Improved complete ensemble EMD: A suitable tool for biomedical signal processing

By Marcelo Alejandro Colominas, Gastón Schlotthauer, María Eugenia Torres. Biomedical Signal Processing and Control, 2014

1,337 citations recorded by the source corpus.

Abstract The empirical mode decomposition (EMD) decomposes non-stationary signals that may stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted versions have been proposed to alleviate the so-called “mode mixing” phenomenon, which may appear when real signals are analyzed. Among them, the complete ensemble EMD with adaptive noise (CEEMDAN) recovered the completeness property of EMD. In this work we present improvements on this last technique, obtaining components with less noise and more physical meaning. Artificial signals are analyzed to illustrate the capabilities of the new method. Finally, several real biomedical signals are decomposed, obtaining components that represent physiological phenomenons.

Subfields: Cardiology and Cardiovascular Medicine, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/fc0f9d8642a332a64481a790a4fa719aa1c7ed5b

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Citations in the corpus

• A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery (2019, methodology): “While avoiding modal mixing, CEEMDAN can effectively solve the incomplete decomposition problem of EEMD method [36].”