Name
Abstract | ||
|---|---|---|
We present empirical comparisons of several wavelet-denoising methods applied to the problem of removing (denoising) myopotential noise from the observed noisy ECG signal. Namely, we compare the denoising accuracy of several wavelet thresholding methods (VISU, SURE and soft thresholding) and a new thresholding approach based on Vapnik-Chervonenkis (VC) learning theory. Our findings indicate that the VC-based wavelet approach is superior to the standard thresholding methods in that it achieves higher denoising accuracy (in terms of both MSE measure and visual quality) as well as a more robust and compact representation of the denoised signal (i.e., it uses fewer wavelets). |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-44668-0_87 | ICANN |
Keywords | Field | DocType |
denoised signal,fewer wavelet,higher denoising accuracy,standard thresholding method,denoising accuracy,soft thresholding,wavelet thresholding methods,new thresholding approach,mse measure,denoising ecg signals,vc-based wavelet approach,observed noisy ecg signal,learning theory | Noise reduction,Pattern recognition,Wavelet thresholding,Computer science,Signal-to-noise ratio,Mean squared error,Artificial intelligence,Balanced histogram thresholding,Thresholding,Video denoising,Wavelet | Conference |
Volume | ISSN | ISBN |
2130 | 0302-9743 | 3-540-42486-5 |
Citations | PageRank | References |
1 | 0.37 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Cherkassky | 1 | 1064 | 126.66 |
| Steven Kilts | 2 | 10 | 2.08 |