• DocumentCode
    2495749
  • Title

    Empirical Mode Decomposition - an introduction

  • Author

    Zeiler, A. ; Faltermeier, R. ; Keck, I.R. ; Tome, A.M. ; Puntonet, C.G. ; Lang, E.W.

  • Author_Institution
    Biophys. Dept., Univ. of Regensburg, Regensburg, Germany
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The method is fully adaptive and generates the basis to represent the data solely from these data and based on them. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications.
  • Keywords
    Hilbert transforms; medical signal processing; physiology; Hilbert spectral transform; Hilbert-Huang transform; biomedical signal; intrinsic mode functions; orthogonal basis function; physiological process; Boundary conditions; Interpolation; Oscillators; Signal resolution; Spline; Time series analysis; Transforms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2010 International Joint Conference on
  • Conference_Location
    Barcelona
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-6916-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2010.5596829
  • Filename
    5596829