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Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection

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dc.contributor.authorRodrigues, Pedro Miguel
dc.contributor.authorFreitas, Diamantino Silva
dc.contributor.authorTeixeira, João Paulo
dc.contributor.authorAlves, Dílio
dc.contributor.authorGarrett, Carolina
dc.date.accessioned2018-05-03T09:53:14Z
dc.date.available2018-05-03T09:53:14Z
dc.date.issued2018
dc.description.abstractThe World’s health systems are now facing a global problem known as Alzheimer’s disease (AD) that mainly affects the elderly. The goal of this work is to perform a classification methodology skilled with Artificial Neural Networks (ANN) to improve the discrimination accuracy amongst patients at AD different stages comparatively to the state-of-art. For that, several study features that characterized the Electroencephalogram (EEG) signals “slow-down” were extracted and presented to the ANN entries in order to classify the dataset. The classification results achieved in the present work are promising concerning AD early diagnosis and they show that EEG can be a good tool for AD detection (Controls (C) vs AD: accuracy 95%; C vs Mild-cognitive Impairment (MCI): accuracy 77%; MCI vs AD: accuracy 83%; All vs All: accuracy 90%).pt_PT
dc.description.sponsorshipThe authors gratefully acknowledge the opportunity to publish this work to the International Conference on Health and Social Care Information Systems and Technologies Co-chairs and to Neurological Unity of “Hospital de São João” for supplying the EEG signals used in this study.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationRodrigues, Pedro Miguel; Freitas, Diamantino Rui; Teixeira, João Paulo; Alves, Dílio; Garrett, Carolina (2018). Electroencephalogram signal analysis in Alzheimer's disease early detection. International Journal of Reliable and Quality E-Healthcare. ISSN 2160-9551. 7:1, p. 40-59pt_PT
dc.identifier.doi10.4018/IJRQEH.2018010104pt_PT
dc.identifier.issn2160-9551
dc.identifier.urihttp://hdl.handle.net/10198/17579
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIGI Globalpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAlzheimer’s diseasept_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectClassificationpt_PT
dc.subjectEarly diagnosispt_PT
dc.subjectElectroencephalogram signalspt_PT
dc.subjectFeatures, stagespt_PT
dc.titleElectroencephalogram Signal Analysis in Alzheimer's Disease Early Detectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage59pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage40pt_PT
oaire.citation.titleInternational Journal of Reliable and Quality E-Healthcarept_PT
oaire.citation.volume7pt_PT
person.familyNameTeixeira
person.givenNameJoão Paulo
person.identifier663194
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscovery33f4af65-7ddf-46f0-8b44-a7470a8ba2bf

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