Adaptive Filtering for Epileptic Event Detection in the EEG
- Autores
- Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Purpose The development of online seizure detection techniques as well as prediction methods are very critical. Patient quality of life could improve signifcantly if the beginning of a seizure could be predicted or detected early. Methods This paper proposes a method to automatically detect epileptic seizures based on adaptive flters and signal averaging. The process was applied to 425 h of epileptic EEG records from CHB-MIT EEG database. The developed algorithm does not require any training since it is simple and involves low processing time. Therefore, it can be implemented in real time as well as ofine. Results Three thresholds were evaluated and calculated as 10, 20 and 30 times the median value of ST(n). The threshold of 20 showed the best relation between SEN and SPE. In this case, these indexes reached average values, across all the patients, of 90.3% and 73.7% respectively. Conclusions The proposed method has several strengths, for example: that no training is required due to the automatic adaptation to the threshold to each new EEG record. The algorithm could be implemented in real time. It is simple owing to its low processing time which makes it suitable for the analysis of long-term records and a large number of channels. The system could be implemented on electronic devices for warning purposes (of the seizure onset). It employs methods to process signals that were not used with epileptic seizure detection in EEG, such as in the case of adaptive predictive flters.
Fil: Garces Correa, Maria Agustina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Orosco, Lorena Liliana. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina - Materia
-
ADAPTIVE FILTER
EEG
EPILEPTIC SEIZURE
SIGNAL PROCESSING - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/147667
Ver los metadatos del registro completo
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Adaptive Filtering for Epileptic Event Detection in the EEGGarces Correa, Maria AgustinaOrosco, Lorena LilianaDiez, Pablo FedericoLaciar Leber, EricADAPTIVE FILTEREEGEPILEPTIC SEIZURESIGNAL PROCESSINGhttps://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2Purpose The development of online seizure detection techniques as well as prediction methods are very critical. Patient quality of life could improve signifcantly if the beginning of a seizure could be predicted or detected early. Methods This paper proposes a method to automatically detect epileptic seizures based on adaptive flters and signal averaging. The process was applied to 425 h of epileptic EEG records from CHB-MIT EEG database. The developed algorithm does not require any training since it is simple and involves low processing time. Therefore, it can be implemented in real time as well as ofine. Results Three thresholds were evaluated and calculated as 10, 20 and 30 times the median value of ST(n). The threshold of 20 showed the best relation between SEN and SPE. In this case, these indexes reached average values, across all the patients, of 90.3% and 73.7% respectively. Conclusions The proposed method has several strengths, for example: that no training is required due to the automatic adaptation to the threshold to each new EEG record. The algorithm could be implemented in real time. It is simple owing to its low processing time which makes it suitable for the analysis of long-term records and a large number of channels. The system could be implemented on electronic devices for warning purposes (of the seizure onset). It employs methods to process signals that were not used with epileptic seizure detection in EEG, such as in the case of adaptive predictive flters.Fil: Garces Correa, Maria Agustina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Orosco, Lorena Liliana. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaInstitute of Biomedical Engineering2019-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/147667Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric; Adaptive Filtering for Epileptic Event Detection in the EEG; Institute of Biomedical Engineering; Journal of Medical and Biological Engineering; 39; 6; 12-2019; 912-9181609-09852199-4757CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs40846-019-00467-winfo:eu-repo/semantics/altIdentifier/doi/10.1007/s40846-019-00467-winfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:42:38Zoai:ri.conicet.gov.ar:11336/147667instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:42:38.471CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Adaptive Filtering for Epileptic Event Detection in the EEG |
title |
Adaptive Filtering for Epileptic Event Detection in the EEG |
spellingShingle |
Adaptive Filtering for Epileptic Event Detection in the EEG Garces Correa, Maria Agustina ADAPTIVE FILTER EEG EPILEPTIC SEIZURE SIGNAL PROCESSING |
title_short |
Adaptive Filtering for Epileptic Event Detection in the EEG |
title_full |
Adaptive Filtering for Epileptic Event Detection in the EEG |
title_fullStr |
Adaptive Filtering for Epileptic Event Detection in the EEG |
