Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment

Producción Científica

Detalhes bibliográficos
Autores: Ruiz Gómez, Saúl José, Gómez Peña, Carlos, Poza Crespo, Jesús, Gutierrez Tobal, Gonzalo César, Tola Arribas, Miguel Ángel, Cano, Mónica, Hornero Sánchez, Roberto
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Recursos:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/57386
Acesso em linha:https://doi.org/10.3390/e20010035
https://uvadoc.uva.es/handle/10324/57386
Access Level:acceso abierto
Palavra-chave:Alzheimer’s disease
Mild cognitive impairment
Electroencephalography (EEG)
Spectral analysis
Nonlinear analysis
Multiclass classification approach
12 Matemáticas
32 Ciencias Médicas
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spelling Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairmentRuiz Gómez, Saúl JoséGómez Peña, CarlosPoza Crespo, JesúsGutierrez Tobal, Gonzalo CésarTola Arribas, Miguel ÁngelCano, MónicaHornero Sánchez, RobertoAlzheimer’s diseaseMild cognitive impairmentElectroencephalography (EEG)Spectral analysisNonlinear analysisMulticlass classification approach12 Matemáticas32 Ciencias MédicasProducción CientíficaThe discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.Ministerio de Economía y Competitividad y “Fondo Europeo de Desarrollo Regional” (FEDER) proyecto “Análisis y obtención del genoma entre el completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer” (“Programa de Cooperación Interreg V-A España-Portugal, POCTEP 2014–2020”),(underl proyect TEC2014-53196-R)Junta de Castilla y León - Consejería de Educación y FEDER en el marco del proyecto VA037U16.MDPI2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/e20010035https://uvadoc.uva.es/handle/10324/57386reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.mdpi.com/1099-4300/20/1/35info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:uvadoc.uva.es:10324/573862026-06-13T12:44:47Z
dc.title.none.fl_str_mv Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
title Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
spellingShingle Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
Ruiz Gómez, Saúl José
Alzheimer’s disease
Mild cognitive impairment
Electroencephalography (EEG)
Spectral analysis
Nonlinear analysis
Multiclass classification approach
12 Matemáticas
32 Ciencias Médicas
title_short Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
title_full Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
title_fullStr Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
title_full_unstemmed Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
title_sort Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
dc.creator.none.fl_str_mv Ruiz Gómez, Saúl José
Gómez Peña, Carlos
Poza Crespo, Jesús
Gutierrez Tobal, Gonzalo César
Tola Arribas, Miguel Ángel
Cano, Mónica
Hornero Sánchez, Roberto
author Ruiz Gómez, Saúl José
author_facet Ruiz Gómez, Saúl José
Gómez Peña, Carlos
Poza Crespo, Jesús
Gutierrez Tobal, Gonzalo César
Tola Arribas, Miguel Ángel
Cano, Mónica
Hornero Sánchez, Roberto
author_role author
author2 Gómez Peña, Carlos
Poza Crespo, Jesús
Gutierrez Tobal, Gonzalo César
Tola Arribas, Miguel Ángel
Cano, Mónica
Hornero Sánchez, Roberto
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Alzheimer’s disease
Mild cognitive impairment
Electroencephalography (EEG)
Spectral analysis
Nonlinear analysis
Multiclass classification approach
12 Matemáticas
32 Ciencias Médicas
topic Alzheimer’s disease
Mild cognitive impairment
Electroencephalography (EEG)
Spectral analysis
Nonlinear analysis
Multiclass classification approach
12 Matemáticas
32 Ciencias Médicas
description Producción Científica
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/e20010035
https://uvadoc.uva.es/handle/10324/57386
url https://doi.org/10.3390/e20010035
https://uvadoc.uva.es/handle/10324/57386
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/1099-4300/20/1/35
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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