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Nonlinear Analysis of Auscultation Signals in TCM Using the Combination of Wavelet Packet Transform and Sample Entropy.

Yan JJ, Wang YQ, Guo R, Zhou JZ, Yan HX, Xia CM, Shen Y - Evid Based Complement Alternat Med (2012)

Bottom Line: SampEns for WPT coefficients were computed to quantify the signals from qi- and yin-deficient, as well as healthy, subjects.Then, SampEn values for approximated and detailed coefficients were calculated.The recognition accuracy rates were higher than 90%.

View Article: PubMed Central - PubMed

Affiliation: Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China.

ABSTRACT
Auscultation signals are nonstationary in nature. Wavelet packet transform (WPT) has currently become a very useful tool in analyzing nonstationary signals. Sample entropy (SampEn) has recently been proposed to act as a measurement for quantifying regularity and complexity of time series data. WPT and SampEn were combined in this paper to analyze auscultation signals in traditional Chinese medicine (TCM). SampEns for WPT coefficients were computed to quantify the signals from qi- and yin-deficient, as well as healthy, subjects. The complexity of the signal can be evaluated with this scheme in different time-frequency resolutions. First, the voice signals were decomposed into approximated and detailed WPT coefficients. Then, SampEn values for approximated and detailed coefficients were calculated. Finally, SampEn values with significant differences in the three kinds of samples were chosen as the feature parameters for the support vector machine to identify the three types of auscultation signals. The recognition accuracy rates were higher than 90%.

No MeSH data available.


Related in: MedlinePlus

Wavelet packet decomposition tree.
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Related In: Results  -  Collection


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fig2: Wavelet packet decomposition tree.

Mentions: Given a finite energy signal whose scaling space is assumed as S00, WPT can decompose S00 into small subspaces Sjn in a dichotomous way (Figure 2).


Nonlinear Analysis of Auscultation Signals in TCM Using the Combination of Wavelet Packet Transform and Sample Entropy.

Yan JJ, Wang YQ, Guo R, Zhou JZ, Yan HX, Xia CM, Shen Y - Evid Based Complement Alternat Med (2012)

Wavelet packet decomposition tree.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3369240&req=5

fig2: Wavelet packet decomposition tree.
Mentions: Given a finite energy signal whose scaling space is assumed as S00, WPT can decompose S00 into small subspaces Sjn in a dichotomous way (Figure 2).

Bottom Line: SampEns for WPT coefficients were computed to quantify the signals from qi- and yin-deficient, as well as healthy, subjects.Then, SampEn values for approximated and detailed coefficients were calculated.The recognition accuracy rates were higher than 90%.

View Article: PubMed Central - PubMed

Affiliation: Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China.

ABSTRACT
Auscultation signals are nonstationary in nature. Wavelet packet transform (WPT) has currently become a very useful tool in analyzing nonstationary signals. Sample entropy (SampEn) has recently been proposed to act as a measurement for quantifying regularity and complexity of time series data. WPT and SampEn were combined in this paper to analyze auscultation signals in traditional Chinese medicine (TCM). SampEns for WPT coefficients were computed to quantify the signals from qi- and yin-deficient, as well as healthy, subjects. The complexity of the signal can be evaluated with this scheme in different time-frequency resolutions. First, the voice signals were decomposed into approximated and detailed WPT coefficients. Then, SampEn values for approximated and detailed coefficients were calculated. Finally, SampEn values with significant differences in the three kinds of samples were chosen as the feature parameters for the support vector machine to identify the three types of auscultation signals. The recognition accuracy rates were higher than 90%.

No MeSH data available.


Related in: MedlinePlus