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Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography?

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Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography?

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dc.contributor.author Alberola Rubio, José es_ES
dc.contributor.author Garcia Casado, Francisco Javier es_ES
dc.contributor.author Prats-Boluda, Gema es_ES
dc.contributor.author Ye Lin, Yiyao es_ES
dc.contributor.author Desantes, D. es_ES
dc.contributor.author Valero, J. es_ES
dc.contributor.author Perales Marin, Alfredo Jose es_ES
dc.date.accessioned 2017-05-19T12:02:43Z
dc.date.available 2017-05-19T12:02:43Z
dc.date.issued 2017-03-28
dc.identifier.issn 0169-2607
dc.identifier.uri http://hdl.handle.net/10251/81498
dc.description.abstract Background and objective Induction of labor (IOL) is a medical procedure used to initiate uterine contractions to achieve delivery. IOL entails medical risks and has a significant impact on both the mother's and newborn's well-being. The assistance provided by an automatic system to help distinguish patients that will achieve labor spontaneously from those that will need late-term IOL would help clinicians and mothers to take an informed decision about prolonging pregnancy. With this aim, we developed and evaluated predictive models using not only traditional obstetrical data but also electrophysiological parameters derived from the electrohysterogram (EHG). Methods EHG recordings were made on singleton term pregnancies. A set of 10 temporal and spectral parameters was calculated to characterize EHG bursts and a further set of 6 common obstetrical parameters was also considered in the predictive models design. Different models were implemented based on single layer Support Vector Machines (SVM) and with aggregation of majority voting of SVM (double layer), to distinguish between the two groups: term spontaneous labor (≤41 weeks of gestation) and IOL late-term labor. The areas under the curve (AUC) of the models were compared. Results The obstetrical and EHG parameters of the two groups did not show statistically significant differences. The best results of non-contextualized single input parameter SVM models were achieved by the Bishop Score (AUC = 0.65) and GA at recording time (AUC = 0.68) obstetrical parameters. The EHG parameter median frequency, when contextualized with the two obstetrical parameters improved these results, reaching AUC = 0.76. Multiple input SVM obtained AUC = 0.70 for all EHG parameters. Aggregation of majority voting of SVM models using contextualized EHG parameters achieved the best result AUC = 0.93. Conclusions Measuring the electrophysiological uterine condition by means of electrohysterographic recordings yielded a promising clinical decision support system for distinguishing patients that will spontaneously achieve active labor before the end of full term from those who will require late term IOL. The importance of considering these EHG measurements in the patient's individual context was also shown by combining EHG parameters with obstetrical parameters. Clinicians considering elective labor induction would benefit from this technique. es_ES
dc.description.sponsorship General Electric Healthcare en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation Conselleria d'Educació, Cultura i Esport, Generalitat Valenciana Conselleria (GV/2014/029). es_ES
dc.relation Universitat Politècnica de Valencia (SP20120490) and by a VLC_Campus grant (Prematuro ID34). es_ES
dc.relation info:eu-repo/grantAgreement/MINECO//DPI2015-68397-R/ES/ELECTROHISTEROGRAFIA, CONSTRUYENDO PUENTES PARA SU USO CLINICO EN OBSTETRICIA/ es_ES
dc.relation General Electric Healthcare es_ES
dc.relation.ispartof Computer Methods and Programs in Biomedicine es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Electrohysterogram es_ES
dc.subject SVM es_ES
dc.subject Majority voting es_ES
dc.subject Labor management es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography? es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cmpb.2017.03.018
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny es_ES
dc.contributor.affiliation Universitat Politècnica de València. Servicio de Alumnado - Servei d'Alumnat es_ES
dc.description.bibliographicCitation Alberola Rubio, J.; Garcia Casado, FJ.; Prats-Boluda, G.; Ye Lin, Y.; Desantes, D.; Valero, J.; Perales Marin, AJ. (2017). Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography?. Computer Methods and Programs in Biomedicine. 144:127-133. https://doi.org/10.1016/j.cmpb.2017.03.018 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.cmpb.2017.03.018 es_ES
dc.description.upvformatpinicio 127 es_ES
dc.description.upvformatpfin 133 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 144 es_ES
dc.relation.senia 331820 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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