Ensemble EMD-based signal denoising using modified interval thresholding

Hongrui Wang, Zhigang Liu, Yang Song, Xiaobing Lu

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)452-461
Number of pages10
JournalIET Signal Processing
Volume11
Issue number4
Early online date6 Apr 2017
DOIs
Publication statusPublished - 1 Jun 2017
Externally publishedYes

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Signal denoising
Decomposition
Signal to noise ratio
Sampling

Cite this

Wang, Hongrui ; Liu, Zhigang ; Song, Yang ; Lu, Xiaobing. / Ensemble EMD-based signal denoising using modified interval thresholding. In: IET Signal Processing. 2017 ; Vol. 11, No. 4. pp. 452-461.
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Ensemble EMD-based signal denoising using modified interval thresholding. / Wang, Hongrui; Liu, Zhigang; Song, Yang; Lu, Xiaobing.

In: IET Signal Processing, Vol. 11, No. 4, 01.06.2017, p. 452-461.

Research output: Contribution to journalArticle

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AU - Wang, Hongrui

AU - Liu, Zhigang

AU - Song, Yang

AU - Lu, Xiaobing

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