Brain Status Data Analysis by Sliding EMD

  • A. Zeiler
  • R. Faltermeier
  • A. Brawanski
  • A. M. Tomé
  • C. G. Puntonet
  • J. M. Górriz
  • E. W. Lang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called slidingEMD, which is shown to perform well on large scale biomedical time series recorded during neuromonitoring.

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References

  1. 1.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.L., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. Roy. Soc. London A 454, 903–995 (1998)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Jánosi, I.M., Müller, R.: Empirical mode decomposition and correlation properties of long daily ozone records. Phys. Rev. E 71, 056126 (2005)CrossRefGoogle Scholar
  3. 3.
    Rilling, G., Flandrin, P., Goncalès, P.: On empirical mode decomposition and its algorithms. In: Proc. 6th IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (2003)Google Scholar
  4. 4.
    Wu, Z., Huang, N.: Ensemble empirical mode decomposition: A noise assisted data analysis method. Technical report, Center for Ocean-Land-Atmosphere Studies, 193, 51 (2005)Google Scholar
  5. 5.
    Flandrin, P., Gonçalvès, P., Rilling, G.: Emd equivalent filter banks: From interpretation to application. In: Huang, N., Shen, S. (eds.) Hilbert-Huang Transform: Introduction and Application, pp. 67–87. World Scientific, Singapore (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • A. Zeiler
    • 1
  • R. Faltermeier
    • 2
  • A. Brawanski
    • 2
  • A. M. Tomé
    • 3
  • C. G. Puntonet
    • 4
  • J. M. Górriz
    • 5
  • E. W. Lang
    • 1
  1. 1.CIML Group, BiophysicsUniversity of RegensburgRegensburgGermany
  2. 2.NeurosurgeryUniversity Regensburg Medical CenterRegensburgGermany
  3. 3.IEETA/DETIUniversidade de AveiroAveiroPortugal
  4. 4.DATC/ETSIUniversidad de GranadaGranadaSpain
  5. 5.DSTNCUniversidad de GranadaGranadaSpain

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