Maduración pre-inducción de parto. Comparación entre dinoprostona y misoprostol con objetivación de dinámica uterina mediante electrohisterografía: Un enfoque pragmático

La inducción del parto y la maduración cervical son temas de gran interés dentro de la obstetricia, llegando a alcanzar el 30% del total de nacimientos. Además, su inadecuada indicación conlleva una iatrogenia considerable respecto a los resultados neonatales, el aumento de la tasa de cesáreas y los costes. Si bien disponemos de muchos métodos con una efectividad y seguridad contrastada, la investigación en busca del método óptimo para la maduración cervical en cuellos uterinos desfavorables continúa en marcha. Actualmente las prostaglandinas Dinoprostona y Misoprostol son los agentes maduradores de primera elección en nuestro medio. Entre los diferentes estudios publicados, existe heterogeneidad en las dosificaciones, por ejemplo, la dosis óptima y el intervalo de tiempo del misoprostol por vía vaginal no son del todo conocidos. El uso de dosis en dichos estudios es variable, existiendo pocos en lo que se comparan dosis similares a las que se comercializan en el mercado como la de 25 mcg para el misoprostol y de 10 mg para la dinoprostona. En los estudios en los que se ha visto una mayor eficacia del misoprostol, no se ha llegado a justificar la causa exacta de la misma, siendo la más aceptada la producción de una mayor dinámica uterina. La tocografía externa es la técnica más utilizada en la actualidad para determinar la dinámica uterina. Este método no proporciona información muy fiable ya que depende en gran medida del juicio subjetivo del examinador. Como alternativa, para el control de la dinámica uterina se ha propuesto la medición de la actividad mioeléctrica del útero en la superficie abdominal conocido como electrohisterografía (EHG). La literatura ha establecido que la señal de EHG puede proporcionar información fiable y objetiva sobre las contracciones. Por ello podremos objetivar mayor cantidad de parámetros de la dinámica y no solo la frecuencia que nos permitían los tocógrafos convencionales, es decir confirmar si realmente existe una mayor intensidad contráctil si se usa misoprostol en comparación con la dinoprostona. Pocos estudios se centran en la respuesta de la actividad mioeléctrica uterina a los fármacos de inducción del parto, por ello, sigue sin estar claro si los parámetros de la señal de EHG experimentan cambios a través de la inducción del trabajo de parto por parte de las prostaglandinas y si estos parámetros también podrían usarse para desarrollar herramientas para predecir la inducción exitosa en las primeras horas del proceso de inducción. Nuestra hipótesis se centra en el estudio del proceso de maduración cervical y la actividad contráctil uterina en respuesta a la administración de prostaglandinas. Por una parte, presentamos la hipótesis de que al realizar el análisis comparativo entre el misoprostol y la dinoprostona vía vaginal, ambos fármacos presentarían un perfil de eficacia y seguridad similar. Por otra, planteamos la hipótesis de que la dinoprostona y el misoprostol administrados en los embarazos en vías de prolongación, proporcionarían diferentes respuestas en términos de actividad uterina. Podremos evaluar la respuesta electrofisiológica, analizando los parámetros de EHG en las madres embarazadas tratadas con dinoprostona y misoprostol. Esto último con la intención de explorar la posibilidad de predecir el éxito de la inducción a partir de la respuesta electrofisiológica en las primeras horas de la maduración cervical. Nuestro objetivo fue comparar la respuesta de la actividad uterina a la administración de misoprostol (25 mcg) y dinoprostona (10 mg) para la maduración cervical en gestantes con cuellos uterinos desfavorables durante las primeras 4 h en respuesta a los medicamentos de inducción del parto. Además de evaluar la eficacia y seguridad materno-fetal asociada a la administración de cada uno de los fármacos. Un total de 500 pacientes fueron reclutados. Se definieron dos cohortes: misoprostol (comprimidos vaginales de 25 mcg; 251 mujeres) y dinoprostona (dispositivo vaginal de 10 mg; 249 mujeres). La cohorte de misoprostol se asoció con un tiempo más corto para alcanzar periodo activo de parto y parto vaginal y con un mayor porcentaje de parto vaginal en menos de 24 h en las madres con una puntuación cervical muy desfavorable (Índice de Bishop entre 0 y 3). Respecto al análisis con EHG, un conjunto de parámetros temporales, espectrales y de complejidad se calcularon a partir de las ráfagas de EHG. Al analizar las diferencias entre las mujeres que lograron la fase activa del parto y aquellas que no (inducciones exitosas y fallidas), en cuanto a las inducciones exitosas con misoprostol, se obtuvieron incrementos con respecto al período basal para la amplitud de EHG, frecuencia media, índice de actividad uterina (AUI) y Teager, después de 60 minutos de la administración del fármaco. En el caso de las inducciones exitosas con dinoprostona, la duración, amplitud, número de contracciones y AUI presentaron incrementos con respecto al período basal después de 120 minutos. Además, Teager mostró diferencias estadísticamente significativas y sostenidas entre inducciones exitosas y fallidas para el grupo de misoprostol, pero no en el de dinoprostona, probablemente debido a la farmacocinética más lenta de este medicamento. Estos resultados revelaron que EHG podría ser útil para la predicción exitosa de inducción en las primeras etapas de la inducción del parto, especialmente cuando se usa misoprostol. En conclusión, no hay diferencias entre misoprostol y dinoprostona respecto a su perfil de seguridad, que es favorable. El misoprostol vaginal acorta el tiempo hasta parto vaginal en 3 horas en comparación con la dinoprostona vaginal, además favorece el parto vaginal en menos de 24 horas en pacientes con condiciones cervicales muy desfavorables. Al comparar con EHG las inducciones exitosas de las fallidas, con el Misoprostol se distingue la evolución de los parámetros contráctiles a partir