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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

Influence of m on the separability among three classes using SampEn. The maximum separability is achieved with m = 2.
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Related In: Results  -  Collection


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fig4: Influence of m on the separability among three classes using SampEn. The maximum separability is achieved with m = 2.

Mentions: In the second stage, SampEn values of approximation and detailed coefficients at each level of the wavelet decomposition were computed for the voice signals of the healthy subjects, as well as yin- and qi-deficient patients. In choosing the optimum parameters m and r, Pincus suggested m = 2 and r = 0.1  δ to 0.25 δ, where δ is the standard deviation of the original signal u(i), i = 1,…, N. One of the original signals was chosen and analysed using different m and r values to better illustrate the advantages of the choice. The results are shown in Figures 4 and 5. We can easily see that the difference in the SampEn values was the largest among the signals of the three kinds of samples (shown in Figure 5). This condition indicates that the choice of the value m = 2 is appropriate. We can also see that the SampEn value decreased as the parameter increased, although in a lower degree. Therefore, r is selected as 0.2 δ appropriately.


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)

Influence of m on the separability among three classes using SampEn. The maximum separability is achieved with m = 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Influence of m on the separability among three classes using SampEn. The maximum separability is achieved with m = 2.
Mentions: In the second stage, SampEn values of approximation and detailed coefficients at each level of the wavelet decomposition were computed for the voice signals of the healthy subjects, as well as yin- and qi-deficient patients. In choosing the optimum parameters m and r, Pincus suggested m = 2 and r = 0.1  δ to 0.25 δ, where δ is the standard deviation of the original signal u(i), i = 1,…, N. One of the original signals was chosen and analysed using different m and r values to better illustrate the advantages of the choice. The results are shown in Figures 4 and 5. We can easily see that the difference in the SampEn values was the largest among the signals of the three kinds of samples (shown in Figure 5). This condition indicates that the choice of the value m = 2 is appropriate. We can also see that the SampEn value decreased as the parameter increased, although in a lower degree. Therefore, r is selected as 0.2 δ appropriately.

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