Detrended fluctuation thresholding for empirical mode decomposition based denoising


Mert A., Akan A.

DIGITAL SIGNAL PROCESSING, vol.32, pp.48-56, 2014 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32
  • Publication Date: 2014
  • Doi Number: 10.1016/j.dsp.2014.06.006
  • Title of Journal : DIGITAL SIGNAL PROCESSING
  • Page Numbers: pp.48-56

Abstract

Signal decompositions such as wavelet and Gabor transforms have successfully been applied in denoising
problems. Empirical mode decomposition (EMD) is a recently proposed method to analyze non-linear and
non-stationary time series and may be used for noise elimination. Similar to other decomposition based
denoising approaches, EMD based denoising requires a reliable threshold to determine which oscillations
called intrinsic mode functions (IMFs) are noise components or noise free signal components. Here,
we propose a metric based on detrended fluctuation analysis (DFA) to define a robust threshold. The
scaling exponent of DFA is an indicator of statistical self-affinity. In our study, it is used to determine
a threshold region to eliminate the noisy IMFs. The proposed DFA threshold and denoising by DFA–EMD
are tested on different synthetic and real signals at various signal to noise ratios (SNR). The results are
promising especially at 0 dB when signal is corrupted by white Gaussian noise (WGN). The proposed
method outperforms soft and hard wavelet threshold method.

 

Signal decompositions such as wavelet and Gabor transforms have successfully been applied in denoising problems. Empirical mode decomposition (EMD) is a recently proposed method to analyze non-linear and non-stationary time series and may be used for noise elimination. Similar to other decomposition based denoising approaches, EMD based denoising requires a reliable threshold to determine which oscillations called intrinsic mode functions (IMFs) are noise components or noise free signal components. Here, we propose a metric based on detrended fluctuation analysis (DFA) to define a robust threshold. The scaling exponent of DFA is an indicator of statistical self-affinity. In our study, it is used to determine a threshold region to eliminate the noisy IMFs. The proposed DFA threshold and denoising by DFA-EMD are tested on different synthetic and real signals at various signal to noise ratios (SNR). The results are promising especially at 0 dB when signal is corrupted by white Gaussian noise (WGN). The proposed method outperforms soft and hard wavelet threshold method. (C) 2014 Elsevier Inc. All rights reserved.