title_full_unstemmed |
Adaptive Filtering for Epileptic Event Detection in the EEG |
title_sort |
Adaptive Filtering for Epileptic Event Detection in the EEG |
dc.creator.none.fl_str_mv |
Garces Correa, Maria Agustina Orosco, Lorena Liliana Diez, Pablo Federico Laciar Leber, Eric |
author |
Garces Correa, Maria Agustina |
author_facet |
Garces Correa, Maria Agustina Orosco, Lorena Liliana Diez, Pablo Federico Laciar Leber, Eric |
author_role |
author |
author2 |
Orosco, Lorena Liliana Diez, Pablo Federico Laciar Leber, Eric |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
ADAPTIVE FILTER EEG EPILEPTIC SEIZURE SIGNAL PROCESSING |
topic |
ADAPTIVE FILTER EEG EPILEPTIC SEIZURE SIGNAL PROCESSING |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.6 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
Purpose The development of online seizure detection techniques as well as prediction methods are very critical. Patient quality of life could improve signifcantly if the beginning of a seizure could be predicted or detected early. Methods This paper proposes a method to automatically detect epileptic seizures based on adaptive flters and signal averaging. The process was applied to 425 h of epileptic EEG records from CHB-MIT EEG database. The developed algorithm does not require any training since it is simple and involves low processing time. Therefore, it can be implemented in real time as well as ofine. Results Three thresholds were evaluated and calculated as 10, 20 and 30 times the median value of ST(n). The threshold of 20 showed the best relation between SEN and SPE. In this case, these indexes reached average values, across all the patients, of 90.3% and 73.7% respectively. Conclusions The proposed method has several strengths, for example: that no training is required due to the automatic adaptation to the threshold to each new EEG record. The algorithm could be implemented in real time. It is simple owing to its low processing time which makes it suitable for the analysis of long-term records and a large number of channels. The system could be implemented on electronic devices for warning purposes (of the seizure onset). It employs methods to process signals that were not used with epileptic seizure detection in EEG, such as in the case of adaptive predictive flters. Fil: Garces Correa, Maria Agustina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina Fil: Orosco, Lorena Liliana. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina Fil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina Fil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina |
description |
Purpose The development of online seizure detection techniques as well as prediction methods are very critical. Patient quality of life could improve signifcantly if the beginning of a seizure could be predicted or detected early. Methods This paper proposes a method to automatically detect epileptic seizures based on adaptive flters and signal averaging. The process was applied to 425 h of epileptic EEG records from CHB-MIT EEG database. The developed algorithm does not require any training since it is simple and involves low processing time. Therefore, it can be implemented in real time as well as ofine. Results Three thresholds were evaluated and calculated as 10, 20 and 30 times the median value of ST(n). The threshold of 20 showed the best relation between SEN and SPE. In this case, these indexes reached average values, across all the patients, of 90.3% and 73.7% respectively. Conclusions The proposed method has several strengths, for example: that no training is required due to the automatic adaptation to the threshold to each new EEG record. The algorithm could be implemented in real time. It is simple owing to its low processing time which makes it suitable for the analysis of long-term records and a large number of channels. The system could be implemented on electronic devices for warning purposes (of the seizure onset). It employs methods to process signals that were not used with epileptic seizure detection in EEG, such as in the case of adaptive predictive flters. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/147667 Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric; Adaptive Filtering for Epileptic Event Detection in the EEG; Institute of Biomedical Engineering; Journal of Medical and Biological Engineering; 39; 6; 12-2019; 912-918 1609-0985 2199-4757 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/147667 |
identifier_str_mv |
Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric; Adaptive Filtering for Epileptic Event Detection in the EEG; Institute of Biomedical Engineering; Journal of Medical and Biological Engineering; 39; 6; 12-2019; 912-918 1609-0985 2199-4757 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs40846-019-00467-w info:eu-repo/semantics/altIdentifier/doi/10.1007/s40846-019-00467-w |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Institute of Biomedical Engineering |
publisher.none.fl_str_mv |
Institute of Biomedical Engineering |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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