- -

Prediction of Labor Induction Success from the Uterine Electrohysterogram

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Prediction of Labor Induction Success from the Uterine Electrohysterogram

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Benalcazar-Parra; ...
Tamaño: 1.832Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Benalcazar-Parra, Carlos es_ES
dc.contributor.author Ye Lin, Yiyao es_ES
dc.contributor.author Garcia-Casado, Javier es_ES
dc.contributor.author Monfort-Ortiz, Rogelio es_ES
dc.contributor.author Alberola Rubio, José es_ES
dc.contributor.author PERALES MARIN, ALFREDO JOSE es_ES
dc.contributor.author Prats-Boluda, Gema es_ES
dc.date.accessioned 2020-06-10T03:32:43Z
dc.date.available 2020-06-10T03:32:43Z
dc.date.issued 2019-11-15 es_ES
dc.identifier.issn 1687-725X es_ES
dc.identifier.uri http://hdl.handle.net/10251/145868
dc.description.abstract [EN] Pharmacological agents are often used to induce labor. Failed inductions are associated with unnecessarily long waits and greater maternal-fetal risks, as well as higher costs. No reliable models are currently able to predict the induction outcome from common obstetric data (area under the ROC curve (AUC) between 0.6 and 0.7). The aim of this study was to design an early success-predictor system by extracting temporal, spectral, and complexity parameters from the uterine electromyogram (electrohysterogram (EHG)). Different types of feature sets were used to design and train artificial neural networks: Set_1: obstetrical features, Set_2: EHG features, and Set_3: EHG+obstetrical features. Predictor systems were built to classify three scenarios: (1) induced women who reached active phase of labor (APL) vs. women who did not achieve APL (non-APL), (2) APL and vaginal delivery vs. APL and cesarean section delivery, and (3) vaginal vs. cesarean delivery. For Scenario 3, we also proposed 2-step predictor systems consisting of the cascading predictor systems from Scenarios 1 and 2. EHG features outperformed traditional obstetrical features in all the scenarios. Little improvement was obtained by combining them (Set_3). The results show that the EHG can potentially be used to predict successful labor induction and outperforms the traditional obstetric features. Clinical use of this prediction system would help to improve maternal-fetal well-being and optimize hospital resources. es_ES
dc.description.sponsorship This work received financial support from the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (DPI2015-68397-R and RTI2018-094449-A-I00), Universitat Politècnica de València VLC/Campus (UPV-FE-2018-B02), Generalitat Valenciana (GV/2018/104), and Bial S.A. es_ES
dc.language Inglés es_ES
dc.publisher Hindawi Limited es_ES
dc.relation.ispartof Journal of Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Electrical-Activity es_ES
dc.subject Signal es_ES
dc.subject Term es_ES
dc.subject EMG es_ES
dc.subject Delivery es_ES
dc.subject Parameters es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Prediction of Labor Induction Success from the Uterine Electrohysterogram es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1155/2019/6916251 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2015-68397-R/ES/ELECTROHISTEROGRAFIA, CONSTRUYENDO PUENTES PARA SU USO CLINICO EN OBSTETRICIA/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//UPV-FE-2018-B02/ES/PARTOS INDUCIDOS:¿ÉXITO O FRACASO? PREDICCIÓN TEMPRANA MEDIANTE EHG/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//GV%2F2018%2F104/ 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.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica 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. Servicio de Alumnado - Servei d'Alumnat es_ES
dc.description.bibliographicCitation Benalcazar-Parra, C.; Ye Lin, Y.; Garcia-Casado, J.; Monfort-Ortiz, R.; Alberola Rubio, J.; Perales Marin, AJ.; Prats-Boluda, G. (2019). Prediction of Labor Induction Success from the Uterine Electrohysterogram. Journal of Sensors. 2019:1-12. https://doi.org/10.1155/2019/6916251 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1155/2019/6916251 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 12 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 2019 es_ES
dc.relation.pasarela S\400851 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Instituto de Investigación Sanitaria La Fe es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Filho, O. B. M., Albuquerque, R. M., & Cecatti, J. G. (2010). A randomized controlled trial comparing vaginal misoprostol versus Foley catheter plus oxytocin for labor induction. Acta Obstetricia et Gynecologica Scandinavica, 89(8), 1045-1052. doi:10.3109/00016349.2010.499447 es_ES
dc.description.references Seyb, S. (1999). Risk of cesarean delivery with elective induction of labor at term in nulliparous women. Obstetrics & Gynecology, 94(4), 600-607. doi:10.1016/s0029-7844(99)00377-4 es_ES
