{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T18:14:27Z","timestamp":1779041667240,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,12]],"date-time":"2017-01-12T00:00:00Z","timestamp":1484179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Alzheimer\u2019s 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 (MSE\u03c32) to multichannel signals, termed multivariate MSE\u03c32 (mvMSE\u03c32), to take into account both the spatial and time domains of time series. Then, we investigate the mvMSE\u03c32 of EEGs at different frequency bands, including the broadband signals filtered between 1 and 40 Hz, \u03b8, \u03b1, and \u03b2 bands, and compare it with the previously-proposed multiscale entropy based on mean (MSE\u00b5), multivariate MSE\u00b5 (mvMSE\u00b5), and MSE\u03c32, to distinguish different kinds of dynamical properties of the spread and the 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. MSE\u03c32 and mvMSE\u03c32 results, showing a loss of complexity in AD signals, led to smaller p-values in comparison with MSE\u00b5 and mvMSE\u00b5 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 the mvMSE curves were smaller than for MSE at large scale factors, also showing the possible usefulness of multivariate techniques.<\/jats:p>","DOI":"10.3390\/e19010031","type":"journal-article","created":{"date-parts":[[2017,1,12]],"date-time":"2017-01-12T10:02:08Z","timestamp":1484215328000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7612-0840","authenticated-orcid":false,"given":"Hamed","family":"Azami","sequence":"first","affiliation":[{"name":"Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4268-2885","authenticated-orcid":false,"given":"Daniel","family":"Ab\u00e1solo","sequence":"additional","affiliation":[{"name":"Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4547-793X","authenticated-orcid":false,"given":"Samantha","family":"Simons","sequence":"additional","affiliation":[{"name":"Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-8725","authenticated-orcid":false,"given":"Javier","family":"Escudero","sequence":"additional","affiliation":[{"name":"Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1159\/000441447","article-title":"Clinical neurophysiological and automated EEG-based diagnosis of the Alzheimer\u2019s disease","volume":"74","author":"Bhat","year":"2015","journal-title":"Eur. 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