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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

The SampEn values for the coefficients of WPT: (a)–(e) SampEn values for the first to the fifth level coefficients.
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Related In: Results  -  Collection


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fig6: The SampEn values for the coefficients of WPT: (a)–(e) SampEn values for the first to the fifth level coefficients.

Mentions: The average SampEn values for the coefficients of the 1–5 levels are illustrated in Figures 6(a)–6(e). The differences between healthy and qi- or yin-deficient samples are relatively high, except in 0–0.5 kHz and 7.5–8 kHz of the forth level and 0.25–0.0.5 kHz, 7.5–7.75 kHz and 7.75–8 kHz of fifth level. However, the differences between the qi- and yin-deficient samples are relatively low apart from the following frequency ranges: 0 kHz to 8 kHz in the 1–5 levels.


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)

The SampEn values for the coefficients of WPT: (a)–(e) SampEn values for the first to the fifth level coefficients.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: The SampEn values for the coefficients of WPT: (a)–(e) SampEn values for the first to the fifth level coefficients.
Mentions: The average SampEn values for the coefficients of the 1–5 levels are illustrated in Figures 6(a)–6(e). The differences between healthy and qi- or yin-deficient samples are relatively high, except in 0–0.5 kHz and 7.5–8 kHz of the forth level and 0.25–0.0.5 kHz, 7.5–7.75 kHz and 7.75–8 kHz of fifth level. However, the differences between the qi- and yin-deficient samples are relatively low apart from the following frequency ranges: 0 kHz to 8 kHz in the 1–5 levels.

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