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 Brain status data analysis by sliding EMD
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/5623

title: Brain status data analysis by sliding EMD
authors: Zeiler, A.
Faltermeier, R.
Brawanski, A.
Tomé, A.M.
Puntonet, C.G.
Górriz, J.M.
Lang, E.W.
issue date: Jun-2011
publisher: Springer-Verlag
abstract: Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called slidingEMD, which is shown to perform well on large scale biomedical time series recorded during neuromonitoring.
URI: http://hdl.handle.net/10773/5623
ISBN: 978-3-642-21325-0
ISSN: 0302-9743
publisher version/DOI: http://dx.doi.org/10.1007/978-3-642-21326-7_9
source: IWINAC 2011 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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