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Classification of Pulse Waveforms Using Edit Distance with Real Penalty


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.

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Correspondence to Wangmeng Zuo.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhang, D., Zuo, W., Zhang, D. et al. Classification of Pulse Waveforms Using Edit Distance with Real Penalty. EURASIP J. Adv. Signal Process. 2010, 303140 (2010).

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