<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.</p></abstract>

This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Empirical Mode Decomposition in Discrete Time Signals Denoising

University of Medicine, Pharmacy, Sciences and Technology of Târgu Mureș

Acta Marisiensis. Seria Technologica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Empirical Mode Decomposition in Discrete Time Signals Denoising"}

This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been...

Empirical Mode Decomposition in Discrete Time Signals Denoising

Published Online: Aug 06, 2019

Page range: 10 - 13

DOI: https://doi.org/10.2478/amset-2019-0002

Keywords
empirical mode decomposition, signal processing, denoising

© 2019 Zoltán Germán-Salló, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Empirical Mode Decomposition in Discrete Time Signals Denoising

Published Online: Aug 06, 2019

Page range: 10 - 13

DOI: https://doi.org/10.2478/amset-2019-0002

Keywordsempirical mode decomposition, signal processing, denoising

© 2019 Zoltán Germán-Salló, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
empirical mode decomposition, signal processing, denoising