Methodology Article | | Peer-Reviewed

Research on Feature Extraction and Recognition of CHD Heart Sound Signal Based on S Transform

Received: 28 October 2018     Accepted: 30 November 2018     Published: 20 December 2018
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Abstract

Auscultation is the main means in the early diagnosis of congenital heart disease. The research on analysis and classification of CHD heart sound has important significant and can be used in clinical diagnosis of CHD. It will be helpful for machine auxiliary diagnosis. In this work, a feature extraction and recognition algorithm based on S transform was put forward, including the heart sound signal preprocessing, feature extraction and classification recognition. In heart sound preprocessing, denoising, envelope extracting, and segmenting were done to obtain the each cycle of the heart sound. Some of time-frequency analysis methods such as STFT, Wigner-Ville, wavelet transform, and S transform were discussed and analyzed. Then S transform and wavelet transform were used for feature extraction of each cycle. Finally, the BP neural network was used as classifier to recognize the normal and the abnormal heart sound signal. All cases of CHD heart sound used in this experiment came from heart sound data base sampled in clinic at Yunnan Fuwai Cardiovascular Disease Hospital. 361cases heart sounds including CHD and healthy heart sound were selected randomly for analysis. The result showed that recognition rates of S transform method and wavelet transform method were 80.4% and 76% respectively. S transform has a better recognition than wavelet transform.

Published in Asia-Pacific Journal of Computer Science and Technology (Volume 1, Issue 1)
Page(s) 1-7
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

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Keywords

Congenital Heart Disease (CHD), Heart Sound, S Transform, Wavelet Transform, BP Neural Net

References
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Cite This Article
  • APA Style

    Zeng Zheng, Pan Jiahua, Cai Guanghui, Yang Hongbo, Wang Weilian. (2018). Research on Feature Extraction and Recognition of CHD Heart Sound Signal Based on S Transform. Asia-Pacific Journal of Computer Science and Technology, 1(1), 1-7.

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    ACS Style

    Zeng Zheng; Pan Jiahua; Cai Guanghui; Yang Hongbo; Wang Weilian. Research on Feature Extraction and Recognition of CHD Heart Sound Signal Based on S Transform. Asia-Pac. J. Comput. Sci. Technol. 2018, 1(1), 1-7.

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    AMA Style

    Zeng Zheng, Pan Jiahua, Cai Guanghui, Yang Hongbo, Wang Weilian. Research on Feature Extraction and Recognition of CHD Heart Sound Signal Based on S Transform. Asia-Pac J Comput Sci Technol. 2018;1(1):1-7.

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  • @article{10034636,
      author = {Zeng Zheng and Pan Jiahua and Cai Guanghui and Yang Hongbo and Wang Weilian},
      title = {Research on Feature Extraction and Recognition of CHD Heart Sound Signal Based on S Transform},
      journal = {Asia-Pacific Journal of Computer Science and Technology},
      volume = {1},
      number = {1},
      pages = {1-7},
      url = {https://www.sciencepublishinggroup.com/article/10034636},
      abstract = {Auscultation is the main means in the early diagnosis of congenital heart disease. The research on analysis and classification of CHD heart sound has important significant and can be used in clinical diagnosis of CHD. It will be helpful for machine auxiliary diagnosis. In this work, a feature extraction and recognition algorithm based on S transform was put forward, including the heart sound signal preprocessing, feature extraction and classification recognition. In heart sound preprocessing, denoising, envelope extracting, and segmenting were done to obtain the each cycle of the heart sound. Some of time-frequency analysis methods such as STFT, Wigner-Ville, wavelet transform, and S transform were discussed and analyzed. Then S transform and wavelet transform were used for feature extraction of each cycle. Finally, the BP neural network was used as classifier to recognize the normal and the abnormal heart sound signal. All cases of CHD heart sound used in this experiment came from heart sound data base sampled in clinic at Yunnan Fuwai Cardiovascular Disease Hospital. 361cases heart sounds including CHD and healthy heart sound were selected randomly for analysis. The result showed that recognition rates of S transform method and wavelet transform method were 80.4% and 76% respectively. S transform has a better recognition than wavelet transform.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Research on Feature Extraction and Recognition of CHD Heart Sound Signal Based on S Transform
    AU  - Zeng Zheng
    AU  - Pan Jiahua
    AU  - Cai Guanghui
    AU  - Yang Hongbo
    AU  - Wang Weilian
    Y1  - 2018/12/20
    PY  - 2018
    T2  - Asia-Pacific Journal of Computer Science and Technology
    JF  - Asia-Pacific Journal of Computer Science and Technology
    JO  - Asia-Pacific Journal of Computer Science and Technology
    SP  - 1
    EP  - 7
    PB  - Science Publishing Group
    UR  - http://www.sciencepg.com/article/10034636
    AB  - Auscultation is the main means in the early diagnosis of congenital heart disease. The research on analysis and classification of CHD heart sound has important significant and can be used in clinical diagnosis of CHD. It will be helpful for machine auxiliary diagnosis. In this work, a feature extraction and recognition algorithm based on S transform was put forward, including the heart sound signal preprocessing, feature extraction and classification recognition. In heart sound preprocessing, denoising, envelope extracting, and segmenting were done to obtain the each cycle of the heart sound. Some of time-frequency analysis methods such as STFT, Wigner-Ville, wavelet transform, and S transform were discussed and analyzed. Then S transform and wavelet transform were used for feature extraction of each cycle. Finally, the BP neural network was used as classifier to recognize the normal and the abnormal heart sound signal. All cases of CHD heart sound used in this experiment came from heart sound data base sampled in clinic at Yunnan Fuwai Cardiovascular Disease Hospital. 361cases heart sounds including CHD and healthy heart sound were selected randomly for analysis. The result showed that recognition rates of S transform method and wavelet transform method were 80.4% and 76% respectively. S transform has a better recognition than wavelet transform.
    VL  - 1
    IS  - 1
    ER  - 

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Author Information
  • School of Information, Yunnan University, Kunming, China

  • Yunnan Fuwai Cardiovascular Disease Hospital, Kunming, China

  • School of Information, Yunnan University, Kunming, China

  • Yunnan Fuwai Cardiovascular Disease Hospital, Kunming, China

  • School of Information, Yunnan University, Kunming, China

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