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Complexity of Cardiotocographic Signals as A Predictor of Labor

2020-01-20Journal article Open access
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dc.contributor.authorMonteiro-Santos, João
dc.contributor.authorHenriques, Teresa
dc.contributor.authorNunes, Ines
dc.contributor.authorAmorim-Costa, Célia
dc.contributor.authorBernardes, João
dc.contributor.authorCosta-Santos, Cristina
dc.date.accessioned2021-11-22T13:40:02Z
dc.date.available2021-11-22T13:40:02Z
dc.date.issued2020-01-20
dc.description.abstractPrediction of labor is of extreme importance in obstetric care to allow for preventive measures, assuring that both baby and mother have the best possible care. In this work, the authors studied how important nonlinear parameters (entropy and compression) can be as labor predictors. Linear features retrieved from the SisPorto system for cardiotocogram analysis and nonlinear measures were used to predict labor in a dataset of 1072 antepartum tracings, at between 30 and 35 weeks of gestation. Two groups were defined: Group A-fetuses whose traces date was less than one or two weeks before labor, and Group B-fetuses whose traces date was at least one or two weeks before labor. Results suggest that, compared with linear features such as decelerations and variability indices, compression improves labor prediction both within one (C-Statistics of 0.728) and two weeks (C-Statistics of 0.704). Moreover, the correlation between compression and long-term variability was significantly different in groups A and B, denoting that compression and heart rate variability look at different information associated with whether the fetus is closer to or further from labor onset. Nonlinear measures, compression in particular, may be useful in improving labor prediction as a complement to other fetal heart rate features.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMonteiro-Santos J, Henriques T, Nunes I, Amorim-Costa C, Bernardes J, Costa-Santos C. Complexity of Cardiotocographic Signals as A Predictor of Labor. Entropy (Basel). 2020;22(1):104. doi:10.3390/e22010104pt_PT
dc.identifier.doi10.3390/e22010104pt_PT
dc.identifier.issn1099-4300
dc.identifier.urihttp://hdl.handle.net/10400.16/2591
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1099-4300/22/1/104pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectcomplexity analysispt_PT
dc.subjectdata compressionpt_PT
dc.subjectentropypt_PT
dc.subjectfetal heart ratept_PT
dc.subjectlaborpt_PT
dc.subjectnonlinear analysispt_PT
dc.subjectpretermpt_PT
dc.titleComplexity of Cardiotocographic Signals as A Predictor of Laborpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceSwitzerlandpt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage104pt_PT
oaire.citation.titleEntropypt_PT
oaire.citation.volume22pt_PT
person.familyNameNunes
person.givenNameInes
person.identifier859117
person.identifier.ciencia-idF91A-0D37-BAB6
person.identifier.orcid0000-0001-6709-3916
person.identifier.scopus-author-id55778407000
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication1f75697b-7e31-42cd-9517-e15c4767f437
relation.isAuthorOfPublication.latestForDiscovery1f75697b-7e31-42cd-9517-e15c4767f437

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