国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Heart sound classification plays a crucial role in the early detection of cardiovascular disease, especially in small primary health care clinics and in homes without a professional presence. In order to improve the discriminability between categories of heart sound signal data and further improve the classification accuracy, a classification method based on multi-preprocessing method (LMFP) and convolutional neural network (CNN) model was proposed. First of all, the acquisition frequency of the original data is 44100Hz, and the amount of data processed is relatively large, so it is necessary to downsample the data to reduce the unnecessary amount of data.? Second, bandpass filter, SG filter and MFCC were used for preprocessing to extract the features of heart sound data, and the one-dimensional data was converted into two-dimensional data or spectrogram, and the data PCA transformation matrix was calculated. Third, multiply the PCA transformation matrix corresponding to the preprocessed two-dimensional data, which is the main part of LMFP. It can reduce unnecessary dimensions and make the data more representative. Finally, the processed data is input into the model CNN of this paper. In order to verify the validity and reliability of LMFP+CNN algorithm, experiments were carried out with partial data set of PASCAL challenge data. Compared with other methods and convolutional neural networks with different layers, the superiority of this method is proved. The experimental results show that the method proposed in this paper can effectively achieve 97.21% accuracy.