Dispersion entropy for the analysis of resting-state MEG regularity in Alzheimer's disease

Conference proceedings article


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Publication Details

Author list: Fernández A
Publisher: Elsevier
Publication year: 2016
Title of series: Article number 7592197
Volume number: 2016-October
Start page: 6417
End page: 6420
Number of pages: 4
ISBN: 978-145770220-4
Languages: English-Great Britain (EN-GB)


Abstract

Alzheimer's disease (AD) is a progressive degenerative brain disorder
affecting memory, thinking, behaviour and emotion. It is the most common
form of dementia and a big social problem in western societies. The
analysis of brain activity may help to diagnose this disease. Changes in
entropy methods have been reported useful in research studies to
characterize AD. We have recently proposed dispersion entropy (DisEn) as
a very fast and powerful tool to quantify the irregularity of time
series. The aim of this paper is to evaluate the ability of DisEn, in
comparison with fuzzy entropy (FuzEn), sample entropy (SampEn), and
permutation entropy (PerEn), to discriminate 36 AD patients from 26
elderly control subjects using resting-state magnetoencephalogram (MEG)
signals. The results obtained by DisEn, FuzEn, and SampEn, unlike PerEn,
show that the AD patients' signals are more regular than controls' time
series. The p-values obtained by DisEn, FuzEn, SampEn, and PerEn based
methods demonstrate the superiority of DisEn over PerEn, SampEn, and
PerEn. Moreover, the computation time for the newly proposed DisEn-based
method is noticeably less than for the FuzEn, SampEn, and PerEn based
approaches.


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