del minuto 60-90, mientras que la evolución de la dinoprostona es similar entre el éxito y el fracaso de inducción. Podría ser posible usar los parámetros de EHG para propósitos de predicción y sugieren que podrían proporcionar información valiosa sobre el estado mioeléctrico del útero durante la inducción del parto.Induction of labor and cervical ripening are topics of great interest within obstetrics, reaching 30% of all births. In addition, its inadequate indication leads to a considerable iatrogenia regarding the neonatal results, the increase in the cesarean rate and costs. Although we have many methods with proven effectiveness and safety, research into the optimal method for cervical ripening in unfavorable cervix is ongoing. Currently the prostaglandins Dinoprostone and Misoprostol are the ripening agents of first choice in our environment. Among the different studies published, there is heterogeneity in the dosages, for example, the optimal dose and time interval of vaginal misoprostol are not completely known. The use of doses in these studies is variable, there being few in which doses similar to those marketed in the market are compared, such as 25 mcg for misoprostol and 10 mg for dinoprostone. In studies that have seen a greater efficacy of misoprostol, the exact cause of it has not been justified, the most accepted being the production of greater uterine dynamics. External tocography is the most commonly used technique to determine uterine dynamics. This method does not provide very reliable information since it depends to a large extent on the subjective judgment of the examiner. As an alternative, for the control of uterine dynamics, the measurement of the myoelectric activity of the uterus on the abdominal surface known as electrohysterography (EHG) has been proposed. The literature has established that the EHG signal can provide reliable and objective information about contractions. Therefore, we can objectify more parameters of the dynamics and not only the frequency that conventional tohographs allowed, that is, confirm if there really is a greater contractile intensity if misoprostol is used in comparison with dinoprostone. Few studies focus on the response of uterine myoelectric activity to induction of labor drugs, therefore, it remains unclear if the parameters of the EHG signal undergo changes through the induction of labor with prostaglandins and whether these parameters could also be used to develop tools to predict successful induction in the early hours of the induction process. Our hypothesis focuses on the study of cervical maturation process and uterine contractile activity in response to the administration of prostaglandins. On the one hand, we present the hypothesis that when performing the comparative analysis between vaginal misoprostol and vaginal dinoprostone, both drugs would present a similar efficacy and safety profile. On the other hand, we hypothesized that dinoprostone and misoprostol administered during prolonged pregnancy would provide different responses in terms of uterine activity. We can evaluate the electrophysiological response, analyzing the parameters of EHG in pregnant mothers treated with dinoprostone and misoprostol. The latter with the intention of exploring the possibility of predicting the success of induction from the electrophysiological response in the first hours of cervical ripening. Our objective was to compare the response of uterine activity to the administration of misoprostol (25 mcg) and dinoprostone (10 mg) for cervical ripening in pregnant women with unfavorable cervix during the first 4 h in response to induction of labor. In addition to assess the maternal-fetal efficacy and safety associated with the administration of each of the drugs. A total of 500 patients were recruited. Two cohorts were defined: misoprostol (25 mcg vaginal tablets, 251 women) and dinoprostone (10 mg vaginal device, 249 women). The misoprostol cohort was associated with a shorter time to reach the active period of vaginal delivery and delivery and with a higher percentage of vaginal delivery in less than 24 h in mothers with a very unfavorable cervical score (Bishop Index between 0 and 3). Regarding the analysis with EHG, a set of temporal, spectral and complexity parameters were calculated from the EHG bursts. When analyzing the differences between women who achieved the active phase of delivery and those who did not (successful and failed inductions), in successful inductions with misoprostol, increases were obtained with respect to the baseline period for the EHG amplitude, mean frequency, uterine activity index (AUI) and Teager, after 60 minutes of drug administration. In the case of successful inductions with dinoprostone, the duration, amplitude, number of contractions and UAI showed increases with respect to the baseline period after 120 minutes. In addition, Teager showed statistically significant and sustained differences between successful and failed inductions for the misoprostol group, but not for dinoprostone, probably due to the slower pharmacokinetics of this medication. These results revealed that EHG could be useful for the successful prediction of induction in the early stages of induction of labor, especially when misoprostol is used. In conclusion, there is no difference between misoprostol and dinoprostone with respect to its safety profile, which is favorable. The vaginal misoprostol shortens the time to vaginal delivery in 3 hours compared to the vaginal dinoprostone, also favors vaginal delivery in less than 24 hours in patients with very unfavorable cervical conditions. When comparing successful inductions of failed ones with EHG, with the Misoprostol the evolution of the contractile parameters is distinguished from the 60-90 minute, while the evolution of dinoprostone is similar between success and failure of induction. It may be possible to use the EHG parameters for prediction purposes and suggest that they could provide valuable information about the myoelectric status of the uterus during induction of labor

