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    The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer's disease

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    Publication date
    2015
    Author
    Wang, G.
    Shepherd, Simon J.
    Beggs, Clive B.
    Rao, N.
    Zhang, Y.
    Keyword
    Alzheimer's disease
    ; Electroencephalogram; EEG
    ; Kurtosis
    ; Sample entropy; SE
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    The use of electroencephalograms (EEGs) to diagnose and analyses Alzheimer's disease (AD) has received much attention in recent years. The sample entropy (SE) has been widely applied to the diagnosis of AD. In our study, nine EEGs from 21 scalp electrodes in 3 AD patients and 9 EEGs from 3 age-matched controls are recorded. The calculations show that the kurtoses of the AD patients' EEG are positive and much higher than that of the controls. This finding encourages us to introduce a kurtosis-based de-noising method. The 21-electrode EEG is first decomposed using independent component analysis (ICA), and second sort them using their kurtoses in ascending order. Finally, the subspace of EEG signal using back projection of only the last five components is reconstructed. SE will be calculated after the above de-noising preprocess. The classifications show that this method can significantly improve the accuracy of SE-based diagnosis. The kurtosis analysis of EEG may contribute to increasing the understanding of brain dysfunction in AD in a statistical way.
    URI
    http://hdl.handle.net/10454/9242
    Version
    No full-text available in the repository
    Citation
    Wang G, Shepherd SJ, Beggs CB et al (2015) The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer's disease. Bio-Medical Materials and Engineering. 26(s1): S1135-S1148.
    Link to publisher’s version
    http://dx.doi.org/10.3233/BME-151410
    Type
    Article
    Collections
    Engineering and Digital Technology Publications

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