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  • Research Article
  • Open Access

Classification of Pulse Waveforms Using Edit Distance with Real Penalty

  • 1,
  • 1Email author,
  • 1, 2,
  • 1 and
  • 1
EURASIP Journal on Advances in Signal Processing20102010:303140

https://doi.org/10.1155/2010/303140

  • Received: 13 March 2010
  • Accepted: 25 August 2010
  • Published:

Abstract

Advances in sensor and signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis (TCPD). Because of the inevitable intraclass variation of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. In this paper, by referring to the edit distance with real penalty (ERP) and the recent progress in -nearest neighbors (KNN) classifiers, we propose two novel ERP-based KNN classifiers. Taking advantage of the metric property of ERP, we first develop an ERP-induced inner product and a Gaussian ERP kernel, then embed them into difference-weighted KNN classifiers, and finally develop two novel classifiers for pulse waveform classification. The experimental results show that the proposed classifiers are effective for accurate classification of pulse waveform.

Keywords

  • Information Technology
  • Quantum Information
  • Full Article
  • Edit Distance
  • Pulse Waveform

Publisher note

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Authors’ Affiliations

(1)
Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
(2)
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, 518055, China

Copyright

© Dongyu Zhang et al. 2010

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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