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Study of a vocal feature selection method and vocal properties for discriminating four constitution types.

Kim KH, Ku B, Kang N, Kim YS, Jang JS, Kim JY - Evid Based Complement Alternat Med (2012)

Bottom Line: Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis.Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed.Finally, the vocal features are applied to a discriminant analysis to classify each constitution type.

View Article: PubMed Central - PubMed

Affiliation: Division of Constitutional Medicine Research, Korea Institute of Oriental Medicine, 461-24 Jeonmin-dong, Yuseong-gu, Daejeon 305-811, Republic of Korea.

ABSTRACT
The voice has been used to classify the four constitution types, and to recognize a subject's health condition by extracting meaningful physical quantities, in traditional Korean medicine. In this paper, we propose a method of selecting the reliable variables from various voice features, such as frequency derivative features, frequency band ratios, and intensity, from vowels and a sentence. Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis. Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed. Finally, the vocal features are applied to a discriminant analysis to classify each constitution type. This method of voice classification can be widely used in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy.

No MeSH data available.


Related in: MedlinePlus

Transformation of variable (feature) distribution.
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Related In: Results  -  Collection


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fig5: Transformation of variable (feature) distribution.

Mentions: However, as shown in the left-hand figures of Figure 5, it was known from precollected datasets that the distributions of a few features were not Gaussian but were instead skewed to the right, which we transformed by taking the log or square root. These transformed distributions are shown on the right of Figures 5(a) and 5(b), respectively, which are similar to a Gaussian model.


Study of a vocal feature selection method and vocal properties for discriminating four constitution types.

Kim KH, Ku B, Kang N, Kim YS, Jang JS, Kim JY - Evid Based Complement Alternat Med (2012)

Transformation of variable (feature) distribution.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3313581&req=5

fig5: Transformation of variable (feature) distribution.
Mentions: However, as shown in the left-hand figures of Figure 5, it was known from precollected datasets that the distributions of a few features were not Gaussian but were instead skewed to the right, which we transformed by taking the log or square root. These transformed distributions are shown on the right of Figures 5(a) and 5(b), respectively, which are similar to a Gaussian model.

Bottom Line: Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis.Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed.Finally, the vocal features are applied to a discriminant analysis to classify each constitution type.

View Article: PubMed Central - PubMed

Affiliation: Division of Constitutional Medicine Research, Korea Institute of Oriental Medicine, 461-24 Jeonmin-dong, Yuseong-gu, Daejeon 305-811, Republic of Korea.

ABSTRACT
The voice has been used to classify the four constitution types, and to recognize a subject's health condition by extracting meaningful physical quantities, in traditional Korean medicine. In this paper, we propose a method of selecting the reliable variables from various voice features, such as frequency derivative features, frequency band ratios, and intensity, from vowels and a sentence. Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis. Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed. Finally, the vocal features are applied to a discriminant analysis to classify each constitution type. This method of voice classification can be widely used in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy.

No MeSH data available.


Related in: MedlinePlus