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

Escudero, J, Hornero, R, Abásolo, D, Fernández, A and Poza, J (2007) Magnetoencephalogram blind source separation and component selection procedure to improve the diagnosis of Alzheimer's disease patients.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords: Aged, Algorithms, Alzheimer Disease, Artificial Intelligence, Brain, Diagnosis, Computer-Assisted, Female, Humans, Magnetoencephalography, Male, Pattern Recognition, Automated, Principal Component Analysis, Reproducibility of Results, Sensitivity and Specificity
Divisions: Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Depositing User: Symplectic Elements
Date Deposited: 14 Nov 2012 13:54
Last Modified: 23 Sep 2013 19:37
URI: http://epubs.surrey.ac.uk/id/eprint/714844

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