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Possibilistic Entropy: A New Method for Nonlinear Dynamical Analysis of Biosignals

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Book cover Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6881))

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Abstract

The theory of nonlinear dynamical systems has opened doors to discovering potential patterns hidden in complex time-series data. An attrative approach to nonlinear time-series analysis is the measure of predictability which characterizes the data in terms of entropy. A new entropy measure is presented in this paper as a new nonlinear dynamical method, which is based on the theory of possibility and the kriging computation. The proposed model has the potential for studying complex biosignals.

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Author information

Authors and Affiliations

  1. School of Engineeering and Information Technology, The University of New South Wales, Canberra, ACT, 2600, Australia

    Tuan D. Pham

Authors
  1. Tuan D. Pham
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Editor information

Editors and Affiliations

  1. Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-str. 12, 67663, Kaiserslautern, Germany

    Andreas König

  2. Knowledge-Based Systems Group, Department of omputer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Andreas Dengel

  3. School of Business, University of Applied Sciences Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Knut Hinkelmann

  4. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Sakai, Osaka, Japan

    Koichi Kise

  5. KES International, P.O. Box 2115, BN43 9AF, Shoreham-by-sea, UK

    Robert J. Howlett

  6. University of South Australia, Mawson Lakes, 5095, Adelaide, SA, Australia

    Lakhmi C. Jain

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© 2011 Springer-Verlag Berlin Heidelberg

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Pham, T.D. (2011). Possibilistic Entropy: A New Method for Nonlinear Dynamical Analysis of Biosignals. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23851-2_48

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  • DOI: https://doi.org/10.1007/978-3-642-23851-2_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23850-5

  • Online ISBN: 978-3-642-23851-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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