@inproceedings{50200e89213f4a6d9fef57e0b94e0b86,
title = "Comparison of wavelet thresholding methods for denoising ECG signals",
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).",
author = "Vladimir Cherkassky and Steven Kilts",
year = "2001",
month = jan,
day = "1",
doi = "10.1007/3-540-44668-0_87",
language = "English (US)",
isbn = "3540424865",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer- Verlag",
pages = "625--630",
editor = "Kurt Hornik and Georg Dorffner and Horst Bischof",
booktitle = "Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings",
note = "International Conference on Artificial Neural Networks, ICANN 2001 ; Conference date: 21-08-2001 Through 25-08-2001",
}