Classification of heart sound signals in congenital heart disease based on convolutional neural network.
10.7507/1001-5515.201806031
- Author:
Zhaowen TAN
1
;
Weilian WANG
2
;
Rong ZONG
1
;
Jiahua PAN
3
;
Hongbo YANG
3
Author Information
1. School of Information Science and Engineering, Yunnan University, Kunming 650504, P.R.China.
2. School of Information Science and Engineering, Yunnan University, Kunming 650504, P.R.China.wlwang_47@126.com.
3. Yunnan Fuwai Cardiovascular Disease Hospital, Kunming 650102, P.R.China.
- Publication Type:Journal Article
- Keywords:
Mel coefficient;
classification;
congenital heart disease;
convolutional neural network;
machine aided auscultation
- MeSH:
Algorithms;
Heart Defects, Congenital;
diagnosis;
Heart Sounds;
Humans;
Neural Networks (Computer);
Sensitivity and Specificity
- From:
Journal of Biomedical Engineering
2019;36(5):728-736
- CountryChina
- Language:Chinese
-
Abstract:
Cardiac auscultation is the basic way for primary diagnosis and screening of congenital heart disease(CHD). A new classification algorithm of CHD based on convolution neural network was proposed for analysis and classification of CHD heart sounds in this work. The algorithm was based on the clinically collected diagnosed CHD heart sound signal. Firstly the heart sound signal preprocessing algorithm was used to extract and organize the Mel Cepstral Coefficient (MFSC) of the heart sound signal in the one-dimensional time domain and turn it into a two-dimensional feature sample. Secondly, 1 000 feature samples were used to train and optimize the convolutional neural network, and the training results with the accuracy of 0.896 and the loss value of 0.25 were obtained by using the Adam optimizer. Finally, 200 samples were tested with convolution neural network, and the results showed that the accuracy was up to 0.895, the sensitivity was 0.910, and the specificity was 0.880. Compared with other algorithms, the proposed algorithm has improved accuracy and specificity. It proves that the proposed method effectively improves the robustness and accuracy of heart sound classification and is expected to be applied to machine-assisted auscultation.