Prediction of Labor Induction Success from the Uterine Electrohysterogram

[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.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.Benalcazar-Parra, C.; Ye Lin, Y.; Garcia-Casado, J.; Monfort-Ortiz, R.; Alberola Rubio, J.; Perales Marin, AJ.; Prats-Boluda, G. (2019). 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Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

[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.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).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/s21072496S11821

Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity

[EN] The prolonged latent phase of Induction of Labour (IOL) is associated with increased risks of maternal mortality and morbidity. Electrohysterography (EHG) has outperformed traditional clinical measures monitoring labour progress. Although parity is agreed to be of particular relevance to the success of IOL, no previous EHG¿related studies have been found in the literature. We thus aimed to identify EHG¿biomarkers to predict IOL success (active phase of labour in¿¿¿24¿h) and determine the influence of the myoelectrical response on the parity of this group. Statistically significant and sustained differences between the successful and failed groups were found from 150¿min in amplitude and non¿linear parameters, especially in Spectral Entropy and in their progression rates. In the nulliparous¿parous comparison, parous women showed statistically significantly higher amplitude progression rate. These biomarkers would therefore be useful for early detection of the risk of induction failure and would help to develop more robust and generalizable IOL success¿prediction systems.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR and PID2021-124038OB-I00). Funding for open access charge: CRUE-Universitat Politècnica de ValènciaDiaz-Martinez, A.; Monfort-Ortiz, R.; Ye Lin, Y.; Garcia-Casado, J.; Nieto-Tous, M.; Nieto Del-Amor, F.; Diago-Almela, VJ.... (2023). Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity. Biocybernetics and Biomedical Engineering (Online). 43(1):142-156. https://doi.org/10.1016/j.bbe.2022.12.00414215643

Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography

[EN] Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.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) and by the Generalitat Valenciana (AICO/2019/220).Nieto-Del-Amor, F.; Prats-Boluda, G.; Martínez-De-Juan, JL.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.; Ye Lin, Y. (2021). Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography. Sensors. 21(10):1-15. https://doi.org/10.3390/s21103350S115211

