A modified frequency slice wavelet transform for physiological signal time-frequency analysis

Kan Luo, Keqin Du, Zhipeng Cai, Jianqing Li, Zhigang Wang, Alfred Cuschieri

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    As a new signal processing tool, a Modified frequency slice wavelet transform (MFSWT) is proposed for physiological signal time-frequency analysis in this study. The transform generates time-frequency representation from the frequency domain, and the reconstruction is independent of frequency slice function (FSF). To realize accurate time-frequency location of signal components, a bound signal-adaptive FSF was introduced to serve as a dynamic frequency filter for the transform. This method avoids troublesome parameter selection, is signal-adaptive and easy to use. The results of two case studies demonstrate the validity of the proposed method, which has good interpretability with high time-frequency resolution, and hence has significant potential for bio-signal time-frequency analysis.

    Original languageEnglish
    Title of host publicationProceedings - 2017 Chinese Automation Congress, CAC 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3441-3444
    Number of pages4
    Volume2017-January
    ISBN (Electronic)9781538635247
    ISBN (Print)9781538635254
    DOIs
    Publication statusPublished - 29 Dec 2017
    Event2017 Chinese Automation Congress, CAC 2017 - Jinan, China
    Duration: 20 Oct 201722 Oct 2017

    Conference

    Conference2017 Chinese Automation Congress, CAC 2017
    CountryChina
    CityJinan
    Period20/10/1722/10/17

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    Keywords

    • frequency slice wavelet transform
    • Physiological signal processing
    • spectrogram
    • time-frequency analysis

    Cite this

    Luo, K., Du, K., Cai, Z., Li, J., Wang, Z., & Cuschieri, A. (2017). A modified frequency slice wavelet transform for physiological signal time-frequency analysis. In Proceedings - 2017 Chinese Automation Congress, CAC 2017 (Vol. 2017-January, pp. 3441-3444). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC.2017.8243375