Investigating the contribution of distance-based features to automatic sleep stage classification

Detalhes bibliográficos
Autor(a) principal: Gharbali, Ali Abdollahi
Data de Publicação: 2018
Outros Autores: Najdi, Shirin, Fonseca, José Manuel
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.1016/j.compbiomed.2018.03.001
Resumo: This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS. Sem PDF conforme despacho.
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spelling Investigating the contribution of distance-based features to automatic sleep stage classificationDistance-based featuresFeature extractionFeature selectionItakuraItakura-saitoPolysomnographySleep stage classificationComputer Science ApplicationsHealth InformaticsThis work was partially funded by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS. Sem PDF conforme despacho.Objective: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features. Methods: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C3-A2), Left EOG, Chin EMG and ECG signals. All the evaluations are performed on three feature sets: distance-based, conventional and total (combined distance based and conventional). Six ranking methods were used to find the top features with the highest discrimination ability in each set. The ranked feature lists were evaluated using k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), and Decision-tree-based multi-SVM (DSVM) classifiers for five sleep stages including Wake, REM, N1, N2 and N3. Furthermore, the ability of distance-based and conventional features to discriminate between each pair of sleep stages was evaluated using t-test, a hypothesis testing method. Results: Distance-based features occupied 25% of top-ranked features. Simulation results showed that using distance-based features together with conventional features can lead to an enhancement of accuracy. The best classification accuracy (85.5%) was achieved by DSVM classifier and 13 features selected by mRMR-MID and normalized with Min-Max method for total feature set, where two of them were from the distance-based feature set. The t-test results show that distance-based features outperform conventional features in discriminating between N1 and REM stages that is usually a challenge for classification systems. Conclusion: Distance-based features have a positive contribution to sleep stage classification, including enhancement of accuracy and better REM-N1 discrimination ability. Significance: The main motivation for this work was to evaluate new features to characterize each sleep stage in such a way that extracted features were more powerful than conventional features, to distinguish sleep stages from each other, and to improve classifiers accuracy.UNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasDEE2010-C1 Sistemas Digitais e PercepcionaisDEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNGharbali, Ali AbdollahiNajdi, ShirinFonseca, José Manuel2019-05-02T22:15:07Z2018-05-012018-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttps://doi.org/10.1016/j.compbiomed.2018.03.001eng0010-4825PURE: 3790833http://www.scopus.com/inward/record.url?scp=85043355702&partnerID=8YFLogxKhttps://doi.org/10.1016/j.compbiomed.2018.03.001info: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:RCAAP2024-03-11T04:32:19Zoai:run.unl.pt:10362/68430Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:34:46.052509Repositório 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 Investigating the contribution of distance-based features to automatic sleep stage classification
title Investigating the contribution of distance-based features to automatic sleep stage classification
spellingShingle Investigating the contribution of distance-based features to automatic sleep stage classification
Gharbali, Ali Abdollahi
Distance-based features
Feature extraction
Feature selection
Itakura
Itakura-saito
Polysomnography
Sleep stage classification
Computer Science Applications
Health Informatics
title_short Investigating the contribution of distance-based features to automatic sleep stage classification
title_full Investigating the contribution of distance-based features to automatic sleep stage classification
title_fullStr Investigating the contribution of distance-based features to automatic sleep stage classification
title_full_unstemmed Investigating the contribution of distance-based features to automatic sleep stage classification
title_sort Investigating the contribution of distance-based features to automatic sleep stage classification
author Gharbali, Ali Abdollahi
author_facet Gharbali, Ali Abdollahi
Najdi, Shirin
Fonseca, José Manuel
author_role author
author2 Najdi, Shirin
Fonseca, José Manuel
author2_role author
author
dc.contributor.none.fl_str_mv UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
CTS - Centro de Tecnologia e Sistemas
DEE2010-C1 Sistemas Digitais e Percepcionais
DEE - Departamento de Engenharia Electrotécnica e de Computadores
RUN
dc.contributor.author.fl_str_mv Gharbali, Ali Abdollahi
Najdi, Shirin
Fonseca, José Manuel
dc.subject.por.fl_str_mv Distance-based features
Feature extraction
Feature selection
Itakura
Itakura-saito
Polysomnography
Sleep stage classification
Computer Science Applications
Health Informatics
topic Distance-based features
Feature extraction
Feature selection
Itakura
Itakura-saito
Polysomnography
Sleep stage classification
Computer Science Applications
Health Informatics
description This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS. Sem PDF conforme despacho.
publishDate 2018
dc.date.none.fl_str_mv 2018-05-01
2018-05-01T00:00:00Z
2019-05-02T22:15:07Z
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 https://doi.org/10.1016/j.compbiomed.2018.03.001
url https://doi.org/10.1016/j.compbiomed.2018.03.001
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0010-4825
PURE: 3790833
http://www.scopus.com/inward/record.url?scp=85043355702&partnerID=8YFLogxK
https://doi.org/10.1016/j.compbiomed.2018.03.001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
application/pdf
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
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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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