{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:17:13Z","timestamp":1761401833221,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,17]],"date-time":"2017-03-17T00:00:00Z","timestamp":1489708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004955","name":"\u00d6sterreichische Forschungsf\u00f6rderungsgesellschaft","doi-asserted-by":"publisher","award":["827462"],"award-info":[{"award-number":["827462"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Analysis of nonlinear quantitative EEG (qEEG) markers describing complexity of signal in relation to severity of Alzheimer\u2019s disease (AD) was the focal point of this study. In this study, 79 patients diagnosed with probable AD were recruited from the multi-centric Prospective Dementia Database Austria (PRODEM). EEG recordings were done with the subjects seated in an upright position in a resting state with their eyes closed. Models of linear regressions explaining disease severity, expressed in Mini Mental State Examination (MMSE) scores, were analyzed by the nonlinear qEEG markers of auto mutual information (AMI), Shannon entropy (ShE), Tsallis entropy (TsE), multiscale entropy (MsE), or spectral entropy (SpE), with age, duration of illness, and years of education as co-predictors. Linear regression models with AMI were significant for all electrode sites and clusters, where      R 2      is 0.46 at the electrode site C3, 0.43 at Cz, F3, and central region, and 0.42 at the left region. MsE also had significant models at C3 with      R 2  &gt; 0.40     at scales     \u03c4 = 5     and     \u03c4 = 6    . ShE and TsE also have significant models at T7 and F7 with      R 2  &gt; 0.30    . Reductions in complexity, calculated by AMI, SpE, and MsE, were observed as the MMSE score decreased.<\/jats:p>","DOI":"10.3390\/e19030130","type":"journal-article","created":{"date-parts":[[2017,3,17]],"date-time":"2017-03-17T11:22:56Z","timestamp":1489749776000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Quantitative EEG Markers of Entropy and Auto Mutual Information in Relation to MMSE Scores of Probable Alzheimer\u2019s Disease Patients"],"prefix":"10.3390","volume":"19","author":[{"given":"Carmina","family":"Coronel","sequence":"first","affiliation":[{"name":"AIT Austrian Institute of Technology GmbH, 1220 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2194-7418","authenticated-orcid":false,"given":"Heinrich","family":"Garn","sequence":"additional","affiliation":[{"name":"AIT Austrian Institute of Technology GmbH, 1220 Vienna, Austria"}]},{"given":"Markus","family":"Waser","sequence":"additional","affiliation":[{"name":"AIT Austrian Institute of Technology GmbH, 1220 Vienna, Austria"}]},{"given":"Manfred","family":"Deistler","sequence":"additional","affiliation":[{"name":"Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, 1040 Vienna, Austria"}]},{"given":"Thomas","family":"Benke","sequence":"additional","affiliation":[{"name":"Clinic of Neurology, Medical University of Innsbruck, 6020 Innsbruck, Austria"}]},{"given":"Peter","family":"Dal-Bianco","sequence":"additional","affiliation":[{"name":"Department of Clinical Neurology, Medical University of Vienna, 1090 Vienna, Austria"}]},{"given":"Gerhard","family":"Ransmayr","sequence":"additional","affiliation":[{"name":"Department of Neurology 2, MedCampus III of the Kepler University Hospital, 4021 Linz, Austria"}]},{"given":"Stephan","family":"Seiler","sequence":"additional","affiliation":[{"name":"Department of Neurology, Clinical Division of Neurogeriatrics , Medical University of Graz, 8036 Graz, Austria"}]},{"given":"Dieter","family":"Grossegger","sequence":"additional","affiliation":[{"name":"Dr. Grossegger &amp; Drbal GmbH, Ruthgasse 19, 1190 Vienna, Austria"}]},{"given":"Reinhold","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Department of Neurology, Clinical Division of Neurogeriatrics , Medical University of Graz, 8036 Graz, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2016). 2016 Alzheimer\u2019s disease Facts and Figures. Alzheimer Dement., 12, 459\u2013509.","DOI":"10.1016\/j.jalz.2016.03.001"},{"key":"ref_2","unstructured":"Prince, M., Wimo, A., Guerchet, M., Ali, G.C., Wu, Y.T., and Prina, M. (2015). World Alzheimer Report 2015, Alzheimer\u2019s Disease International (ADI)."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3233\/JAD-2006-9S305","article-title":"Vulnerability of cortical neurons to Alzheimer\u2019s and Parkinson\u2019s diseases","volume":"9","author":"Braak","year":"2006","journal-title":"J. Alzheimer Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1212\/WNL.34.7.939","article-title":"Clinical Diagnosis of Alzheimer\u2019s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer\u2019s Disease","volume":"34","author":"McKhann","year":"1984","journal-title":"Neurology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"a006213","DOI":"10.1101\/cshperspect.a006213","article-title":"Brain Imaging in Alzheimer Disease","volume":"2","author":"Johnson","year":"2012","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S137","DOI":"10.1016\/j.jalz.2007.10.008","article-title":"Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer\u2019s diseases","volume":"4","author":"Jackson","year":"2008","journal-title":"Alzheimer Dement."