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    The use of Ensemble Empirical Mode Decomposition with Canonical Correlation Analysis as a Novel Artifact Removal Technique


    Sweeney, Kevin T. and McLoone, Sean F. and Ward, Tomas E. (2013) The use of Ensemble Empirical Mode Decomposition with Canonical Correlation Analysis as a Novel Artifact Removal Technique. IEEE transactions on biomedical engineering, 60 (1). pp. 97-105. ISSN 0018-9294

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    Abstract

    Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are however considerably fewer techniques available if only a single channel measurement is available and yet single channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single channel measurements. The EEMD technique is first used to decompose the single channel signal into a multi-dimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second order statistics. The new technique is tested against the currently available Wavelet denoising and EEMDICA techniques using both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data and is shown to produce significantly improved results.

    Item Type: Article
    Additional Information: The definitive version of this article is available at doi: 10.1109/TBME.2012.2225427
    Keywords: Ensemble Empirical Mode Decomposition; EEMD; Canonical Correlation Analysis; CCA; Independent Component Analysis; ICA; Wavelet denoising; EEMD-CCA; EEMD-ICA;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 4364
    Depositing User: Dr Tomas Ward
    Date Deposited: 15 May 2013 14:39
    Journal or Publication Title: IEEE transactions on biomedical engineering
    Publisher: IEEE
    Refereed: Yes
    URI:

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