Pattern classification for Doppler ultrasonic wrist pulse signals

Yinghui Chen, Lei Zhang, Dapeng Zhang, Dongyu Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Wrist pulse signal contains important information about the health status of a person and it has been used in Traditional Chinese Medicine for a long time. In this work, digitalized wrist pulse signals from patients with different diseases as well as healthy persons are collected by a Doppler ultrasonic device. Two methods, namely, the wavelet method and the auto regressive prediction error (ARPE) method, are proposed to analyze the pulse signals and distinguish patients from healthy persons. Distinctive features are first extracted from the pulse signals and then the support vector machine (SVM) is used for classification. The applicability of the methods is investigated using wrist pulse signals collected from 50 healthy persons and 74 patients. The results illustrate a great promise of the proposed methods for computerized pulse signal analysis.
Original languageEnglish
Title of host publication3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
DOIs
Publication statusPublished - 31 Dec 2009
Event3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 - Beijing, China
Duration: 11 Jun 200913 Jun 2009

Conference

Conference3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
Country/TerritoryChina
CityBeijing
Period11/06/0913/06/09

Keywords

  • Auto regressive model
  • SVM
  • Traditional Chinese pulse diagnosis
  • Wavelet transform

ASJC Scopus subject areas

  • Biotechnology
  • Biomedical Engineering

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