Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21501-8_37 | IWANN (1) |
Keywords | Field | DocType |
empirical mode decomposition,hilbert-huang transform,general non-linear,complete set,non-stationary time series,biomedical signal,intrinsic mode functions,on-line analysis,data-driven technique,biomedical time series necessitate,biomedical time series | Pattern recognition,Computer science,Orthogonal basis,Artificial intelligence,Machine learning,Hilbert–Huang transform | Conference |
Volume | ISSN | Citations |
6691 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. W. Lang | 1 | 4 | 1.09 |
| Rupert Faltermeier | 2 | 17 | 5.38 |
| A M Tome | 3 | 116 | 10.44 |
| C. G. Puntonet | 4 | 354 | 34.99 |
| Alexander Brawanski | 5 | 17 | 5.38 |
| Elmar Wolfgang Lang | 6 | 260 | 36.10 |