Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine
This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extrac...
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Series: | Evidence-Based Complementary and Alternative Medicine |
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doaj-f51fb4aaa81d40969348cc85b0facd212020-11-24T22:57:41ZengHindawi LimitedEvidence-Based Complementary and Alternative Medicine1741-427X1741-42882014-01-01201410.1155/2014/502348502348Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector MachineJian-Jun Yan0Rui Guo1Yi-Qin Wang2Guo-Ping Liu3Hai-Xia Yan4Chun-Ming Xia5Xiaojing Shen6Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, ChinaLaboratory of Information Access and Synthesis of TCM Four Diagnostic, Shanghai University of Chinese Traditional Medicine, Shanghai 201203, ChinaLaboratory of Information Access and Synthesis of TCM Four Diagnostic, Shanghai University of Chinese Traditional Medicine, Shanghai 201203, ChinaLaboratory of Information Access and Synthesis of TCM Four Diagnostic, Shanghai University of Chinese Traditional Medicine, Shanghai 201203, ChinaLaboratory of Information Access and Synthesis of TCM Four Diagnostic, Shanghai University of Chinese Traditional Medicine, Shanghai 201203, ChinaCenter for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, ChinaCenter for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, ChinaThis study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.http://dx.doi.org/10.1155/2014/502348 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jian-Jun Yan Rui Guo Yi-Qin Wang Guo-Ping Liu Hai-Xia Yan Chun-Ming Xia Xiaojing Shen |
spellingShingle |
Jian-Jun Yan Rui Guo Yi-Qin Wang Guo-Ping Liu Hai-Xia Yan Chun-Ming Xia Xiaojing Shen Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine Evidence-Based Complementary and Alternative Medicine |
author_facet |
Jian-Jun Yan Rui Guo Yi-Qin Wang Guo-Ping Liu Hai-Xia Yan Chun-Ming Xia Xiaojing Shen |
author_sort |
Jian-Jun Yan |
title |
Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine |
title_short |
Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine |
title_full |
Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine |
title_fullStr |
Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine |
title_full_unstemmed |
Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine |
title_sort |
objective auscultation of tcm based on wavelet packet fractal dimension and support vector machine |
publisher |
Hindawi Limited |
series |
Evidence-Based Complementary and Alternative Medicine |
issn |
1741-427X 1741-4288 |
publishDate |
2014-01-01 |
description |
This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered. |
url |
http://dx.doi.org/10.1155/2014/502348 |
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