Zeiler, A. and Faltermeier, R. and Tome, A. M. and Puntonet, C. and Brawanski, A. and Lang, E. W. (2013) Weighted Sliding Empirical Mode Decomposition for Online Analysis of Biomedical Time Series. NEURAL PROCESSING LETTERS, 37 (1). pp. 21-32. ISSN 1370-4621,
Full text not available from this repository. (Request a copy)Abstract
Biomedical signals are in general non-linear and non-stationary. empirical mode decomposition in conjunction with a Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract intrinsic mode functions. The latter represent a complete set of locally orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an online analysis, which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the recently proposed weighted sliding EMD algorithm (wSEMD) and, additionally, proposes a more sophisticated implementation of the weighting process. As an application to biomedical signals we will show that wSEMD in combination with mutual information could be used to detect temporal correlations of arterial blood pressure and intracranial pressure monitored at a neurosurgical intensive care unit. We will demonstrate that the wSEMD technique renders itself much more flexible than the Fourier based method used in Faltermeier et al. (Acta Neurochir Suppl, 114, 35-38, 2012).
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ; Empirical mode decomposition; Neuromonitoring; Online analysis |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Neurochirurgie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 28 Apr 2020 09:25 |
| Last Modified: | 28 Apr 2020 09:25 |
| URI: | https://pred.uni-regensburg.de/id/eprint/17228 |
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