Proceedings ArticleDOI
Map Filtering in the Diversity-Enhanced Wavelet Domain Applied to Ecg Signals Denoising
M. Oltean,J.-M. Boucher,A. Isar +2 more
- Vol. 2, pp 1196-1199
TLDR
The method is based on a maximum a-posteriori filtering in the diversity-enhanced wavelet domain, under realistic a-priori assumptions regarding the statistical properties of the wavelet coefficients of the ECG signal.Abstract:
An effective denoising method for ECG signals affected by real sources of noise is proposed in this paper. The method is based on a maximum a-posteriori (MAP) filtering in the diversity-enhanced wavelet domain, under realistic a-priori assumptions regarding the statistical properties of the wavelet coefficients of the ECG signal. In order to evaluate the performance of the method, we studied the signal-to-noise ratio (SNR) improvement factor and the degree of the denoising influence on the automatic signal segmentation procedures. The method was tested in both synthetic and real noise conditions and it showed very promising results.read more
Citations
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Application of Wavelet Techniques in ECG Signal Processing: An Overview
TL;DR: An overview of some wavelet techniques published in journals and conferences since 2005 onwards for processing the ECG and also the performance, advantages and limitations of these techniques are compared.
Proceedings ArticleDOI
ECG statistical denoising in the wavelet domain
TL;DR: The paper presents a denoising algorithm particularly suited to ECG signals processing, which consists in a MAP filtering in the wavelet domain and shows very promising results.
ECG and APG signal analysis during exercise in a hot environment
TL;DR: The effect of heat stress on the electrocardiogram (ECG) and the acceleration plethysmogram (APG) was investigated and a new peak detection technique achieved a comprehensive detection of more than 98 percent for both the ECG and APG signals without adjusting thresholds.
Book ChapterDOI
A new LUT watermarking scheme with near minimum distortion based on the statistical modeling in the wavelet domain
Kan Li,Xiao-Ping Zhang +1 more
TL;DR: A new wavelet domain look-up table (LUT) watermarking algorithm that leads to the sub-optimal embedding of watermarks in the sense of minimizing distortion and provides a joint distortion-robustness design of the LUT based watermark.
Proceedings ArticleDOI
A three step algorithm based on biorthogonal wavelets for an intelligent cardiac remote monitoring system
TL;DR: A wavelet based algorithm using biorthogonal mother wavelets is presented in this paper, which proposes methods for three important steps to be incorporated in a remote monitoring system for cardiac illnesses: baseline correction, denoising and compression of electrocardiograms.
References
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Journal ArticleDOI
Adapting to Unknown Smoothness via Wavelet Shrinkage
TL;DR: In this article, the authors proposed a smoothness adaptive thresholding procedure, called SureShrink, which is adaptive to the Stein unbiased estimate of risk (sure) for threshold estimates and is near minimax simultaneously over a whole interval of the Besov scale; the size of this interval depends on the choice of mother wavelet.
Book ChapterDOI
Translation-Invariant De-Noising
TL;DR: A reconstruction subject to far weaker Gibbs phenomena than thresholding based De-Noising using the traditional orthogonal wavelet transform is produced.
Journal ArticleDOI
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
L. Sendur,Ivan Selesnick +1 more
TL;DR: This work proposes new non-Gaussian bivariate distributions, and corresponding nonlinear threshold functions (shrinkage functions) are derived from the models using Bayesian estimation theory, but the new shrinkage functions do not assume the independence of wavelet coefficients.
Journal ArticleDOI
Wavelet threshold estimators for data with correlated noise
TL;DR: In this paper, a wavelet threshold estimator for data with stationary correlated noise is constructed by applying a level-dependent soft threshold to the coefficients in the wavelet transform, and a variety of threshold choices are proposed, including one based on an unbiased estimate of mean-squared error.
Proceedings ArticleDOI
Improved wavelet denoising via empirical Wiener filtering
TL;DR: A new algorithm for wavelet denoising is developed that uses aWavelet shrinkage estimate as a means to design a wavelet-domain Wiener filter and typically decreases both bias and variance compared to wavelet shrinkages.
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