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 language | English |
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Title of host publication | 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 |
DOIs | |
Publication status | Published - 31 Dec 2009 |
Event | 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 - Beijing, China Duration: 11 Jun 2009 → 13 Jun 2009 |
Conference
Conference | 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 |
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Country/Territory | China |
City | Beijing |
Period | 11/06/09 → 13/06/09 |
Keywords
- Auto regressive model
- SVM
- Traditional Chinese pulse diagnosis
- Wavelet transform
ASJC Scopus subject areas
- Biotechnology
- Biomedical Engineering