Sliding Empirical Mode Decomposition for On-line Analysis of Biomedical Time Series

  • A. Zeiler
  • R. Faltermeier
  • A. M. Tomé
  • C. Puntonet
  • A. Brawanski
  • E. W. Lang
Conference paper

DOI: 10.1007/978-3-642-21501-8_37

Volume 6691 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Zeiler A., Faltermeier R., Tomé A.M., Puntonet C., Brawanski A., Lang E.W. (2011) Sliding Empirical Mode Decomposition for On-line Analysis of Biomedical Time Series. In: Cabestany J., Rojas I., Joya G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg

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 orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an on-line analysis which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the newly proposed slidingEMD algorithm and presents some applications to biomedical time series from neuromonitoring.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • A. Zeiler
    • 1
  • R. Faltermeier
    • 2
  • A. M. Tomé
    • 3
  • C. Puntonet
    • 4
  • A. Brawanski
    • 2
  • E. W. Lang
    • 1
  1. 1.CIML Group, BiophysicsUniversity of RegensburgRegensburgGermany
  2. 2.Clinic of NeurosurgeryUniversity Hospital RegensburgRegensburgGermany
  3. 3.DETI/IEETAUniversidade de AveiroAveiroPortugal
  4. 4.DATC, EEISUniversidad de GranadaGranadaSpain