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Smart-data-driven system for alzheimer disease detection through electroencephalographic signals

2022Journal article Open access
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dc.contributor.authorAraújo, Teresa
dc.contributor.authorTeixeira, João Paulo
dc.contributor.authorRodrigues, Pedro Miguel
dc.date.accessioned2022-05-17T15:43:48Z
dc.date.available2022-05-17T15:43:48Z
dc.date.issued2022
dc.description.abstractAlzheimer’s Disease (AD) stands out as one of the main causes of dementia worldwide and it represents around 65% of all dementia cases, affecting mainly elderly people. AD is composed of three evolutionary stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). It is crucial to create a tool for assisting AD diagnosis in its early stages with the aim of halting the disease progression. Methods: The main purpose of this study is to develop a system with the ability of differentiate each disease stage by means of Electroencephalographic Signals (EEG). Thereby, an EEG nonlinear multi-band analysis by Wavelet Packet was performed enabling to extract several features from each study group. Classic Machine Learning (ML) and Deep Learning (DL) methods have been used for data classification per EEG channel. Results: The maximum accuracies obtained were 78.9% (Healthy controls (C) vs. MCI), 81.0% (C vs. ADM), 84.2% (C vs. ADA), 88.9% (MCI vs. ADM), 93.8% (MCI vs. ADA), 77.8% (ADM vs. ADA) and 56.8% (All vs. All). Conclusions: The proposed method outperforms previous studies with the same database by 2% in binary comparison MCI vs. ADM and central and parietal brain regions revealed abnormal activity as AD progresses.pt_PT
dc.description.sponsorshipThis research was funded by National Funds from FCT - Fundação para a Ciência e a Tecnologia through projects UIDB/50016/2020 and UIDB/05757/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAraújo, Teresa; Teixeira, João Paulo; Rodrigues, Pedro Miguel (2022). Smart-data-driven system for alzheimer disease detection through electroencephalographic signals. Bioengineering. ISSN 2306-5354. 9:4, p. 1-16pt_PT
dc.identifier.doi10.3390/bioengineering9040141pt_PT
dc.identifier.issn2306-5354
dc.identifier.urihttp://hdl.handle.net/10198/25475
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationCentre of Biotechnology and Fine Chemistry
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAlzheimer diseasept_PT
dc.subjectNonlinear multi-band analysispt_PT
dc.subjectElectroencephalographic signalspt_PT
dc.subjectClassic machine learningpt_PT
dc.subjectDeep learningpt_PT
dc.subjectWavelet packetpt_PT
dc.subjectClassificationpt_PT
dc.titleSmart-data-driven system for alzheimer disease detection through electroencephalographic signalspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre of Biotechnology and Fine Chemistry
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50016%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.issue4pt_PT
oaire.citation.startPage141pt_PT
oaire.citation.titleBioengineeringpt_PT
oaire.citation.volume9pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
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
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isProjectOfPublication00845db3-ea08-46c8-8653-e7ec38f8c69d
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