标题:Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network
作者:Chen, Yongchao ;Wei, Shoushui ;Zhang, Yatao
作者机构:[Chen, Yongchao ;Wei, Shoushui ;Zhang, Yatao ] School of Control Science and Engineering, Shandong University, Jinan, China;[Zhang, Yatao ] School of 更多
通讯作者:Wei, Shoushui;Wei, Shoushui
来源:Medical and Biological Engineering and Computing
出版年:2020
卷:58
期:9
页码:2039-2047
DOI:10.1007/s11517-020-02218-5
关键词:Cardiac cycle; Convolutional neural network; Modified frequency slice wavelet transform; Patient; PCG
摘要:We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exact position of the four periods of S1, S2, systole, and diastole. Then the one-dimensional cardiac cycle signal was converted into a two-dimensional time-frequency picture using the MFSWT. Finally, two CNN models are trained using the aforementioned pictures. We combine two CNN models using sample entropy (SampEn) to determine which model is used to classify the heart sound signal. We evaluated our model on the heart sound public dataset provided by the PhysioNet Computing in Cardiology Challenge 2016. Experimental classification performance from a 10-fold cross-validation indicated that sensitivity (Se), specificity (Sp) and mean accuracy (MAcc) were 0.95, 0.93, and 0.94, respectively. The results showed the proposed method can classify normal and abnormal heart sounds with efficiency and high accuracy. [Figure not available: see fulltext.]. © 2020, International Federation for Medical and Biological Engineering.
收录类别:EI;SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087619280&doi=10.1007%2fs11517-020-02218-5&partnerID=40&md5=25887f964884b57fa9a52fa72c83fa90
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