dc.description.references Hou, L., Zhu, Y., Ma, X., Li, J., & Zhang, W. (2012). Clinical parameters for prediction of successful labor induction after application of intravaginal dinoprostone in nulliparous Chinese women. Medical Science Monitor, 18(8), CR518-CR522. doi:10.12659/msm.883273 es_ES
dc.description.references Pitarello, P. da R. P., Tadashi Yoshizaki, C., Ruano, R., & Zugaib, M. (2012). Prediction of successful labor induction using transvaginal sonographic cervical measurements. Journal of Clinical Ultrasound, 41(2), 76-83. doi:10.1002/jcu.21929 es_ES
dc.description.references Prado, C. A. de C., Araujo Júnior, E., Duarte, G., Quintana, S. M., Tonni, G., Cavalli, R. de C., & Marcolin, A. C. (2016). Predicting success of labor induction in singleton term pregnancies by combining maternal and ultrasound variables. The Journal of Maternal-Fetal & Neonatal Medicine, 1-35. doi:10.3109/14767058.2015.1135124 es_ES
dc.description.references Sievert, R. A., Kuper, S. G., Jauk, V. C., Parrish, M., Biggio, J. R., & Harper, L. M. (2017). Predictors of vaginal delivery in medically indicated early preterm induction of labor. American Journal of Obstetrics and Gynecology, 217(3), 375.e1-375.e7. doi:10.1016/j.ajog.2017.05.025 es_ES
dc.description.references Garfield, R. E., Maner, W. L., Maul, H., & Saade, G. R. (2005). Use of uterine EMG and cervical LIF in monitoring pregnant patients. BJOG: An International Journal of Obstetrics & Gynaecology, 112, 103-108. doi:10.1111/j.1471-0528.2005.00596.x es_ES
dc.description.references Fergus, P., Cheung, P., Hussain, A., Al-Jumeily, D., Dobbins, C., & Iram, S. (2013). Prediction of Preterm Deliveries from EHG Signals Using Machine Learning. PLoS ONE, 8(10), e77154. doi:10.1371/journal.pone.0077154 es_ES
dc.description.references Aviram, A., Melamed, N., Hadar, E., Raban, O., Hiersch, L., & Yogev, Y. (2013). Effect of Prostaglandin E2 on Myometrial Electrical Activity in Women Undergoing Induction of Labor. American Journal of Perinatology, 31(05), 413-418. doi:10.1055/s-0033-1352486 es_ES
dc.description.references Benalcazar-Parra, C., Ye-Lin, Y., Garcia-Casado, J., Monfort-Orti, R., Alberola-Rubio, J., Perales, A., & Prats-Boluda, G. (2018). Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Medical Engineering & Physics, 56, 27-35. doi:10.1016/j.medengphy.2018.04.002 es_ES
dc.description.references Benalcazar-Parra, C., Monfort-Orti, R., Ye-Lin, Y., Prats-Boluda, G., Alberola-Rubio, J., Perales, A., & Garcia-Casado, J. (2017). Comparison of labour induction with misoprostol and dinoprostone and characterization of uterine response based on electrohysterogram. The Journal of Maternal-Fetal & Neonatal Medicine, 32(10), 1586-1594. doi:10.1080/14767058.2017.1410791 es_ES
dc.description.references Maner, W. L., & Garfield, R. E. (2007). Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Annals of Biomedical Engineering, 35(3), 465-473. doi:10.1007/s10439-006-9248-8 es_ES
dc.description.references Diab, M. O., Marque, C., & Khalil, M. (2009). An unsupervised classification method of uterine electromyography signals: Classification for detection of preterm deliveries. Journal of Obstetrics and Gynaecology Research, 35(1), 9-19. doi:10.1111/j.1447-0756.2008.00981.x es_ES
dc.description.references Shi, S.-Q., Maner, W. L., Mackay, L. B., & Garfield, R. E. (2008). Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals. American Journal of Obstetrics and Gynecology, 198(2), 235.e1-235.e4. doi:10.1016/j.ajog.2007.08.039 es_ES
dc.description.references Østborg, T. B., Romundstad, P. R., & Eggebø, T. M. (2016). Duration of the active phase of labor in spontaneous and induced labors. Acta Obstetricia et Gynecologica Scandinavica, 96(1), 120-127. doi:10.1111/aogs.13039 es_ES
dc.description.references Baños, N., Migliorelli, F., Posadas, E., Ferreri, J., & Palacio, M. (2015). Definition of Failed Induction of Labor and Its Predictive Factors: Two Unsolved Issues of an Everyday Clinical Situation. Fetal Diagnosis and Therapy, 38(3), 161-169. doi:10.1159/000433429 es_ES
dc.description.references Bueno, B., San-Frutos, L., Salazar, F., Pérez-Medina, T., Engels, V., Archilla, B., … Bajo, J. (2005). Variables that predict the success of labor induction. Acta Obstetricia et Gynecologica Scandinavica, 84(11), 1093-1097. doi:10.1111/j.0001-6349.2005.00881.x es_ES
dc.description.references Ware, V., & Raynor, B. D. (2000). Transvaginal ultrasonographic cervical measurement as a predictor of successful labor induction. American Journal of Obstetrics and Gynecology, 182(5), 1030-1032. doi:10.1067/mob.2000.105399 es_ES
dc.description.references Rooijakkers, M. J., Song, S., Rabotti, C., Oei, S. G., Bergmans, J. W. M., Cantatore, E., & Mischi, M. (2014). Influence of Electrode Placement on Signal Quality for Ambulatory Pregnancy Monitoring. Computational and Mathematical Methods in Medicine, 2014, 1-12. doi:10.1155/2014/960980 es_ES
