Automatic detection of drowsiness in EEG records based on multimodal analysis

Autores
Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Laciar Leber, Eric
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Drowsiness is one of the main causal factors in many traffic accidents due to the clear decline in the attention and recognition of danger drivers, diminishing vehicle-handling abilities. The aim of this research is to develop an automatic method to detect the drowsiness stage in EEG records using time, spectral and wavelet analysis. A total of 19 features were computed from only one EEG channel to differentiate the alertness and drowsiness stages. After a selection process based on lambda of Wilks criterion, 7 parameters were chosen to feed a Neural Network classifier. Eighteen EEG records were analyzed. The method gets 87.4% and 83.6% of alertness and drowsiness correct detections rates, respectively. The results obtained indicate that the parameters can differentiate both stages. The features are easy to calculate and can be obtained in real time. Those variables could be used in an automatic drowsiness detection system in vehicles, thereby decreasing the rate of accidents caused by sleepiness of the driver.
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; 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; 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; Argentina
Materia
Drowsiness
Alert
Eeg
Wavelet
Neural Networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/32750
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Automatic detection of drowsiness in EEG records based on multimodal analysisGarces Correa, Maria AgustinaOrosco, Lorena LilianaLaciar Leber, EricDrowsinessAlertEegWaveletNeural Networkshttps://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2https://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Drowsiness is one of the main causal factors in many traffic accidents due to the clear decline in the attention and recognition of danger drivers, diminishing vehicle-handling abilities. The aim of this research is to develop an automatic method to detect the drowsiness stage in EEG records using time, spectral and wavelet analysis. A total of 19 features were computed from only one EEG channel to differentiate the alertness and drowsiness stages. After a selection process based on lambda of Wilks criterion, 7 parameters were chosen to feed a Neural Network classifier. Eighteen EEG records were analyzed. The method gets 87.4% and 83.6% of alertness and drowsiness correct detections rates, respectively. The results obtained indicate that the parameters can differentiate both stages. The features are easy to calculate and can be obtained in real time. Those variables could be used in an automatic drowsiness detection system in vehicles, thereby decreasing the rate of accidents caused by sleepiness of the driver.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; 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; 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; ArgentinaElsevier2013-08info: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/11336/32750Orosco, Lorena Liliana; Garces Correa, Maria Agustina; Laciar Leber, Eric; Automatic detection of drowsiness in EEG records based on multimodal analysis; Elsevier; Medical Engineering & Physics; 36; 2; 8-2013; 244-2491350-4533CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1350453313001690info:eu-repo/semantics/altIdentifier/doi/10.1016/j.medengphy.2013.07.011info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:42:41Zoai:ri.conicet.gov.ar:11336/32750instacron: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 10:42:41.59CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic detection of drowsiness in EEG records based on multimodal analysis
title Automatic detection of drowsiness in EEG records based on multimodal analysis
spellingShingle Automatic detection of drowsiness in EEG records based on multimodal analysis
Garces Correa, Maria Agustina
Drowsiness
Alert
Eeg
Wavelet
Neural Networks
title_short Automatic detection of drowsiness in EEG records based on multimodal analysis
title_full Automatic detection of drowsiness in EEG records based on multimodal analysis
title_fullStr Automatic detection of drowsiness in EEG records based on multimodal analysis
title_full_unstemmed Automatic detection of drowsiness in EEG records based on multimodal analysis
title_sort Automatic detection of drowsiness in EEG records based on multimodal analysis
dc.creator.none.fl_str_mv Garces Correa, Maria Agustina
Orosco, Lorena Liliana
Laciar Leber, Eric
author Garces Correa, Maria Agustina
author_facet Garces Correa, Maria Agustina
Orosco, Lorena Liliana
Laciar Leber, Eric
author_role author
author2 Orosco, Lorena Liliana
Laciar Leber, Eric
author2_role author
author
dc.subject.none.fl_str_mv Drowsiness
Alert
Eeg
Wavelet
Neural Networks
topic Drowsiness
Alert
Eeg
Wavelet
Neural Networks
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Drowsiness is one of the main causal factors in many traffic accidents due to the clear decline in the attention and recognition of danger drivers, diminishing vehicle-handling abilities. The aim of this research is to develop an automatic method to detect the drowsiness stage in EEG records using time, spectral and wavelet analysis. A total of 19 features were computed from only one EEG channel to differentiate the alertness and drowsiness stages. After a selection process based on lambda of Wilks criterion, 7 parameters were chosen to feed a Neural Network classifier. Eighteen EEG records were analyzed. The method gets 87.4% and 83.6% of alertness and drowsiness correct detections rates, respectively. The results obtained indicate that the parameters can differentiate both stages. The features are easy to calculate and can be obtained in real time. Those variables could be used in an automatic drowsiness detection system in vehicles, thereby decreasing the rate of accidents caused by sleepiness of the driver.
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; 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; 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; Argentina
description Drowsiness is one of the main causal factors in many traffic accidents due to the clear decline in the attention and recognition of danger drivers, diminishing vehicle-handling abilities. The aim of this research is to develop an automatic method to detect the drowsiness stage in EEG records using time, spectral and wavelet analysis. A total of 19 features were computed from only one EEG channel to differentiate the alertness and drowsiness stages. After a selection process based on lambda of Wilks criterion, 7 parameters were chosen to feed a Neural Network classifier. Eighteen EEG records were analyzed. The method gets 87.4% and 83.6% of alertness and drowsiness correct detections rates, respectively. The results obtained indicate that the parameters can differentiate both stages. The features are easy to calculate and can be obtained in real time. Those variables could be used in an automatic drowsiness detection system in vehicles, thereby decreasing the rate of accidents caused by sleepiness of the driver.
publishDate 2013
dc.date.none.fl_str_mv 2013-08
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/11336/32750
Orosco, Lorena Liliana; Garces Correa, Maria Agustina; Laciar Leber, Eric; Automatic detection of drowsiness in EEG records based on multimodal analysis; Elsevier; Medical Engineering & Physics; 36; 2; 8-2013; 244-249
1350-4533
CONICET Digital
CONICET
url http://hdl.handle.net/11336/32750
identifier_str_mv Orosco, Lorena Liliana; Garces Correa, Maria Agustina; Laciar Leber, Eric; Automatic detection of drowsiness in EEG records based on multimodal analysis; Elsevier; Medical Engineering & Physics; 36; 2; 8-2013; 244-249
1350-4533
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1350453313001690
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.medengphy.2013.07.011
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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