Detrending and denoising with empirical mode decompositions

By Patrick Flandrin, Paulo Gonçalves, Gabriel Rilling. European Signal Processing Conference, 2004

334 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b09566501a540b9a89a1960b5dce079ba9be69f1

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1. Jiajun Han
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3. M. Blanco–Velasco
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5. Binwei Weng
6. Shubhojeet Chatterjee

Citations in the corpus

• Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding (2015, methodology): “Thus, Flandrin et al. (2004b) and Wu and Huang (2004) nearly simultaneously investigate the EMD feature for Gaussian noise, and they conclude that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decomposition.”
• ECG signal denoising and baseline wander correction based on the empirical mode decomposition (2008, methodology): “strategy works well for those signals whose frequency content is clearly distinguished from that of noise and is successfully applied in [18,24].”