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 |
Congenital Heart Disease (CHD), Heart Sound, S Transform, Wavelet Transform, BP Neural Net
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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.
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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Download@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}
}
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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