Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/5327
Title: Sliding empirical mode decomposition for on-line analysis of biomedical time series
Author: Zeiler, A.
Faltermeier, R.
Tomé, A. M.
Puntonet, C.
Brawanski, A.
Lang, E. W.
Issue Date: 2011
Publisher: Springer Verlag
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.
Peer review: yes
URI: http://hdl.handle.net/10773/5327
DOI: 10.1007/978-3-642-21501-8_37
ISBN: 978-3-642-21500-1
ISSN: 0302-9743
Appears in Collections:DETI - Comunicações



FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.