NDLI: Ensemble EMD-based signal denoising using modified interval thresholding

Content Provider	IET Digital Library
Author	Wang, Hongrui Liu, Zhigang Song, Yang Lu, Xiaobing
Abstract	Empirical mode decomposition (EMD) is extensively realised in its potential of non-parametric signal denoising. Ensemble EMD (EEMD) is an improved self-adapting signal decomposition approach that can produce signal components with no frequency aliasing. In this study, the interval thresholding and iteration operation of EMD-based denoising techniques are applied to the EEMD and found not entirely feasible in the EEMD case. A modified interval thresholding is proposed, which can be adjustable for the intrinsic mode functions from EEMD. By taking advantage of the characteristics of EEMD, the internal and external iterations are compared and properly adopted in the EEMD-based denoising strategy. As a result, the EEMD-based denoising methods are proposed by combining the modified interval thresholding and the iterations. The denoising results on synthetic and real-life signals indicate that the presented methods exhibit better performance comparing with EMD-based methods, especially for signals with low signal-to-noise ratio. Based on the time complexities of the proposed methods, the acceptable sampling frequencies of the methods in real-time denoising are given.
Starting Page	452
Ending Page	461
Page Count	10
ISSN	17519675
Volume Number	11
e-ISSN	17519683
Issue Number	Issue 4, Jun (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/11/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2016.0147
Journal	IET Signal Processing
Publisher Date	2016-12-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computational Complexity Empirical Mode Decomposition Ensemble EMD-based Signal Denoising Interpolation And Function Approximation Intrinsic Mode Function Iteration Operation Iterative Method Modified Interval Thresholding Nonparametric Signal Denoising Numerical Analysis Self-adapting Signal Decomposition Signal Component Signal Denoising Signal Processing And Detection Signal Processing Theory Signal to Noise Ratio Synthetic Real-life Signal Time Complexity
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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