Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Outros Autores: | , , , |
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. |
id |
RCAP_19af7c8c396d7be53c04c2d40b1cb05c |
---|---|
oai_identifier_str |
oai:ubibliorum.ubi.pt:10400.6/8254 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1799136380243148800 |