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https://hdl.handle.net/1822/61996
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Campo DC | Valor | Idioma |
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dc.contributor.author | Pinho, F. | por |
dc.contributor.author | Ferreira, João | por |
dc.contributor.author | Reis, Joana | por |
dc.contributor.author | Sousa, Nuno | por |
dc.contributor.author | Cerqueira, João José | por |
dc.contributor.author | Correia, J. H. | por |
dc.contributor.author | Dias, N. S. | por |
dc.date.accessioned | 2019-11-08T16:14:09Z | - |
dc.date.issued | 2014-07 | - |
dc.identifier.citation | Pinho, 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.isbn | 978-1-4799-2920-7 | - |
dc.identifier.uri | https://hdl.handle.net/1822/61996 | - |
dc.description.abstract | Detecting 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 training | por |
dc.description.sponsorship | This 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.iso | eng | por |
dc.publisher | IEEE | por |
dc.rights | closedAccess | por |
dc.subject | Electroencephalography | por |
dc.subject | Algorithm design and analysis | por |
dc.subject | Event detection | por |
dc.subject | Epilepsy | por |
dc.subject | Monitoring | por |
dc.subject | Real-time systems | por |
dc.subject | Classification algorithms | por |
dc.subject | root mean square | por |
dc.subject | ambulatory | por |
dc.title | Epileptic event detection algorithm for ambulatory monitoring platforms | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://ieeexplore.ieee.org/abstract/document/6860104 | por |
oaire.citationStartPage | 449 | por |
oaire.citationEndPage | 454 | por |
oaire.citationConferencePlace | Lisboa, Portugal | por |
dc.identifier.doi | 10.1109/MeMeA.2014.6860104 | por |
dc.date.embargo | 10000-01-01 | - |
dc.identifier.eisbn | 978-1-4799-2921-4 | - |
dc.subject.fos | Ciências Médicas::Medicina Básica | por |
dc.subject.wos | Science & Technology | por |
sdum.conferencePublication | 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA) | por |
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pinho2014.pdf Acesso restrito! | 1,66 MB | Adobe PDF | Ver/Abrir |