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

[EN] One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.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), and by the Generalitat Valenciana (AICO/2019/220)Nieto Del-Amor, F.; Beskhani, R.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.... (2021). Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. Sensors. 21(18):1-17. https://doi.org/10.3390/s21186071S117211

Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios

[EN] Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel-Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.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) and the Generalitat Valenciana (AICO/2019/220 & GV/2018/104)Mas-Cabo, J.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Perales-Marin, A.; Monfort-Ortiz, R.; Roca-Prats, A.... (2020). Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios. Entropy. 22(7):1-15. https://doi.org/10.3390/e22070743S115227Wagura, P., Wasunna, A., Laving, A., Wamalwa, D., & Ng’ang’a, P. (2018). Prevalence and factors associated with preterm birth at kenyatta national hospital. BMC Pregnancy and Childbirth, 18(1). doi:10.1186/s12884-018-1740-2Liu, L., Johnson, H. L., Cousens, S., Perin, J., Scott, S., Lawn, J. E., … Black, R. E. (2012). Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. The Lancet, 379(9832), 2151-2161. doi:10.1016/s0140-6736(12)60560-1Howson, C. P., Kinney, M. V., McDougall, L., & Lawn, J. E. (2013). Born Too Soon: Preterm birth matters. Reproductive Health, 10(S1). doi:10.1186/1742-4755-10-s1-s1Euliano, T. Y., Nguyen, M. T., Darmanjian, S., McGorray, S. P., Euliano, N., Onkala, A., & Gregg, A. R. (2013). Monitoring uterine activity during labor: a comparison of 3 methods. American Journal of Obstetrics and Gynecology, 208(1), 66.e1-66.e6. doi:10.1016/j.ajog.2012.10.873Devedeux, D., Marque, C., Mansour, S., Germain, G., & Duchêne, J. (1993). Uterine electromyography: A critical review. American Journal of Obstetrics and Gynecology, 169(6), 1636-1653. doi:10.1016/0002-9378(93)90456-sChkeir, A., Fleury, M.-J., Karlsson, B., Hassan, M., & Marque, C. (2013). Patterns of electrical activity synchronization in the pregnant rat uterus. BioMedicine, 3(3), 140-144. doi:10.1016/j.biomed.2013.04.007Fele-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-yMas-Cabo, J., Prats-Boluda, G., Perales, A., Garcia-Casado, J., Alberola-Rubio, J., & Ye-Lin, Y. (2018). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing, 57(2), 401-411. doi:10.1007/s11517-018-1888-yVinken, M. P. G. C., Rabotti, C., Mischi, M., & Oei, S. G. (2009). Accuracy of Frequency-Related Parameters of the Electrohysterogram for Predicting Preterm Delivery. Obstetrical & Gynecological Survey, 64(8), 529-541. doi:10.1097/ogx.0b013e3181a8c6b1Hassan, 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.2011.03.010Lemancewicz, 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.2015.11.005Garcia-Casado, J., Ye-Lin, Y., Prats-Boluda, G., Mas-Cabo, J., Alberola-Rubio, J., & Perales, A. (2018). Electrohysterography