper proposes a heart sound classification method based on power spectral density time-frequency distribution features and convolutional neural network. The proposed method uses wavelet denoising as preprocessing, and uses cyclic autocorrelation to obtain the cardiac cycle. Bilinear interpolation is used to extract the time-frequency features of the power spectrum density of the cardiac cycle with the same dimensions, and then the features are sent to the convolutional neural network for training and testing. The experiment uses the Challenge 2016 data set for training and testing. The classification accuracy of the test set reaches 0.8472, and the sensitivity and specificity scores reach 0.7763 and 0.9463. The comparison with other algorithms shows that the proposed algorithm achieves a higher overall score.

Key words: heart sound classification, power spectral density, convolutional neural network, bilinear interpolation, cardiac cycle detection