The nature of statistical learning theory
The nature of statistical learning theory
Signal estimation and denoising using VC-theory
Neural Networks
Data compression and harmonic analysis
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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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).