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Characterization of Inter-Cycle Variations for Wrist Pulse Diagnosis

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Computational Pulse Signal Analysis

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Abstract

Although pulse signal is quasiperiodic, most feature extraction methods usually consider it as a whole or only use a single cycle, neglecting the variations between pulse cycles. To characterize both the inter- and intra-cycle variations, in this chapter we propose three feature extraction methods, i.e., simple combination, multi-scale entropy, and complex network. The simple combination method is a direct extension of conventional single-cycle feature extraction method by concatenating features from multiple cycles. The multi-scale entropy method measures the inter- and intra-cycle variations using entropies of different scales. The complex network method transforms the pulse signal from time domain to network domain and measures the inter-cycle variations using the statistical properties on complex network. Experimental results show that the presented features are effective in characterizing both inter- and intra-cycle variations and can obtain better performance in pulse diagnosis.

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Author information

Authors and Affiliations

  1. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, China

    David Zhang

  2. Harbin Institute of Technology, Harbin, China

    Wangmeng Zuo

  3. Northeast Agricultural University, Harbin, China

    Peng Wang

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  1. David Zhang
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  2. Wangmeng Zuo
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  3. Peng Wang
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Zhang, D., Zuo, W., Wang, P. (2018). Characterization of Inter-Cycle Variations for Wrist Pulse Diagnosis. In: Computational Pulse Signal Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-4044-3_10

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  • DOI: https://doi.org/10.1007/978-981-10-4044-3_10

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  • Online ISBN: 978-981-10-4044-3

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