DIGITAL SIGNAL PROCESSING, cilt.32, ss.48-56, 2014 (SCI-Expanded)
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.