EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs

Abstract

This paper introduces a new signal-filtering, which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The key idea of this paper is to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode. Different similarity measures are investigated and compared. The obtained results, on simulated and real signals, show the effectiveness of the pdf-based filtering strategy for removing both white Gaussian and colored noises and demonstrate its superior performance over partial reconstruction approaches reported in the literature.

Ensemble EMD-Based Automatic Extraction of the Catenary Structure Wavelength From the Pantograph–Catenary Contact Force

“…Despite of the lack of theoretical support [25], EMD has been widely used in many applications where signal decomposition is needed [26]- [29]. In some previous studies [30], [31], it is specifically adopted to eliminate the useless or noisy components of a signal. However, the extraction approach in this paper considers both the CSWs and the non-CSW PCCF useful components.…”

Probabilistic Entropy EMD Thresholding for Periodic Fault Signal Enhancement in Rotating Machine

“…Inspired by the IMF-dependent universal threshold proposed by [20], the baseline entropy threshold in each IMF is defined in…”

“…Recently, based on the statistical characteristics analysis of white Gaussian noise and fractional Gaussian noise in EMD sifting process [9][10][11], Flandrin et al put forward an EMD denoising scheme with partial reconstruction (EMD-PR) of relevant IMFs in an adaptive way [12], and many attempts have been made to select relevant IMFs in an efficient way [13][14][15][16][17][18][19][20]. Boudraa and Cexus proposed a distortion measure method called consecutive mean square error (CMSE) to determine the relevant IMFs [13].…”

“…Ricci and Pennacchi [14] and Albert and Nii [15] developed a merit index and correlation coefficient to realize the automatic selection of relevant IMFs, respectively. Komaty et al [20] provided a new EMDbased filtering algorithm, in which the relevant IMFs were selected on the basis of a striking similarity between the 2 Shock and Vibration probability density function of the measured signals and each IMF.…”

Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist-type photoplethysmography

“…Empirical Mode Decomposition (EMD) is another attractive method to remove MA from biomedical signals, such as ECG signals [11,12] and PPG signals [13,14]. EMD is a data-driven algorithm that decomposes a time series into multiple intrinsic mode functions (IMFs) [15].…”