ArticleEvidence-based complementary and alternative medicine : eCAM2012
Study of a vocal feature selection method and vocal properties for discriminating four constitution types.
Article in Evidence-based complementary and alternative medicine : eCAM, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations.Journal of healthcare engineering · 2021Article
- Constitutional multicenter bank linked to Sasang constitutional phenotypic data.BMC complementary and alternative medicine · 2015Article
- Constitution-specific features of perspiration and skin visco-elasticity in SCM.BMC complementary and alternative medicine · 2014Article
- Recent progress in voice-based sasang constitutional medicine: improving stability of diagnosis.Evidence-based complementary and alternative medicine : eCAM · 2013Article
- A novel method for classifying body mass index on the basis of speech signals for future clinical applications: a pilot study.Evidence-based complementary and alternative medicine : eCAM · 2013Article
- The concept of sasang health index and constitution-based health assessment: an integrative model with computerized four diagnosis methods.Evidence-based complementary and alternative medicine : eCAM · 2013Article
- Predicting sasang constitution using body-shape information.Evidence-based complementary and alternative medicine : eCAM · 2012Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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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.
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Registered trials
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