in the diagnosis of preterm birth: a review. Physiological Measurement, 39(2), 02TR01. doi:10.1088/1361-6579/aaad56Most, O., Langer, O., Kerner, R., Ben David, G., & Calderon, I. (2008). Can myometrial electrical activity identify patients in preterm labor? American Journal of Obstetrics and Gynecology, 199(4), 378.e1-378.e6. doi:10.1016/j.ajog.2008.08.003Verdenik, I., Pajntar, M., & Leskošek, B. (2001). Uterine electrical activity as predictor of preterm birth in women with preterm contractions. European Journal of Obstetrics & Gynecology and Reproductive Biology, 95(2), 149-153. doi:10.1016/s0301-2115(00)00418-8Horoba, K., Jezewski, J., Matonia, A., Wrobel, J., Czabanski, R., & Jezewski, M. (2016). Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybernetics and Biomedical Engineering, 36(4), 574-583. doi:10.1016/j.bbe.2016.06.004Lucovnik, M., Maner, W. L., Chambliss, L. R., Blumrick, R., Balducci, J., Novak-Antolic, Z., & Garfield, R. E. (2011). Noninvasive uterine electromyography for prediction of preterm delivery. American Journal of Obstetrics and Gynecology, 204(3), 228.e1-228.e10. doi:10.1016/j.ajog.2010.09.024Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Maner, W. (2003). Predicting term and preterm delivery with transabdominal uterine electromyography. Obstetrics & Gynecology, 101(6), 1254-1260. doi:10.1016/s0029-7844(03)00341-7Leman, 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:10.1109/10.790499Mischi, M., Chen, C., Ignatenko, T., de Lau, H., Ding, B., Oei, S. G. G., & Rabotti, C. (2018). Dedicated Entropy Measures for Early Assessment of Pregnancy Progression From Single-Channel Electrohysterography. IEEE Transactions on Biomedical Engineering, 65(4), 875-884. doi:10.1109/tbme.2017.2723933Garfield, R. E., Maner, W. L., MacKay, L. B., Schlembach, D., & Saade, G. R. (2005). Comparing uterine electromyography activity of antepartum patients versus term labor patients. American Journal of Obstetrics and Gynecology, 193(1), 23-29. doi:10.1016/j.ajog.2005.01.050Maner, 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/s10439-006-9248-8DIMITROV, G. V., ARABADZHIEV, T. I., MILEVA, K. N., BOWTELL, J. L., CRICHTON, N., & DIMITROVA, N. A. (2006). Muscle Fatigue during Dynamic Contractions Assessed by New Spectral Indices. Medicine & Science in Sports & Exercise, 38(11), 1971-1979. doi:10.1249/01.mss.0000233794.31659.6dNagarajan, R., Eswaran, H., Wilson, J. D., Murphy, P., Lowery, C., & Preißl, H. (2003). Analysis of uterine contractions: a dynamical approach. The Journal of Maternal-Fetal & Neonatal Medicine, 14(1), 8-21. doi:10.1080/jmf.14.1.8.21Zhang, 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:10.1109/10.966601Garfield, 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.xGrotegut, C. A., Paglia, M. J., Johnson, L. N. C., Thames, B., & James, A. H. (2011). Oxytocin exposure during labor among women with postpartum hemorrhage secondary to uterine atony. American Journal of Obstetrics and Gynecology, 204(1), 56.e1-56.e6. doi:10.1016/j.ajog.2010.08.02

