Sliding Empirical Mode Decomposition-Brain Status Data Analysis and Modeling

  • A. Zeiler
  • R. Faltermeier
  • A. M. Tomé
  • I. R. Keck
  • C. Puntonet
  • A. Brawanski
  • E. W. Lang
Part of the Studies in Computational Intelligence book series (SCI, volume 410)


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.


Mean Square Error Window Size Arterial Blood Pressure Empirical Mode Decomposition Reconstruction Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • A. Zeiler
    • 1
  • R. Faltermeier
    • 2
  • A. M. Tomé
    • 3
  • I. R. Keck
    • 1
  • C. Puntonet
    • 4
  • A. Brawanski
    • 2
  • E. W. Lang
    • 1
  1. 1.CIML Group, BiophysicsUniversity of RegensburgRegensburgGermany
  2. 2.NeurosurgeryUniversity Regensburg Medical CenterRegensburgGermany
  3. 3.DETI/IEETAUniversidade de AveiroAveiroPortugal
  4. 4.DATC, ETSIITUniversidad de GranadaGranadaSpain

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