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A novel algorithm for the automatic detection of sleep apnea from single-lead ECG

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dc.creatorVaron, Carolinaspa
dc.creatorCaicedo Dorado, Alexander
dc.creatorTestelmans, Driesspa
dc.creatorBuyse, Bertienspa
dc.creatorVan Huffel, Sabinespa
dc.date.accessioned2020-08-19T14:43:13Z
dc.date.available2020-08-19T14:43:13Z
dc.date.created2015-04-13spa
dc.description.abstractGoal: This paper presents a methodology for the automatic detection of sleep apnea from single-lead ECG. Methods: It uses two novel features derived from the ECG, and two well-known features in heart rate variability analysis, namely the standard deviation and the serial correlation coefficients of the RR interval time series. The first novel feature uses the principal components of the QRS complexes, and it describes changes in their morphology caused by an increased sympathetic activity during apnea. The second novel feature extracts the information shared between respiration and heart rate using orthogonal subspace projections. Respiratory information is derived from the ECG by means of three state-of-the-art algorithms, which are implemented and compared here. All features are used as input to a least-squares support vector machines classifier, using an RBF kernel. In total, 80 ECG recordings were included in the study. Results: Accuracies of about 85% are achieved on a minute-by-minute basis, for two independent datasets including both hypopneas and apneas together. Separation between apnea and normal recordings is achieved with 100% accuracy. In addition to apnea classification, the proposed methodology determines the contamination level of each ECG minute. Conclusion: The performances achieved are comparable with those reported in the literature for fully automated algorithms. Significance: These results indicate that the use of only ECG sensors can achieve good accuracies in the detection of sleep apnea. Moreover, the contamination level of each ECG segment can be used to automatically detect artefacts, and to highlight segments that require further visual inspection.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1109/TBME.2015.2422378
dc.identifier.issnISSN: 0018-9294
dc.identifier.issnEISSN: 1558-2531
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/27664
dc.language.isoengspa
dc.publisherIEEEspa
dc.relation.citationEndPage2278
dc.relation.citationIssueNo. 9
dc.relation.citationStartPage2269
dc.relation.citationTitleIEEE Transactions on Biomedical Engineering
dc.relation.citationVolumeVol. 62
dc.relation.ispartofIEEE Transactions on Biomedical Engineering, ISSN: 0018-9294;EISSN: 1558-2531, Vol.62, No.9 (Sept 2015); pp. 2269 - 2278spa
dc.relation.urihttps://ieeexplore.ieee.org/document/7084597spa
dc.rights.accesRightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.accesoRestringido (Acceso a grupos específicos)spa
dc.sourceIEEE Transactions on Biomedical Engineeringspa
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subject.keywordElectrocardiographyspa
dc.subject.keywordSleep apneaspa
dc.subject.keywordHeart ratespa
dc.subject.keywordMorphologyspa
dc.subject.keywordPrincipal component analysisspa
dc.subject.keywordFeature extractionspa
dc.subject.keywordEigenvalues and eigenfunctionsspa
dc.titleA novel algorithm for the automatic detection of sleep apnea from single-lead ECGspa
dc.title.TranslatedTitleUn algoritmo novedoso para la detección automática de la apnea del sueño a partir de ECG de una sola derivaciónspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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