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.clinph.2004.01.001","article-title":"EEG dynamics in patients with Alzheimer\u2019s disease","volume":"115","author":"Jeong","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"487","DOI":"10.2174\/156720510792231720","article-title":"Diagnosis of Alzheimer\u2019s Diseases from EEG Signals: Where Are We Standing?","volume":"7","author":"Dauwels","year":"2010","journal-title":"Curr. Alzheimer Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/S1388-2457(01)00513-2","article-title":"Mutual information analysis of the EEG in patients with Alzheimer\u2019s disease","volume":"112","author":"Jeong","year":"2001","journal-title":"Clin. Neurophysiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.pnpbp.2013.07.022","article-title":"Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer\u2019s disease","volume":"47","author":"Yang","year":"2013","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1088\/0967-3334\/27\/11\/004","article-title":"Analysis of electroencephalograms in Alzheimer\u2019s disease patients with multiscale entropy","volume":"27","author":"Escudero","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1016\/j.clinph.2010.03.025","article-title":"Assessment of EEG dynamical complexity in Alzheimer\u2019s disease using multiscale entropy","volume":"121","author":"Mizuno","year":"2010","journal-title":"Clin. Neurophysiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.cmpb.2014.01.019","article-title":"Spectral and Complexity Analysis of Scalp EEG Characteristics for Mild Cognitive Impairment and Early Alzheimer\u2019s Disease","volume":"114","author":"Mcbride","year":"2014","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Garn, H., Waser, M., Deistler, M., Benke, T., Dal-Bianco, P., Ransmayr, G., Schmidt, H., Sanin, G., Santer, P., and Caravias, G. (2014, January 1\u20134). Electroencephalographic Complexity Markers Explain Neuropsychological Test Scores in Alzheimer\u2019s Disease. Proceedings of the 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, Valencia, Spain.","DOI":"10.1109\/BHI.2014.6864411"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.clinph.2014.07.005","article-title":"Quantitative EEG markers relate to Alzheimer\u2019s diseases severity in the Prospective Dementia Registry Austria (PRODEM)","volume":"126","author":"Garn","year":"2015","journal-title":"Clin. Neurophysiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"Mini-Mental State. A Practical Method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1016\/S1474-4422(07)70178-3","article-title":"Research Criteria for the diagnosis of Alzheimer\u2019s disease: Revising the NINCDS-ADRDA criteria","volume":"6","author":"Dubois","year":"2007","journal-title":"Lancet Neurol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Waser, M., and Garn, H. (2013, January 3\u20137). Removing cardiac interference from the electroencephalogram using a modified Pan-Tompkins algorithm and linear regression. Engineering in Medicine and Biology Society (EMBC). Proceedings of the 2013 35th Annual International Conference of the IEEE, Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6609929"},{"key":"ref_19","unstructured":"Draper, N.R., and Smith, H. (1988). Applied Regression Analysis, John Wiley and Sons Inc.. [3rd ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1016\/j.sigpro.2005.07.010","article-title":"Nonstationary nature of the brain activitiy as revealed by EEG\/MEG: Methodological, practical and conceptual challenges","volume":"85","author":"Kaplan","year":"2005","journal-title":"Signal Process."},{"key":"ref_21","first-page":"427","article-title":"Distribution of the estimators for autoregressive time series with a unit root","volume":"74","author":"Dickey","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1109\/10.709563","article-title":"Stochastic complexity markers for physiological signal analysis","volume":"45","author":"Rezek","year":"1998","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"021906","DOI":"10.1103\/PhysRevE.71.021906","article-title":"Multiscale entropy analysis of biological signals","volume":"71","author":"Costa","year":"2005","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5865","DOI":"10.1016\/j.physa.2013.07.075","article-title":"Modified multiscale entropy for short-term time series analysis","volume":"392","author":"Wu","year":"2013","journal-title":"Phys. A Stat. Methods Appl."},{"key":"ref_25","unstructured":"Cover, T.M., and Thomas, J.A. (1991). Elements of Information Theory, Wiley."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1111\/j.1532-5415.1992.tb01992.x","article-title":"The mini-mental state examination: A comprehensive review","volume":"40","author":"Tombaugh","year":"1992","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_27","first-page":"65","article-title":"A simple sequentially rejective multiple test procedure","volume":"6","author":"Holm","year":"1979","journal-title":"Scand. J. Stat."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1016\/j.neurobiolaging.2009.11.008","article-title":"Neurostructural predictors of Alzheimer\u2019s disease: A meta-analysis of VBM studies","volume":"32","author":"Ferreira","year":"2011","journal-title":"Neurobiol. Aging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1088\/0967-3334\/27\/3\/003","article-title":"Entropy analysis of the EEG background activity in Alzheimer\u2019s disease patients","volume":"27","author":"Hornero","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3109\/1354750X.2011.635806","article-title":"A biomarker for severity of Alzheimer\u2019s disease: 1H-NMR resonances in cerebrosprinal fluid correlate with performance in mini-mental-state-exam","volume":"17","author":"Kork","year":"2012","journal-title":"Biomarkers"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Benedictus, M.R., Leeuwis, A.E., Binnewijzend, M.A.A., Kuijer, J.P.A., Scheltens, P., Barkhof, F., van der Flier, W.M., and Prins, N.D. (2016). Lower cerebral blood flow is associated with faster cognitive decline in Alzheimer\u2019s disease. Eur. 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