Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596533 | Neural Networks |
Keywords | Field | DocType |
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 | Time series,Signal processing,Oscillation,Pattern recognition,Computer science,Orthogonal basis,Artificial intelligence,Time–frequency analysis,Amplitude,Hilbert–Huang transform | Conference |
ISSN | ISBN | Citations |
1098-7576 | 978-1-4244-6916-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angela Zeiler | 1 | 8 | 2.24 |
| Rupert Faltermeier | 2 | 17 | 5.38 |
| Ingo R. Keck | 3 | 13 | 4.80 |
| Ana Maria Tomé | 4 | 163 | 30.42 |
| Alexander Brawanski | 5 | 17 | 5.38 |
| Carlos García Puntonet | 6 | 107 | 25.86 |
| Elmar Wolfgang Lang | 7 | 260 | 36.10 |