Singular-spectrum analysis: a toolkit for short, noisy chaotic signals
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Wavelets and subband coding
Nonlinear time series analysis
Nonlinear time series analysis
Physica D
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Results on principal component filter banks: colored noise suppression and existence issues
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive chaotic noise reduction method based on dual-lifting wavelet
Expert Systems with Applications: An International Journal
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Assuming a signal is composed of information and noise, this paper presents a generic approach to denoising by mapping the noisy signal using a priori information about the signal to be retrieved. The method is based on a subspace decomposition of both the a priori information at disposal and the noisy signal, followed by shrinkage of both subspace coefficients and smooth mapping of first onto the second space. Our method propose 3 different ways of building the a priori knowledge: A model-base, averaging-based and recursive-based. The proposed method is particularly suited for low signal to noise ratio. The denoising methods are validated on synthetic electrocardiogram (ECG) signals and further assessed on real life ECG and visual brain evoked potentials using the wavelet transform.