Classification of pulse waveforms using edit distance with real penalty

Authors:
Dongyu Zhang;Wangmeng Zuo;David Zhang;Hongzhi Zhang;Naimin Li
Affiliations:
Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2010

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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 k-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.