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Univariate and Multivariate Generalised Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease

Azami, H, Abasolo, Daniel Emilio, Simons, Samantha and Escudero, J (2017) Univariate and Multivariate Generalised Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease Entropy An International and Interdisciplinary Journal of Entropy and Information Studies, 19 (1).

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Abstract

Alzheimer’s disease (AD) is a degenerative brain disorder leading to memory loss and changes in other cognitive abilities. The complexity of electroencephalogram (EEG) signals may help to characterise AD. To this end, we propose an extension of multiscale entropy based on variance (MSEσ2) to multichannel signals, termed multivariate MSEσ2 (mvMSEσ2), to take into account both the spatial and time domains of time series. Then, we investigate the mvMSEσ2 of EEGs at different frequency bands, including the broadband signals filtered between 1 and 40Hz, θ, α, and β bands, and compare it with the previously proposed multiscale entropy based on mean (MSEµ), multivariate MSEµ (mvMSEµ), and MSEσ2, to distinguish different kinds of dynamical properties of spread and mean in the signals. Results from 11 AD patients and 11 age-matched controls suggest that the presence of broadband activity of EEGs is required for a proper evaluation of complexity. MSEσ2 and mvMSEσ2 results, showing a loss of complexity in AD signals, led to smaller p-values in comparison with MSEµ and mvMSEµ ones, suggesting that the variance-based MSE and mvMSE can characterise changes in EEGs as a result of AD in a more detailed way. The p-values for the slope values of mvMSE curves were smaller than for MSE at large scale factors, also showing the possible usefulness of multivariate techniques.

Item Type: Article
Subjects : Mechanical Engineering Sciences
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
NameEmailORCID
Azami, HUNSPECIFIEDUNSPECIFIED
Abasolo, Daniel EmilioD.Abasolo@surrey.ac.ukUNSPECIFIED
Simons, Samanthas.simons@surrey.ac.ukUNSPECIFIED
Escudero, JUNSPECIFIEDUNSPECIFIED
Date : 12 January 2017
Identification Number : 10.3390/e19010031
Copyright Disclaimer : Copyright 2017 The Authors. Published under a Creative Commons Attribution License 4.0
Uncontrolled Keywords : Alzheimer’s disease; complexity; multivariate generalized multiscale entropy; statistical moments; electroencephalogram
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 13 Jan 2017 13:08
Last Modified : 19 Jul 2017 07:55
URI: http://epubs.surrey.ac.uk/id/eprint/813283

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