dc.description.references Garfield, R. E., & Maner, W. L. (2007). Physiology and electrical activity of uterine contractions. Seminars in Cell & Developmental Biology, 18(3), 289-295. doi:10.1016/j.semcdb.2007.05.004 es_ES
dc.description.references Leman, H., Marque, C., & Gondry, J. (1999). Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Transactions on Biomedical Engineering, 46(10), 1222-1229. doi:10.1109/10.790499 es_ES
dc.description.references BUHIMSCHI, C., BOYLE, M., & GARFIELD, R. (1997). Electrical activity of the human uterus during pregnancy as recorded from the abdominal surface. Obstetrics & Gynecology, 90(1), 102-111. doi:10.1016/s0029-7844(97)83837-9 es_ES
dc.description.references Schlembach, D., Maner, W. L., Garfield, R. E., & Maul, H. (2009). Monitoring the progress of pregnancy and labor using electromyography. European Journal of Obstetrics & Gynecology and Reproductive Biology, 144, S33-S39. doi:10.1016/j.ejogrb.2009.02.016 es_ES
dc.description.references Alamedine, D., Diab, A., Muszynski, C., Karlsson, B., Khalil, M., & Marque, C. (2014). Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor. Signal, Image and Video Processing, 8(6), 1169-1178. doi:10.1007/s11760-014-0655-2 es_ES
dc.description.references Fele-Žorž, G., Kavšek, G., Novak-Antolič, Ž., & Jager, F. (2008). A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-y es_ES
dc.description.references Terrien, J., Marque, C., Gondry, J., Steingrimsdottir, T., & Karlsson, B. (2010). Uterine electromyogram database and processing function interface: An open standard analysis platform for electrohysterogram signals. Computers in Biology and Medicine, 40(2), 223-230. doi:10.1016/j.compbiomed.2009.11.019 es_ES
dc.description.references Hassan, M., Terrien, J., Marque, C., & Karlsson, B. (2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980-986. doi:10.1016/j.medengphy.2011.03.010 es_ES
dc.description.references Lemancewicz, A., Borowska, M., Kuć, P., Jasińska, E., Laudański, P., Laudański, T., & Oczeretko, E. (2016). Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing. Biocybernetics and Biomedical Engineering, 36(1), 302-307. doi:10.1016/j.bbe.2015.11.005 es_ES
dc.description.references Weiting Chen, Zhizhong Wang, Hongbo Xie, & Wangxin Yu. (2007). Characterization of Surface EMG Signal Based on Fuzzy Entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266-272. doi:10.1109/tnsre.2007.897025 es_ES
dc.description.references Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601 es_ES
dc.description.references Blanco, S., Garay, A., & Coulombie, D. (2013). Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction. ISRN Neurology, 2013, 1-5. doi:10.1155/2013/287327 es_ES
dc.description.references Brennan, M., Palaniswami, M., & Kamen, P. (2001). Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48(11), 1342-1347. doi:10.1109/10.959330 es_ES
dc.description.references Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953 es_ES
dc.description.references Makond, B., Wang, K.-J., & Wang, K.-M. (2015). Probabilistic modeling of short survivability in patients with brain metastasis from lung cancer. Computer Methods and Programs in Biomedicine, 119(3), 142-162. doi:10.1016/j.cmpb.2015.02.005 es_ES
dc.description.references Gori, M., & Tesi, A. (1992). On the problem of local minima in backpropagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(1), 76-86. doi:10.1109/34.107014 es_ES
dc.description.references Zong, W., Huang, G.-B., & Chen, Y. (2013). Weighted extreme learning machine for imbalance learning. Neurocomputing, 101, 229-242. doi:10.1016/j.neucom.2012.08.010 es_ES
dc.description.references Acharya, U. R., Sudarshan, V. K., Rong, S. Q., Tan, Z., Lim, C. M., Koh, J. E., … Bhandary, S. V. (2017). Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Computers in Biology and Medicine, 85, 33-42. doi:10.1016/j.compbiomed.2017.04.013 es_ES
dc.description.references Taft, L. M., Evans, R. S., Shyu, C. R., Egger, M. J., Chawla, N., Mitchell, J. A., … Varner, M. (2009). Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery. Journal of Biomedical Informatics, 42(2), 356-364. doi:10.1016/j.jbi.2008.09.001 es_ES
dc.description.references Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341 es_ES
dc.description.references Blagus, R., & Lusa, L. (2015). Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models. BMC Bioinformatics, 16(1). doi:10.1186/s12859-015-0784-9 es_ES
dc.description.references Loughrey, J., & Cunningham, P. (s. f.). Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets. Research and Development in Intelligent Systems XXI, 33-43. doi:10.1007/1-84628-102-4_3 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem