期刊
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
卷 58, 期 9, 页码 2039-2047出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11517-020-02218-5
关键词
Cardiac cycle; PCG; Patient; Modified frequency slice wavelet transform; Convolutional neural network
类别
资金
- Shandong Province Key Research and Development Plan [2018GSF118133]
- China Postdoctoral Science Foundation [2017M612280]
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.
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