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Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network

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Abstract

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.

Block diagram of heart sound classification.

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Funding

This work was supported by Shandong Province Key Research and Development Plan (2018GSF118133); China Postdoctoral Science Foundation (2018M642144); and the China Postdoctoral Science Foundation under Grant (2017M612280).

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, China

    Yongchao Chen, Shoushui Wei & Yatao Zhang

  2. School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, China

    Yatao Zhang

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  1. Yongchao Chen
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  2. Shoushui Wei
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Correspondence to Shoushui Wei or Yatao Zhang.

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Chen, Y., Wei, S. & Zhang, Y. Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network. Med Biol Eng Comput 58, 2039–2047 (2020). https://doi.org/10.1007/s11517-020-02218-5

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  • DOI: https://doi.org/10.1007/s11517-020-02218-5

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