Towards Automated EEG-Based Alzheimer's Disease Diagnosis Using Relevance Vector Machines

Página do item simplificado
dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP
dc.contributor.authorCASSANI, Raymundo
dc.contributor.authorFALK, Tiago H.
dc.contributor.authorFRAGA, Francisco J.
dc.contributor.authorKANDA, Paulo A.
dc.contributor.authorANGHINAH, Renato
dc.date.accessioned2023-07-18T14:19:29Z
dc.date.available2023-07-18T14:19:29Z
dc.date.issued2014
dc.description.abstractExisting electroencephalography (EEG) based Alzheimer's disease (AD) diagnostic systems typically rely on experts to visually inspect and segment the collected signals into artefact-free epochs and on support vector machine (SVM) based classifiers. The manual selection process, however, introduces biases and errors into the diagnostic procedure, renders it ""semi-automated,"" and makes the procedure costly and labour-intensive. In this paper, we overcome these limitations by proposing the use of an automated artefact removal (AAR) algorithm to remove artefacts from the EEG signal without the need for human intervention. We investigate the effects of the so-called wavelet-enhanced independent component analysis (wICA) AAR on three classes of EEG features, namely spectral power, coherence, and amplitude modulation, and ultimately, on diagnostic accuracy, specificity and sensitivity. Furthermore, we propose to replace the binary SVM classifier with a soft-decision relevance vector machine (RVM) classifier. Experimental results show the proposed RVM-based system outperforming the SVM trained on features extracted from both manually-selected and wICA-processed epochs. Moreover, the class membership information output by the RVM is shown to provide clinicians with a richer pool of information to assist with AD assessment.eng
dc.description.conferencedateMAY 26-28, 2014
dc.description.conferencelocalSalvador, BRAZIL
dc.description.conferencename5th ISSNIP-IEEE Biosignals and Biorobotics Conference - Biosignals and Robotics for Better and Safer Living (BRC)
dc.description.indexWoS
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC)
dc.description.sponsorshipINRS-EMT
dc.description.sponsorshipFoundation for Research Support of the State of Sao Paulo (FAPESP), Brazil
dc.identifier.citation5TH ISSNIP-IEEE BIOSIGNALS AND BIOROBOTICS CONFERENCE (2014): BIOSIGNALS AND ROBOTICS FOR BETTER AND SAFER LIVING, p.112-117, 2014
dc.identifier.issn2326-7771
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/54501
dc.language.isoeng
dc.publisherIEEEeng
dc.relation.ispartof5th Issnip-Ieee Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living
dc.relation.ispartofseriesISSNIP Biosignals and Biorobotics Conference
dc.rightsrestrictedAccesseng
dc.rights.holderCopyright IEEEeng
dc.subjectAlzheimer's diseaseeng
dc.subjectelectroencephalographyeng
dc.subjectsupport vector machine (SVM)eng
dc.subjectrelevance vector machine (RVM)eng
dc.subjectwICAeng
dc.subject.othercoherenceeng
dc.subject.otherartifactseng
dc.subject.otherdementiaeng
dc.subject.wosMathematical & Computational Biologyeng
dc.subject.wosRoboticseng
dc.titleTowards Automated EEG-Based Alzheimer's Disease Diagnosis Using Relevance Vector Machineseng
dc.typeconferenceObjecteng
dc.type.categoryproceedings papereng
dc.type.versionpublishedVersioneng
dspace.entity.typePublication
hcfmusp.affiliation.countryCanadá
hcfmusp.affiliation.countryisoca
hcfmusp.author.externalCASSANI, Raymundo:Univ Quebec, Inst Natl Rech Sci INRS EMT, Ste Foy, PQ G1V 2M3, Canada
hcfmusp.author.externalFALK, Tiago H.:Univ Quebec, Inst Natl Rech Sci INRS EMT, Ste Foy, PQ G1V 2M3, Canada
hcfmusp.author.externalFRAGA, Francisco J.:Univ Fed, Modelling & Appl Social Sci Ctr, Para, Brazil
hcfmusp.contributor.author-fmusphcPAULO AFONSO MEDEIROS KANDA
hcfmusp.contributor.author-fmusphcRENATO ANGHINAH
hcfmusp.description.beginpage112
hcfmusp.description.endpage117
hcfmusp.origemWOS
hcfmusp.origem.wosWOS:000345908100023
