Automatic Identification of Interictal Epileptiform Discharges with the Use of Complex Networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorTomanik, Gustavo H. [UNESP]-
Autor(es): dc.creatorBetting, Luiz E. [UNESP]-
Autor(es): dc.creatorCampanharo, Andriana S. L. O. [UNESP]-
Autor(es): dc.creatorRojas, I-
Autor(es): dc.creatorJoya, G.-
Autor(es): dc.creatorCatala, A.-
Data de aceite: dc.date.accessioned2022-02-22T00:08:54Z-
Data de disponibilização: dc.date.available2022-02-22T00:08:54Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2019-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-030-20521-8_13-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/196249-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/196249-
Descrição: dc.descriptionThe identification of Interictal Epileptiform Discharges (IEDs), which are characterized by spikes and waves in electroencephalographic (EEG) data, is highly beneficial to the automated detection and prediction of epileptic seizures. In this paper, a novel single-step approach for IEDs detection based on the complex network theory is proposed. Our main goal is to illustrate how the differences in dynamics in EEG signals from patients diagnosed with idiopathic generalized epilepsy are reflected in the topology of the corresponding networks. Based on various network metrics, namely, the strongly connected component, the shortest path length and the mean jump length, our results show that this method enables the discrimination between IEDs and free IEDs events. A decision about the presence of epileptiform activity in EEG signals was made based on the confusion matrix. An overall detection accuracy of 98.2% was achieved.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Inst Biosci, Dept Biostat, Botucatu, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Botucatu Med Sch, Inst Biosci, Dept Neurol Psychol & Psychiat, Botucatu, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Inst Biosci, Dept Biostat, Botucatu, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Botucatu Med Sch, Inst Biosci, Dept Neurol Psychol & Psychiat, Botucatu, SP, Brazil-
Descrição: dc.descriptionFAPESP: 2015/222935-
Descrição: dc.descriptionFAPESP: 2017/09216-7-
Descrição: dc.descriptionFAPESP: 2018/02014-2-
Descrição: dc.descriptionFAPESP: 2016/17914-3-
Descrição: dc.descriptionFAPESP: 2018/25358-9-
Descrição: dc.descriptionCAPES: 001-
Formato: dc.format152-161-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationAdvances In Computational Intelligence, Iwann 2019, Pt I-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectElectroencephalographic time series-
Palavras-chave: dc.subjectInterictal Epileptiform Discharges-
Palavras-chave: dc.subjectComplex networks-
Palavras-chave: dc.subjectNetwork measures-
Título: dc.titleAutomatic Identification of Interictal Epileptiform Discharges with the Use of Complex Networks-
Aparece nas coleções:Repositório Institucional - Unesp

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