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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 947254, 10 pages
Research Article

Pulse Waveform Classification Using Support Vector Machine with Gaussian Time Warp Edit Distance Kernel

1Harbin Ice Flower Hospital, Harbin 150086, China
2School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Received 13 September 2013; Revised 16 December 2013; Accepted 23 December 2013; Published 9 February 2014

Academic Editor: Kutlu O. Ulgen

Copyright © 2014 Danbing Jia et al. 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.


Advances in signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis. However, because of the inevitable intraclass variations of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. Utilizing the new elastic metric, that is, time wrap edit distance (TWED), this paper proposes to address the problem under the support vector machines (SVM) framework by using the Gaussian TWED kernel function. The proposed method, SVM with GTWED kernel (GTWED-SVM), is evaluated on a dataset including 2470 pulse waveforms of five distinct patterns. The experimental results show that the proposed method achieves a lower average error rate than current pulse waveform classification methods.