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.
Highlights
Traditional Chinese pulse diagnosis (TCPD) is a convenient, noninvasive, and effective diagnostic method that has been widely used in traditional Chinese medicine (TCM) [1]
Much progress has been made: a range of pulse signal acquisition systems have been developed for various pulse analysis tasks [7,8,9]; a number of signal preprocessing and analysis methods have been proposed in pulse signal denoising, baseline rectification [10], segmentation [11]; many pulse feature extraction approaches have been suggested by using various timefrequency analysis techniques [12,13,14]; many classification methods have been studied for pulse diagnosis [15, 16] and pulse waveform classification [17,18,19]
Because of the complicated intra-class variation in pulse patterns and the inevitable influence of local time shifting in pulse waveforms, it has remained a difficult problem for automatic pulse waveform classification
Summary
Traditional Chinese pulse diagnosis (TCPD) is a convenient, noninvasive, and effective diagnostic method that has been widely used in traditional Chinese medicine (TCM) [1]. In TCPD, practitioners feel for the fluctuations in the radial pulse at the styloid processes of the wrist and classify them into the distinct patterns which are related to various syndromes and diseases in TCM. This is a skill which requires considerable training and experience, and may produce significant variation in diagnosis results for different practitioners. We evaluate the proposed methods on a pulse waveform data set of five common pulse patterns, moderate, smooth, taut, unsmooth, and hollow.
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