- -

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 completo del ítem

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/145868

Ficheros en el ítem

Metadatos del ítem

Título: Prediction of Labor Induction Success from the Uterine Electrohysterogram
Autor: Benalcazar-Parra, Carlos Ye Lin, Yiyao Garcia-Casado, Javier Monfort-Ortiz, Rogelio Alberola Rubio, José PERALES MARIN, ALFREDO JOSE Prats-Boluda, Gema
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
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
Universitat Politècnica de València. Servicio de Alumnado - Servei d'Alumnat
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Electrical-Activity , Signal , Term , EMG , Delivery , Parameters
Derechos de uso: Reconocimiento (by)
Fuente:
Journal of Sensors. (issn: 1687-725X )
DOI: 10.1155/2019/6916251
Editorial:
Hindawi Limited
Versión del editor: https://doi.org/10.1155/2019/6916251
Código del Proyecto:
info:eu-repo/grantAgreement/MINECO//DPI2015-68397-R/ES/ELECTROHISTEROGRAFIA, CONSTRUYENDO PUENTES PARA SU USO CLINICO EN OBSTETRICIA/
info:eu-repo/grantAgreement/UPV//UPV-FE-2018-B02/ES/PARTOS INDUCIDOS:¿ÉXITO O FRACASO? PREDICCIÓN TEMPRANA MEDIANTE EHG/
info:eu-repo/grantAgreement/GVA//GV%2F2018%2F104/
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/
Agradecimientos:
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 ...[+]
Tipo: Artículo

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

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

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 [+]
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[-]

recommendations

 

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

Mostrar el registro completo del ítem