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Sliding Empirical Mode Decomposition-Brain Status Data Analysis and Modeling

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Advances in Intelligent Signal Processing and Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 410))

Abstract

Biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract Intrinsic Mode Functions (IMFs). The latter represent a complete set of locally orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an online analysis which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the newly proposed SEMD algorithm and presents some applications to biomedical time series recorded during neuromonitoring.

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Author information

Authors and Affiliations

  1. CIML Group, Biophysics, University of Regensburg, D-93040, Regensburg, Germany

    A. Zeiler, I. R. Keck & E. W. Lang

  2. Neurosurgery, University Regensburg Medical Center, D-93040, Regensburg, Germany

    R. Faltermeier & A. Brawanski

  3. DETI/IEETA, Universidade de Aveiro, 3810-193, Aveiro, Portugal

    A. M. Tomé

  4. DATC, ETSIIT, Universidad de Granada, 38170, Granada, Spain

    C. Puntonet

Authors
  1. A. Zeiler
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  2. R. Faltermeier
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  3. A. M. Tomé
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  4. I. R. Keck
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  5. C. Puntonet
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  6. A. Brawanski
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  7. E. W. Lang
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Editor information

Editors and Affiliations

  1. , Dep. of Electronics Telecommunications, University of Aveiro, Aveiro, 3800 - 193, Portugal

    Petia Georgieva

  2. , School of Computing and Communications, Lancaster University, InfoLab21, South Drive, Lancaster, LA1 4WA, Montserrat

    Lyudmila Mihaylova

  3. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

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Zeiler, A. et al. (2013). Sliding Empirical Mode Decomposition-Brain Status Data Analysis and Modeling. In: Georgieva, P., Mihaylova, L., Jain, L. (eds) Advances in Intelligent Signal Processing and Data Mining. Studies in Computational Intelligence, vol 410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28696-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-28696-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28695-7

  • Online ISBN: 978-3-642-28696-4

  • eBook Packages: EngineeringEngineering (R0)

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