Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25207
Title: Pattern classification for Doppler ultrasonic wrist pulse signals
Authors: Chen, Y
Zhang, L 
Zhang, D 
Zhang, D 
Keywords: Auto regressive model
SVM
Traditional Chinese pulse diagnosis
Wavelet transform
Issue Date: 2009
Source: 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, 2009, 5163172 How to cite?
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.
Description: 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, Beijing, 11-13 June 2009
URI: http://hdl.handle.net/10397/25207
ISBN: 9781424429028
DOI: 10.1109/ICBBE.2009.5163172
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