Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/101172
DC FieldValueLanguage
dc.contributor.authorLopes, Fábio-
dc.contributor.authorLeal, Adriana-
dc.contributor.authorMedeiros, Julio-
dc.contributor.authorPinto, Mauro-
dc.contributor.authorDourado, António-
dc.contributor.authorDumpelmann, Matthias-
dc.contributor.authorTeixeira, César-
dc.date.accessioned2022-08-16T07:37:59Z-
dc.date.available2022-08-16T07:37:59Z-
dc.date.issued2021-
dc.identifier.issn2169-3536pt
dc.identifier.urihttps://hdl.handle.net/10316/101172-
dc.description.abstractScalp electroencephalogram (EEG) is a non-invasive measure of brain activity. It is widely used in several applications including cognitive tasks, sleep stage detection, and seizure prediction. When recorded over several hours, this signal is usually corrupted by noisy disturbances such as experimental errors, environmental interferences, and physiological artifacts. These may generate confounding factors and, therefore, lead to false results. Models able to minimise EEG artifacts are then necessary for improving further analysis and application. In this work, we developed an EEG artifact removal model based on deep convolutional neural networks. The proposed approach was applied on long-term EEGs, acquired from epileptic patients, available in the EPILEPSIAE database. The main goal of our work is to develop a model able to automatically and quickly remove artifacts from EEGs. To develop it, we used EEG segments, manually preprocessed by experts and named target EEG segments. Our approach was evaluated comparing denoised segments with the target segments. Furthermore, we compared our approach with other artifact removal models. Results show that the developed model was able to attenuate the in uence of artifacts, present in long-term EEG signals, in a similar way to that performed by experts. Additionally, results evidence that our approach performs better than other artifact removal models, combining a minor reconstruction error with a fast processing. Being a fully automatic and fast model that does not require reference artifact templates, turns it suitable, for example, for continuous preprocessing of long-term electroencephalogram for sleep staging or seizure prediction.pt
dc.language.isoengpt
dc.relationUID/CEC/00326/2020pt
dc.relationPTDC/EEI-EEE/5788/2020pt
dc.relationFCT/POCH/EU - Grant 2020.04537.BDpt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectArtifact removalpt
dc.subjectautomatic reconstructionpt
dc.subjectdeep convolutional neural networkspt
dc.subjectelectroencephalogrampt
dc.subjectpreprocessingpt
dc.titleAutomatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networkspt
dc.typearticle-
degois.publication.firstPage149955pt
degois.publication.lastPage149970pt
degois.publication.titleIEEE Accesspt
dc.peerreviewedyespt
dc.identifier.doi10.1109/ACCESS.2021.3125728pt
degois.publication.volume9pt
dc.date.embargo2021-01-01*
uc.date.periodoEmbargo0pt
item.languageiso639-1en-
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0003-2852-6285-
crisitem.author.orcid0000-0002-9359-4324-
crisitem.author.orcid0000-0002-5445-6893-
crisitem.author.orcid0000-0001-9396-1211-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
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This item is licensed under a Creative Commons License Creative Commons