Magnetoencephalogram blind source separation and component selection procedure to improve the diagnosis of Alzheimer's disease patients

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Publication Details

Author list: Escudero J, Hornero R, Abásolo D, Fernández A, Poza J.
Publisher: Department of Signal Theory and Communications, E.T.S. Ingenieros de Telecomunicación, University of Valladolid
Publication year: 2007
Journal: Conference proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference (1557170X)
Start page: 2437
End page: 2440
Number of pages: 4
ISBN: 978-1-4244-0787-3
ISSN: 1557170X
Languages: English-Great Britain (EN-GB)


Abstract

The aim of this study was to improve the diagnosis of Alzheimer's
disease (AD) patients applying a blind source separation (BSS) and
component selection procedure to their magnetoencephalogram (MEG)
recordings. MEGs from 18 AD patients and 18 control subjects were
decomposed with the algorithm for multiple unknown signals extraction.
MEG channels and components were characterized by their mean frequency,
spectral entropy, approximate entropy, and Lempel-Ziv complexity. Using
Student's t-test, the components which accounted for the most
significant differences between groups were selected. Then, these
relevant components were used to partially reconstruct the MEG channels.
By means of a linear discriminant analysis, we found that the
BSS-preprocessed MEGs classified the subjects with an accuracy of 80.6%,
whereas 72.2% accuracy was obtained without the BSS and component
selection procedure.


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Last updated on 2019-13-08 at 00:16