hcfmusp.publisher.cityNEW YORKeng
hcfmusp.publisher.countryUSAeng
hcfmusp.relation.referenceAdeli H, 2005, CLIN EEG NEUROSCI, V36, P131, DOI 10.1177/155005940503600303eng
hcfmusp.relation.referenceBabiloni C, 2010, J ALZHEIMERS DIS, V22, P1047, DOI 10.3233/JAD-2010-100798eng
hcfmusp.relation.referenceBEDROSIAN E, 1963, P IEEE, V51, P868, DOI 10.1109/PROC.1963.2308eng
hcfmusp.relation.referenceBESTHORN C, 1994, ELECTROEN CLIN NEURO, V90, P242, DOI 10.1016/0013-4694(94)90095-7eng
hcfmusp.relation.referenceBRENNER RP, 1986, ELECTROEN CLIN NEURO, V64, P483, DOI 10.1016/0013-4694(86)90184-7eng
hcfmusp.relation.referenceBrucki SMD, 2003, ARQ NEURO-PSIQUIAT, V61, P777, DOI 10.1590/S0004-282X2003000500014eng
hcfmusp.relation.referenceCassani R., FRONTIERS A IN PRESSeng
hcfmusp.relation.referenceCastellanos NP, 2006, J NEUROSCI METH, V158, P300, DOI 10.1016/j.jneumeth.2006.05.033eng
hcfmusp.relation.referenceCOBEN LA, 1983, ELECTROEN CLIN NEURO, V55, P372, DOI 10.1016/0013-4694(83)90124-4eng
hcfmusp.relation.referenceCORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411eng
hcfmusp.relation.referenceDaly I., 2013, CLIN EEG NEUROSCI, P1eng
hcfmusp.relation.referenceDauwels J, 2010, NEUROIMAGE, V49, P668, DOI 10.1016/j.neuroimage.2009.06.056eng
hcfmusp.relation.referenceDauwels J., 2011, INT J ALZHEIMERS DIS, V2011eng
hcfmusp.relation.referenceDe Clercq W, 2006, IEEE T BIO-MED ENG, V53, P2583, DOI 10.1109/TBME.2006.879459eng
hcfmusp.relation.referenceFalk TH, 2012, EURASIP J ADV SIG PR, DOI 10.1186/1687-6180-2012-192eng
hcfmusp.relation.referenceFraga FJ, 2013, INT CONF ACOUST SPEE, P1207, DOI 10.1109/ICASSP.2013.6637842eng
hcfmusp.relation.referenceFraga FJ, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0072240eng
hcfmusp.relation.referenceGIAQUINTO S, 1986, ELECTROEN CLIN NEURO, V63, P540, DOI 10.1016/0013-4694(86)90141-0eng
hcfmusp.relation.referenceGomez-Herrero G, 2006, 2006 7TH NORDIC SIGNAL PROCESSING SYMPOSIUM, P130eng
hcfmusp.relation.referenceHurd MD, 2013, NEW ENGL J MED, V368, P1326, DOI [10.1056/NEJMsa1204629, 10.1056/NEJMc1305541]eng
hcfmusp.relation.referenceJeong JS, 2004, CLIN NEUROPHYSIOL, V115, P1490, DOI 10.1016/j.clinph.2004.01.001eng
hcfmusp.relation.referenceKanda P., 2013, CLIN EEG NEUROSCIENCeng
hcfmusp.relation.referenceKrishnaveni V, 2006, J NEURAL ENG, V3, P338, DOI 10.1088/1741-2560/3/4/011eng
hcfmusp.relation.referenceLehmann C, 2007, J NEUROSCI METH, V161, P342, DOI 10.1016/j.jneumeth.2006.10.023eng
hcfmusp.relation.referenceLocatelli T, 1998, ELECTROEN CLIN NEURO, V106, P229, DOI 10.1016/S0013-4694(97)00129-6eng
hcfmusp.relation.referenceMakarov V. A., 2012, WAVELET ENHANCED INDeng
hcfmusp.relation.referenceMCKHANN G, 1984, NEUROLOGY, V34, P939, DOI 10.1212/WNL.34.7.939eng
hcfmusp.relation.referenceNunez P. L., 2006, ELECT FIELDS BRAIN N, DOI 10.1093/acprof:oso/9780195050387.001.0001eng
hcfmusp.relation.referenceSarazin M, 2012, NEUROIMAG CLIN N AM, V22, P23, DOI 10.1016/j.nic.2011.11.004eng
hcfmusp.relation.referenceSLOAN EP, 1994, ELECTROEN CLIN NEURO, V91, P163, DOI 10.1016/0013-4694(94)90066-3eng
hcfmusp.relation.referenceSornmo L., 2005, BIOELECTRICAL SIGNAL, P453eng
hcfmusp.relation.referenceThies W, 2013, ALZHEIMERS DEMENT, V9, P208, DOI 10.1016/j.jalz.2013.02.003eng
hcfmusp.relation.referenceTipping ME, 2001, J MACH LEARN RES, V1, P211, DOI 10.1162/15324430152748236eng
hcfmusp.relation.referenceTrambaiolli LR, 2011, IEEE ENG MED BIO, P3828, DOI 10.1109/IEMBS.2011.6090951eng
hcfmusp.relation.referenceTrambaiolli LR, 2011, CLIN EEG NEUROSCI, V42, P160, DOI 10.1177/155005941104200304eng
hcfmusp.relation.referenceWHO, 2012, GLOB STRAT DENG PREVeng
relation.isAuthorOfPublication5b3b65fa-168f-4c11-a611-bd9803935c30
relation.isAuthorOfPublicationcc1d65de-fd93-4892-831d-ba7477128ebd
relation.isAuthorOfPublication.latestForDiscovery5b3b65fa-168f-4c11-a611-bd9803935c30
Arquivos
Coleções
Comunicações em Eventos - HC/Outros
Comunicações em Eventos - LIM/45