Name
Abstract | ||
|---|---|---|
In this paper a novel method, called WICA, based on the joint use of wavelet transform (WT) and independent component analysis (ICA) is discussed. The main advantage of this method is that it encompasses the characteristics of WT and ICA. In order to show the novelty of our method, we present a biomedical signal processing application in which ICA has poor performances, whereas WICA yields good results. In particular, we discuss the artifact cancellation in electrocardiographic (ECG) signals. The results show the ability of WICA to cancel some artifact from ECG when only two signals are recorded. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11731177_12 | italian workshop on neural nets |
Keywords | Field | DocType |
poor performance,mixed wavelet-ica filter,novel method,joint use,independent component analysis,biomedical signal processing application,wica yield,main advantage,artifact cancellation,good result,wavelet transform | Signal processing,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Independent component analysis,Artificial neural network,Wavelet transform,Wavelet | Conference |
Volume | ISSN | Citations |
3931 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabio La Foresta | 1 | 93 | 15.69 |
| Nadia Mammone | 2 | 136 | 19.69 |
| Francesco Carlo Morabito | 3 | 339 | 54.83 |