Preliminary study on diurnal variation of pulse signals in TCM

Author

Wang Nanyue ; Yu Youhua ; Huang Dawei ; Liu Jia ; Zhou Lingyun ; Chen Yanping ; Xueli Yuan ; Li Tongda

Author_Institution

Exp. Res. Center, China Acad. of Chinese Med. Sci., Beijing, China

fYear

2014

fDate

2-5 Nov. 2014

Firstpage

170

Lastpage

172

Abstract

Objective: To compare the signals of pulse - diagnosis of healthy volunteer in different times in one day. Methods: After collecting the pulse waves of 42 healthy volunteers in 4 specific periods in one day, do pretreatment, parameter extracting basing on harmonic fitting, modeling, and identification by unsupervised learning and supervised learning with cross-validation step by step for analysis of the 4 groups. Finally, paired T-test and ANOVA were used in feature mining. Results: There are significant differences among the pulse-diagnosis signals of healthy volunteers in different times, and the accuracy rate is about 63%~ 84%. Pulse rate, F1zuocun and F2zuocun, etc. are key features in classification. Conclusion: Signals of pulse-diagnosis in TCM of healthy human have circadian rhythmicity.

Keywords

bioelectric potentials; circadian rhythms; feature extraction; medical signal processing; principal component analysis; unsupervised learning; ANOVA; TCM; circadian rhythmicity; cross-validation step; diurnal variation; feature classification; feature mining; harmonic fitting; paired T-test; pulse wave collection; pulse-diagnosis signals; time 1 day; unsupervised learning; Analysis of variance; Circadian rhythm; Harmonic analysis; Medical diagnostic imaging; Principal component analysis; Supervised learning; Unsupervised learning; diurnal variation; machine learning; pulse-diagnosis in TCM;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on

Conference_Location

Belfast

Type

conf

DOI

10.1109/BIBM.2014.6999351

Filename

6999351