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Analysis of MEG recordings from Alzheimer's disease patients with sample and multiscale entropies.

Gómez, C, Hornero, R, Abásolo, D, Fernández, A and Escudero, J (2007) Analysis of MEG recordings from Alzheimer's disease patients with sample and multiscale entropies.

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Abstract

Alzheimer's disease (AD) is one of the most prominent neurodegenerative disorders. The aim of this study is to analyze the magnetoencephalogram (MEG) background activity in AD patients using sample entropy (SampEn) and multiscale entropy (MSE). The former quantifies the signal regularity, while the latter is a complexity measure. These concepts, irregularity and complexity, are linked although the relationship is not straightforward. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 20 patients with probable AD and 21 control subjects. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in some MEG channels with both methods (p<0.01, Student's t-test with Bonferroni's correction). Using receiver operating characteristic curves, accuracies of 75.6% with SampEn and of 87.8% with MSE were reached. Our findings show the usefulness of these entropy measures to increase our insight into AD.

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, Entropy, Female, Humans, Magnetoencephalography, Male, Pattern Recognition, Automated, 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 10:58
Last Modified: 23 Sep 2013 19:36
URI: http://epubs.surrey.ac.uk/id/eprint/713801

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