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
Link To Document :
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