Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics

A hybrid denoising method is presented as a combination of Empirical Mode Decomposition (EMD) and Higher Order Statistics (HOS). EMD, an adaptive data-driven method, is used for effective decomposition of a noisy signal into its functional components. Then Kurtosis and Bispectrum operate as Gaussianity estimators, supplemented by Bootstrap techniques, ensuring detection and removal of the signal’s Gaussian components. Thresholding techniques are used at the final step for maximum suppression of signal noise, where thresholds are set by estimating the long-term correlation of the corrupting colored noise in the form of the Hurst exponent. Experimental results prove the applicability of the method in signal denoising. Specifically, EMD-HOS outperforms similar denoising techniques based on Wavelets for the most types of test signals.

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