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dc.contributor.authorPinho, F.por
dc.contributor.authorFerreira, Joãopor
dc.contributor.authorReis, Joanapor
dc.contributor.authorSousa, Nunopor
dc.contributor.authorCerqueira, João Josépor
dc.contributor.authorCorreia, J. H.por
dc.contributor.authorDias, N. S.por
dc.date.accessioned2019-11-08T16:14:09Z-
dc.date.issued2014-07-
dc.identifier.citationPinho, F., Ferreira, J., Reis, J., Sousa, N. J., Cerqueira, J. J., Correia, J. H., & Dias, N. S. (2014, June). Epileptic event detection algorithm for ambulatory monitoring platforms. In 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE.por
dc.identifier.isbn978-1-4799-2920-7-
dc.identifier.urihttps://hdl.handle.net/1822/61996-
dc.description.abstractDetecting epileptic electroencephalography (EEG) signals, both automatically and accurately, is significant in ambulatory long-term monitoring patients with epilepsy. In this study, it is presented a novel epileptic-like event detection algorithm based on a mixture of amplitude, frequency and spatial analysis with rule-based decision. In this work, EEG signals from 6 different subjects were searched for epileptic-like and normal data segments. The herein proposed algorithm detects putative epileptic EEG channels by comparing the RMS values of EEG activity with a hysteresis threshold, on a channel basis. The raw EEG signals are filtered with an artefact attenuation technique. The threshold is calculated on a reviewer-visually-selected baseline epoch, free of artefacts. Generalized epileptic activity detection is based on a spatial decision rule. Experimental results have shown detection rates as high as 95% with a false-negative rate as low as 1%. The algorithm seems to show a promising detection performance, even on artifact contaminated datasets. The proposed algorithm is intended to be used in real-time ambulatory monitoring of epileptic patients, with subject personalization, small size window analysis, good artefact immunity and no need for classifier trainingpor
dc.description.sponsorshipThis work is supported by FEDER funds through the Programa Operacional Fatores de Competitividade COMPETE and National Funds through FCT - Fundaao para a Cincia e Tecnologia with the reference Project: FCOMP01-0124-FEDER-022674.This work is also supported by ADI Project" DoIT Desenvolvimentoe Operacionalizaao da Investigaao de Translaao" (project no 13853, PPS4- MyHealth), funded by Fundo Europeu de Desenvolvimento Regional (FEDER) through the Programa Operacional Factores de Competitividade (POFC).por
dc.language.isoengpor
dc.publisherIEEEpor
dc.rightsclosedAccesspor
dc.subjectElectroencephalographypor
dc.subjectAlgorithm design and analysispor
dc.subjectEvent detectionpor
dc.subjectEpilepsypor
dc.subjectMonitoringpor
dc.subjectReal-time systemspor
dc.subjectClassification algorithmspor
dc.subjectroot mean squarepor
dc.subjectambulatorypor
dc.titleEpileptic event detection algorithm for ambulatory monitoring platformspor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/6860104por
oaire.citationStartPage449por
oaire.citationEndPage454por
oaire.citationConferencePlaceLisboa, Portugalpor
dc.identifier.doi10.1109/MeMeA.2014.6860104por
dc.date.embargo10000-01-01-
dc.identifier.eisbn978-1-4799-2921-4-
dc.subject.fosCiências Médicas::Medicina Básicapor
dc.subject.wosScience & Technologypor
sdum.conferencePublication2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA)por
Aparece nas coleções:ICVS - Artigos em livros de atas / Papers in proceedings

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