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Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

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Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

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dc.contributor.author Prats-Boluda, Gema es_ES
dc.contributor.author Pastor-Tronch, Julio es_ES
dc.contributor.author Garcia-Casado, Javier es_ES
dc.contributor.author Monfort-Ortiz, Rogelio es_ES
dc.contributor.author Perales Marín, Alfredo es_ES
dc.contributor.author Diago, Vicente es_ES
dc.contributor.author Roca Prats, Alba es_ES
dc.contributor.author Ye Lin, Yiyao es_ES
dc.date.accessioned 2021-11-05T14:09:33Z
dc.date.available 2021-11-05T14:09:33Z
dc.date.issued 2021-04 es_ES
dc.identifier.uri http://hdl.handle.net/10251/176374
dc.description.abstract [EN] Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th-90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 +/- 8.34% and 90.2 +/- 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th-90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability. es_ES
dc.description.sponsorship This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR); by the Generalitat Valenciana (AICO/2019/220). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Electrohysterogram es_ES
dc.subject Uterine myoelectrical activity es_ES
dc.subject Tocolytic therapy es_ES
dc.subject Random forest es_ES
dc.subject Extreme learning machine es_ES
dc.subject K-nearest neighbors es_ES
dc.subject Imminent labor prediction es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s21072496 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094449-A-I00/ES/ELECTROHISTEROGRAFIA PARA LA MEJORA EN LA TOMA DE DECISIONES EN SITUACIONES DE RIESGO EN OBSTETRICIA: PARTO PREMATURO E INDUCCION DEL PARTO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//AICO%2F2019%2F220//DESARROLLO DE HERRAMIENTAS DE USO CLINICO PARA LA PREDICCION DEL PARTO PREMATURO EN BASE A LA ELECTROHISTEROGRAFIA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Servicio de Alumnado - Servei d'Alumnat es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Prats-Boluda, G.; Pastor-Tronch, J.; Garcia-Casado, J.; Monfort-Ortiz, R.; Perales Marín, A.; Diago, V.; Roca Prats, A.... (2021). Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography. Sensors. 21(7):1-18. https://doi.org/10.3390/s21072496 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s21072496 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 18 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 21 es_ES
dc.description.issue 7 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 33916679 es_ES
dc.identifier.pmcid PMC8038321 es_ES
dc.relation.pasarela S\439459 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
upv.costeAPC 1750 es_ES


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