Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection

Detalhes bibliográficos
Autor(a) principal: Pinho, André
Data de Publicação: 2019
Outros Autores: Pombo, Nuno, Silva, Bruno M.C., Bousson, K., Garcia, Nuno M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.6/8254
Resumo: A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography.
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spelling Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selectionSleep apneaElectrocardiogram (ECG)Heart rate variability (HRV)ECG-derived respiration (EDR)Feature selectionClassificationArtificial neural network (ANN)Support vector machine (SVM)Linear discriminant analysis (LDA)Partial least squares regression (PLS)Regression analysis (REG)Wiener–Hopf equation (wienerHopf)Augmented naive bayesian classifier (aNBC)Perceptron learning algorithmA wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography.uBibliorumPinho, AndréPombo, NunoSilva, Bruno M.C.Bousson, K.Garcia, Nuno M.2020-01-14T14:22:15Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/8254eng10.1016/j.asoc.2019.105568info: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:RCAAP2023-12-15T09:48:07Zoai:ubibliorum.ubi.pt:10400.6/8254Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:48:37.802238Repositório 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 Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
title Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
spellingShingle Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
Pinho, André
Sleep apnea
Electrocardiogram (ECG)
Heart rate variability (HRV)
ECG-derived respiration (EDR)
Feature selection
Classification
Artificial neural network (ANN)
Support vector machine (SVM)
Linear discriminant analysis (LDA)
Partial least squares regression (PLS)
Regression analysis (REG)
Wiener–Hopf equation (wienerHopf)
Augmented naive bayesian classifier (aNBC)
Perceptron learning algorithm
title_short Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
title_full Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
title_fullStr Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
title_full_unstemmed Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
title_sort Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
author Pinho, André
author_facet Pinho, André
Pombo, Nuno
Silva, Bruno M.C.
Bousson, K.
Garcia, Nuno M.
author_role author
author2 Pombo, Nuno
Silva, Bruno M.C.
Bousson, K.
Garcia, Nuno M.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv uBibliorum
dc.contributor.author.fl_str_mv Pinho, André
Pombo, Nuno
Silva, Bruno M.C.
Bousson, K.
Garcia, Nuno M.
dc.subject.por.fl_str_mv Sleep apnea
Electrocardiogram (ECG)
Heart rate variability (HRV)
ECG-derived respiration (EDR)
Feature selection
Classification
Artificial neural network (ANN)
Support vector machine (SVM)
Linear discriminant analysis (LDA)
Partial least squares regression (PLS)
Regression analysis (REG)
Wiener–Hopf equation (wienerHopf)
Augmented naive bayesian classifier (aNBC)
Perceptron learning algorithm
topic Sleep apnea
Electrocardiogram (ECG)
Heart rate variability (HRV)
ECG-derived respiration (EDR)
Feature selection
Classification
Artificial neural network (ANN)
Support vector machine (SVM)
Linear discriminant analysis (LDA)
Partial least squares regression (PLS)
Regression analysis (REG)
Wiener–Hopf equation (wienerHopf)
Augmented naive bayesian classifier (aNBC)
Perceptron learning algorithm
description A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01T00:00:00Z
2020-01-14T14:22:15Z
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 http://hdl.handle.net/10400.6/8254
url http://hdl.handle.net/10400.6/8254
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1016/j.asoc.2019.105568
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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
institution RCAAP
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
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