Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Brazilian Applied Science Review |
Texto Completo: | https://ojs.brazilianjournals.com.br/ojs/index.php/BASR/article/view/11034 |
Resumo: | The discrimination of the interictal and preictal states in epilepsy contributes to the construction of an efficient system of seizure prediction. Here, we performed the classification of the interictal and preictal states for EEG signals of the scalp. The energies of the levels obtained by the signal decomposition of the Wavelet Discrete Transform were used as features for classification. The kNN and SVM classifiers were used in the analysis of the individual EEG channels, which gave indications that the occipital lobe region channels are the most relevant to differentiate between the interictal and preictal states. Using these channels, the classification into two states achieved accuracy of 97.29%, sensitivity of 96.25% and specificity of 98.33%. In addition, the different frequency ranges obtained by Wavelet for the classification were analyzed, and it was observed that the range of 32 Hz to 128 Hz presented greater relevance in the task. |
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Brazilian Applied Science Review |
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Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticosEpilepsyElectroencephalogramWaveletPredictionPreictalInterictal.The discrimination of the interictal and preictal states in epilepsy contributes to the construction of an efficient system of seizure prediction. Here, we performed the classification of the interictal and preictal states for EEG signals of the scalp. The energies of the levels obtained by the signal decomposition of the Wavelet Discrete Transform were used as features for classification. The kNN and SVM classifiers were used in the analysis of the individual EEG channels, which gave indications that the occipital lobe region channels are the most relevant to differentiate between the interictal and preictal states. Using these channels, the classification into two states achieved accuracy of 97.29%, sensitivity of 96.25% and specificity of 98.33%. In addition, the different frequency ranges obtained by Wavelet for the classification were analyzed, and it was observed that the range of 32 Hz to 128 Hz presented greater relevance in the task.Brazilian Journals Publicações de Periódicos e Editora Ltda.2020-06-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://ojs.brazilianjournals.com.br/ojs/index.php/BASR/article/view/1103410.34115/basrv4n3-079Brazilian Applied Science Review; Vol. 4 No. 3 (2020); 1730-1747Brazilian Applied Science Review; v. 4 n. 3 (2020); 1730-17472595-36212595-362110.34115/basr.v4i3reponame:Brazilian Applied Science Reviewinstname:Brazilian Journals Publicações de Periódicos e Editora Ltdainstacron:FIEPporhttps://ojs.brazilianjournals.com.br/ojs/index.php/BASR/article/view/11034/9248Copyright (c) 2020 Brazilian Applied Science Reviewinfo:eu-repo/semantics/openAccessKill, Jade BarbosaCiarelli, Patrick MarquesCôco, Klaus FabianSouza, Mariane Lima de2020-06-29T18:19:05Zoai:ojs2.ojs.brazilianjournals.com.br:article/11034Revistahttps://www.brazilianjournals.com/index.php/BASRPRIhttps://ojs.brazilianjournals.com.br/ojs/index.php/BASR/oaibrazilianasr@yahoo.com || brazilianasr@yahoo.com2595-36212595-3621opendoar:2020-06-29T18:19:05Brazilian Applied Science Review - Brazilian Journals Publicações de Periódicos e Editora Ltdafalse |
dc.title.none.fl_str_mv |
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos |
title |
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos |
spellingShingle |
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos Kill, Jade Barbosa Epilepsy Electroencephalogram Wavelet Prediction Preictal Interictal. |
title_short |
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos |
title_full |
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos |
title_fullStr |
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos |
title_full_unstemmed |
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos |
title_sort |
Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos |
author |
Kill, Jade Barbosa |
author_facet |
Kill, Jade Barbosa Ciarelli, Patrick Marques Côco, Klaus Fabian Souza, Mariane Lima de |
author_role |
author |
author2 |
Ciarelli, Patrick Marques Côco, Klaus Fabian Souza, Mariane Lima de |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Kill, Jade Barbosa Ciarelli, Patrick Marques Côco, Klaus Fabian Souza, Mariane Lima de |
dc.subject.por.fl_str_mv |
Epilepsy Electroencephalogram Wavelet Prediction Preictal Interictal. |
topic |
Epilepsy Electroencephalogram Wavelet Prediction Preictal Interictal. |
description |
The discrimination of the interictal and preictal states in epilepsy contributes to the construction of an efficient system of seizure prediction. Here, we performed the classification of the interictal and preictal states for EEG signals of the scalp. The energies of the levels obtained by the signal decomposition of the Wavelet Discrete Transform were used as features for classification. The kNN and SVM classifiers were used in the analysis of the individual EEG channels, which gave indications that the occipital lobe region channels are the most relevant to differentiate between the interictal and preictal states. Using these channels, the classification into two states achieved accuracy of 97.29%, sensitivity of 96.25% and specificity of 98.33%. In addition, the different frequency ranges obtained by Wavelet for the classification were analyzed, and it was observed that the range of 32 Hz to 128 Hz presented greater relevance in the task. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-06-03 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://ojs.brazilianjournals.com.br/ojs/index.php/BASR/article/view/11034 10.34115/basrv4n3-079 |
url |
https://ojs.brazilianjournals.com.br/ojs/index.php/BASR/article/view/11034 |
identifier_str_mv |
10.34115/basrv4n3-079 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://ojs.brazilianjournals.com.br/ojs/index.php/BASR/article/view/11034/9248 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Brazilian Applied Science Review info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Brazilian Applied Science Review |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Journals Publicações de Periódicos e Editora Ltda. |
publisher.none.fl_str_mv |
Brazilian Journals Publicações de Periódicos e Editora Ltda. |
dc.source.none.fl_str_mv |
Brazilian Applied Science Review; Vol. 4 No. 3 (2020); 1730-1747 Brazilian Applied Science Review; v. 4 n. 3 (2020); 1730-1747 2595-3621 2595-3621 10.34115/basr.v4i3 reponame:Brazilian Applied Science Review instname:Brazilian Journals Publicações de Periódicos e Editora Ltda instacron:FIEP |
instname_str |
Brazilian Journals Publicações de Periódicos e Editora Ltda |
instacron_str |
FIEP |
institution |
FIEP |
reponame_str |
Brazilian Applied Science Review |
collection |
Brazilian Applied Science Review |
repository.name.fl_str_mv |
Brazilian Applied Science Review - Brazilian Journals Publicações de Periódicos e Editora Ltda |
repository.mail.fl_str_mv |
brazilianasr@yahoo.com || brazilianasr@yahoo.com |
_version_ |
1797240006758105088 |