Convolutional neural network combined with wavelet denoising for multi-category analysis on heart sound

Chun-dong Xu; Hai Lin

doi:10.1117/12.2599492

30 June 2021 Convolutional neural network combined with wavelet denoising for multi-category analysis on heart sound

Chun-dong Xu, Hai Lin

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780Z (2021) https://doi.org/10.1117/12.2599492
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Due to traditional classification methods can’t meet the requirements of a great variety of cardiovascular diseases in clinical environment, CNN recognition method based on wavelet denoising is proposed to apply in automatic analysis of multi-category heart sounds. Firstly, original heart sound signal is denoising by wavelet threshold and the Mel-Frequency Spectrum Coefficients are extracted as the features of heart sound. Then models of CNN and Long Short-Term Memory (LSTM) networks are constructed and the related parameters are adjusted. Finally, the trained models are used to classify the heart sound signals. The experimental results show all performance indicators including sensitivity of proposed algorithm outperform LSTM and the traditional methods, achieves a better recognition results at the same time.

Citation Download Citation

Chun-dong Xu and Hai Lin "Convolutional neural network combined with wavelet denoising for multi-category analysis on heart sound", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780Z (30 June 2021); https://doi.org/10.1117/12.2599492

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