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 Sliding empirical mode decomposition for on-line analysis of biomedical time series
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
authors: 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.
URI: http://hdl.handle.net/10773/5327
ISBN: 978-3-642-21500-1
ISSN: 0302-9743
publisher version/DOI: http://dx.doi.org/10.1007/978-3-642-21501-8_37
source: IWANN 2011: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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