Brain status data analyzed by Empirical Mode Decomposition

Author

Zeiler, A. ; Faltermeier, R. ; Keck, I.R. ; Tomé, A.M. ; Brawanski, A. ; 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. Intelligent signal processing is crucial to unravel the information content buried in biomedical time series. Empirical Mode Decomposition is ideally suited to extract all pure oscillatory modes which are contained in the signal. These modes, called Intrinsic Mode Functions (IMFs), represent a complete set of locally orthogonal basis functions with time-varying amplitude and frequency. The contribution discusses the application of an online variant, called SEMD, to non-stationary biomedical time series recorded during neuromonitoring.

Keywords

data analysis; medical signal processing; time series; biomedical signals; biomedical time series; brain status data analysis; empirical mode decomposition; intelligent signal processing; intrinsic mode functions; Biomedical monitoring; Blood; Blood pressure; Iterative closest point algorithm; Oscillators; Time frequency analysis; Time series analysis;

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.2010.5596533

Filename

5596533