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  • Research Article
  • Open Access

A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals

EURASIP Journal on Advances in Signal Processing20072008:468390

https://doi.org/10.1155/2008/468390

  • Received: 21 May 2007
  • Accepted: 19 November 2007
  • Published:

Abstract

A methodology for the automated time-domain characteristic parameter extraction of human pulse signals is presented. Due to the subjectivity and fuzziness of pulse diagnosis, the quantitative methods are needed. Up to now, the characteristic parameters are mostly obtained by labeling manually and reading directly from the pulse signal, which is an obstacle to realize the automated pulse recognition. To extract the parameters of pulse signals automatically, the idea is to start with the detection of characteristic points of pulse signals based on wavelet transform, and then determine the number of pulse waves based on chain code to label the characteristics. The time-domain parameters, which are endowed with important physiological significance by specialists of traditional Chinese medicine (TCM), are computed based on the labeling result. The proposed methodology is testified by applying it to compute the parameters of five hundred pulse signal samples collected from clinic. The results are mostly in accord with the expertise, which indicate that the method we proposed is feasible and effective, and can extract the features of pulse signals accurately, which can be expected to facilitate the modernization of pulse diagnosis.

Keywords

  • Information Technology
  • Traditional Chinese Medicine
  • Quantum Information
  • Characteristic Point
  • Quantitative Method

Publisher note

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Authors’ Affiliations

(1)
Institute of VLSI Design, Zhejiang University, Hangzhou, China
(2)
College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, China

Copyright

© P.-Y. Zhang and H.-Y.Wang. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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