Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/105872 https://doi.org/10.1155/2020/4825767 |
Resumo: | 'e Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. 'e performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. 'e capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. 'is contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions. |
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Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy'e Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. 'e performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. 'e capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. 'is contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.Hindawi2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/105872http://hdl.handle.net/10316/105872https://doi.org/10.1155/2020/4825767eng1076-27871099-0526Pisano, FabioSias, GiulianaFanni, AlessandraCannas, BarbaraDourado, AntónioPisano, BarbaraTeixeira, Cesar A.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T21:32:09Zoai:estudogeral.uc.pt:10316/105872Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:22:22.104512Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy |
title |
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy |
spellingShingle |
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy Pisano, Fabio |
title_short |
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy |
title_full |
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy |
title_fullStr |
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy |
title_full_unstemmed |
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy |
title_sort |
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy |
author |
Pisano, Fabio |
author_facet |
Pisano, Fabio Sias, Giuliana Fanni, Alessandra Cannas, Barbara Dourado, António Pisano, Barbara Teixeira, Cesar A. |
author_role |
author |
author2 |
Sias, Giuliana Fanni, Alessandra Cannas, Barbara Dourado, António Pisano, Barbara Teixeira, Cesar A. |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Pisano, Fabio Sias, Giuliana Fanni, Alessandra Cannas, Barbara Dourado, António Pisano, Barbara Teixeira, Cesar A. |
description |
'e Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. 'e performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. 'e capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. 'is contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/105872 http://hdl.handle.net/10316/105872 https://doi.org/10.1155/2020/4825767 |
url |
http://hdl.handle.net/10316/105872 https://doi.org/10.1155/2020/4825767 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1076-2787 1099-0526 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Hindawi |
publisher.none.fl_str_mv |
Hindawi |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134112921944064 |