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

2019Journal article Open access
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dc.contributor.authorPinho, André
dc.contributor.authorPombo, Nuno
dc.contributor.authorSilva, Bruno M.C.
dc.contributor.authorBousson, K.
dc.contributor.authorGarcia, Nuno M.
dc.date.accessioned2020-01-14T14:22:15Z
dc.date.available2020-01-14T14:22:15Z
dc.date.issued2019
dc.description.abstractA 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.asoc.2019.105568pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8254
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationInstituto de Telecomunicações
dc.subjectSleep apneapt_PT
dc.subjectElectrocardiogram (ECG)pt_PT
dc.subjectHeart rate variability (HRV)pt_PT
dc.subjectECG-derived respiration (EDR)pt_PT
dc.subjectFeature selectionpt_PT
dc.subjectClassificationpt_PT
dc.subjectArtificial neural network (ANN)pt_PT
dc.subjectSupport vector machine (SVM)pt_PT
dc.subjectLinear discriminant analysis (LDA)pt_PT
dc.subjectPartial least squares regression (PLS)pt_PT
dc.subjectRegression analysis (REG)pt_PT
dc.subjectWiener–Hopf equation (wienerHopf)pt_PT
dc.subjectAugmented naive bayesian classifier (aNBC)pt_PT
dc.subjectPerceptron learning algorithmpt_PT
dc.titleTowards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50008%2F2019/PT
oaire.citation.startPage105568pt_PT
oaire.citation.titleApplied Soft Computingpt_PT
oaire.citation.volume83pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePombo
person.familyNameBousson
person.familyNameGarcia dos Santos
person.givenNameNuno
person.givenNameKouamana
person.givenNameNuno Manuel
person.identifier.ciencia-id0F16-A18D-96BA
person.identifier.ciencia-idE719-0DEC-9751
person.identifier.orcid0000-0001-7797-8849
person.identifier.orcid0000-0001-5517-8963
person.identifier.orcid0000-0002-3195-3168
person.identifier.ridI-4117-2015
person.identifier.scopus-author-id55389546100
person.identifier.scopus-author-id6602666967
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication73519920-9c7f-4fcd-9207-1b8e9a8b1738
relation.isAuthorOfPublicationcbb18161-dfb1-41ce-9c4f-a08cfb84fb03
relation.isAuthorOfPublication3648e9b2-25ee-4d13-9af2-4addc30dae7c
relation.isAuthorOfPublication.latestForDiscovery73519920-9c7f-4fcd-9207-1b8e9a8b1738
relation.isProjectOfPublication11e7de42-6a06-4bc8-99c3-ce68de57dbda
relation.isProjectOfPublication.latestForDiscovery11e7de42-6a06-4bc8-99c3-ce68de57dbda

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