Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding

By Yannis Kopsinis, Steven W. McLaughlin. IEEE Transactions on Signal Processing, 2009

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Filter Bank Property of Multivariate Empirical Mode Decomposition (2011, methodology): “The stopping criteria used was the S-stopping criterion [17], with the value of . The filter bank structure may be slightly affected by the number of sifting iterations employed, and possibly by the type of stopping criteria used, as mentioned briefly in [ 19 ], for a single-channel EMD.”
Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding (2015, methodology): “Based on these promising characteristics, Magrin-Chagnolleau and Baraniuk (1999) and Han and Van der Baan (2011) apply EMD for robust seismic attribute analysis, Battista et al.”
Variational mode decomposition denoising combined the detrended fluctuation analysis (2016, background): “To avoid these shortcomings, in this paper a combination of VMD and DFA, named DFA–VMD, is proposed to denoise noisy signals corrupted with WGN.”
EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “With regard to EMD, inspired by wavelet thresholding, the authors of [127] use an algorithm that evaluates the noise level and filters each IMF.”