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.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 Boudraa, A. O., Cexus, J. C. (2007), EMD-Based Signal Filtering, IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 6, pp. 2196-2202.
 Bastiaans, M.J., Alieva, T., Stankovic, L. (2002), On rotated time-frequency kernels, IEEE Signal Processing. Letters, vol. 9 (11), pp.378-381.
 Boashash B. (2016), Time-Frequency Signal Analysis and Processing: A Comprehensive Review, 2nd ed., Eurasip and Academic Press Series in Signal and Image Processing, Academic Press.
 Rilling G., Flandrin P. and Goncalves P. (2003), On Empirical Mode Decomposition and its algorithms, IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado (I).
 Rilling G., Flandrin P., Goncalves P. and Lilly. J. M., Bivariate Empirical Mode Decomposition, Signal Processing Letters (submitted).
 Huang N. E. et al.(2003), A confidence limit for the Empirical Mode Decomposition and Hilbert spectral analysis, Proc. Royal Soc. London A, vol. 459, pp. 2317-2345.
 Janosi, I. M., and R. Muller (2005), Empirical mode decomposition and correlation properties of long daily ozone records, Phys. Rev. E, 71, 056126, doi:10.1103/PhysRevE.71.056126.
 Kopsinis Y, McLaughlin S. (2009), Development of EMD-based denoising methods inspired by wavelet thresholding, IEEE Transactions on Signal Processing, 57:1351-1362.
 Kopsinis, Y., McLaughlin, S. (2009) Development of EMD based denoising methods inspired by wavelet thresholding, IEEE Transactions on Signal Processing, vol. 57, pp. 1351-1362.
 Kay, S. (2006), Intuitive Probability and Random Processes Using MATLAB, Springer Science & Business Media, Berlin.
 Khan, N.A., Boashash, B.(2013), Instantaneous frequency estimation of multicomponent nonstationary signals using multiview time-frequency distributions based on the adaptive fractional spectrogram, IEEE Signal Processing Letters, vol. 20 (2), pp.157-160.
 Sejdic, E., Djurovic, I., Jiang, J., (2009), Time-frequency feature representation using energy concentration: an overview of recent advances, Digital Signal Processing, vol. 19 (1), pp. 153-183.
 Stankovic, L., Dakovic, M, Thayaparan, T. (2013), Time-Frequency Signal Analysis with Applications, Artech House, Boston.
 Tsolis, G. S., Xenos, T. D. (2011), Signal denoising using empirical mode decomposition and higher order statistics, Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 4, no. 2, pp. 91-106.