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dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP-
dc.contributor.authorFALK, Tiago H.-
dc.contributor.authorFRAGA, Francisco J.-
dc.contributor.authorTRAMBAIOLLI, Lucas-
dc.contributor.authorANGHINAH, Renato-
dc.date.accessioned2013-07-30T15:32:31Z-
dc.date.available2013-07-30T15:32:31Z-
dc.date.issued2012-
dc.identifier.citationEURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, article ID 192, 9p, 2012-
dc.identifier.issn1687-6180-
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/1183-
dc.description.abstractRecent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.-
dc.language.isoeng-
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AG-
dc.relation.ispartofEurasip Journal on Advances in Signal Processing-
dc.rightsrestrictedAccess-
dc.subjectAlzheimer's disease-
dc.subjectModulation spectrum-
dc.subjectElectroencephalogram-
dc.subjectSpectral peak-
dc.subjectSupport vector machine-
dc.subject.othermild cognitive impairment-
dc.subject.otherblood-flow-
dc.subject.otherdementia-
dc.subject.otherfrequency-
dc.subject.othercoherence-
dc.subject.otheraccuracy-
dc.subject.othermemory-
dc.subject.otherstate-
dc.subject.otherrisk-
dc.titleEEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease-
dc.typearticle-
dc.rights.holderCopyright SPRINGER INTERNATIONAL PUBLISHING AG-
dc.identifier.doi10.1186/1687-6180-2012-192-
dc.subject.wosEngineering, Electrical & Electronic-
dc.type.categoryoriginal article-
dc.type.versionpublishedVersion-
hcfmusp.author.externalFALK, Tiago H.:Univ Quebec, Inst Natl Rech Sci Energy Mat & Telecommun, Montreal, PQ H3C 3P8, Canada-
hcfmusp.author.externalFRAGA, Francisco J.:Univ Fed ABC, Engn Modeling & Appl Social Sci Ctr, Santo Andre, Brazil-
hcfmusp.author.externalTRAMBAIOLLI, Lucas:Univ Fed ABC, Math Comp & Cognit Ctr, Santo Andre, Brazil-
hcfmusp.description.articlenumber192-
hcfmusp.description.volume2012-
hcfmusp.origemWOS-
hcfmusp.origem.id2-s2.0-84877259784-
hcfmusp.origem.idWOS:000314418500001-
hcfmusp.publisher.cityCHAM-
hcfmusp.publisher.countrySWITZERLAND-
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dc.description.indexWoS-
hcfmusp.citation.scopus41-
hcfmusp.scopus.lastupdate2022-05-06-
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