A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity

[EN] Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention.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) and the Generalitat Valenciana (GV/2018/104 and AICO/2019/220).Díaz-Martínez, MDA.; Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Cardona-Urrego, K.; Monfort-Ortiz, R.; Lopez-Corral, A.... (2020). A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity. Sensors. 20(11):1-14. https://doi.org/10.3390/s20113023S1142011Ngwenya, S. (2016). Postpartum hemorrhage: incidence, risk factors, and outcomes in a low-resource setting. International Journal of Women’s Health, Volume 8, 647-650. doi:10.2147/ijwh.s119232Carroli, G., Cuesta, C., Abalos, E., & Gulmezoglu, A. M. (2008). Epidemiology of postpartum haemorrhage: a systematic review. Best Practice & Research Clinical Obstetrics & Gynaecology, 22(6), 999-1012. doi:10.1016/j.bpobgyn.2008.08.004Souza, J. P., Gülmezoglu, A. M., Carroli, G., Lumbiganon, P., & Qureshi, Z. (2011). The world health organization multicountry survey on maternal and newborn health: study protocol. BMC Health Services Research, 11(1). doi:10.1186/1472-6963-11-286Knight, M., Callaghan, W. M., Berg, C., Alexander, S., Bouvier-Colle, M.-H., Ford, J. B., … Walker, J. (2009). Trends in postpartum hemorrhage in high resource countries: a review and recommendations from the International Postpartum Hemorrhage Collaborative Group. BMC Pregnancy and Childbirth, 9(1). doi:10.1186/1471-2393-9-55Callaghan, W. M., Kuklina, E. V., & Berg, C. J. (2010). Trends in postpartum hemorrhage: United States, 1994–2006. American Journal of Obstetrics and Gynecology, 202(4), 353.e1-353.e6. doi:10.1016/j.ajog.2010.01.011Marshall, A. L., Durani, U., Bartley, A., Hagen, C. E., Ashrani, A., Rose, C., … Pruthi, R. K. (2017). The impact of postpartum hemorrhage on hospital length of stay and inpatient mortality: a National Inpatient Sample–based analysis. American Journal of Obstetrics and Gynecology, 217(3), 344.e1-344.e6. doi:10.1016/j.ajog.2017.05.004Prick, B. W., Duvekot, J. J., van der Moer, P. E., van Gemund, N., van der Salm, P. C. M., Jansen, A. J. G., … Uyl-de Groot, C. A. (2014). Cost-effectiveness of red blood cell transfusion vs. non-intervention in women with acute anaemia after postpartum haemorrhage. Vox Sanguinis, 107(4), 381-388. doi:10.1111/vox.12181Castiel, D., Bréchat, P.-H., Benoît, B., Nguon, B., Gayat, E., Soyer, P., … Barranger, E. (2008). Coût total des actes chirurgicaux dans la prise en charge des hémorragies de la délivrance. Gynécologie Obstétrique & Fertilité, 36(5), 507-515. doi:10.1016/j.gyobfe.2008.03.009Fukami, T., Koga, H., Goto, M., Ando, M., Matsuoka, S., Tohyama, A., … Tsujioka, H. (2019). Incidence and risk factors for postpartum hemorrhage among transvaginal deliveries at a tertiary perinatal medical facility in Japan. PLOS ONE, 14(1), e0208873. doi:10.1371/journal.pone.0208873Vogel, J. P., Williams, M., Gallos, I., Althabe, F., & Oladapo, O. T. (2019). WHO recommendations on uterotonics for postpartum haemorrhage prevention: what works, and which one? BMJ Global Health, 4(2), e001466. doi:10.1136/bmjgh-2019-001466Lutomski, J., Byrne, B., Devane, D., & Greene, R. (2012). Increasing trends in atonic postpartum haemorrhage in Ireland: an 11-year population-based cohort study. BJOG: An International Journal of Obstetrics & Gynaecology, 119(9), 1150-1151. doi:10.1111/j.1471-0528.2012.03370.xWilmink, F. A., Wilms, F. F., Heydanus, R., Mol, B. W. J., & Papatsonis, D. N. M. (2008). Fetal complications after placement of an intrauterine pressure catheter: A report of two cases and review of the literature. The Journal of Maternal-Fetal & Neonatal Medicine, 21(12), 880-883. doi:10.1080/14767050802220508Hadar, E., Biron-Shental, T., Gavish, O., Raban, O., & Yogev, Y. (2014). A comparison between electrical uterine monitor, tocodynamometer and intra uterine pressure catheter for uterine activity in labor. The Journal of Maternal-Fetal & Neonatal Medicine, 28(12), 1367-1374. doi:10.3109/14767058.2014.954539Alberola-Rubio, J., Prats-Boluda, G., Ye-Lin, Y., Valero, J., Perales, A., & Garcia-Casado, J. (2013). Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics. Medical Engineering & Physics, 35(12), 1736-1743. doi:10.1016/j.medengphy.2013.07.008Euliano, T. Y., Nguyen, M. T., Darmanjian, S., McGorray, S. P., Euliano, N., Onkala, A., & Gregg, A. R. (2013). Monitoring uterine activity during labor: a comparison of 3 methods. American Journal of Obstetrics and Gynecology, 208(1), 66.e1-66.e6. doi:10.1016/j.ajog.2012.10.873Euliano, T. Y., Nguyen, M. T., Marossero, D., & Edwards, R. K. (2007). Monitoring Contractions in Obese Parturients. Obstetrics & Gynecology, 109(5), 1136-1140. doi:10.1097/01.aog.0000258799.24496.93Benalcazar Parra, C., Tendero, A. I., Ye-Lin, Y., Alberola-Rubio, J., Perales Marin, A., Garcia-Casado, J., & Prats-Boluda, G. (2018). Feasibility of Labor Induction Success Prediction based on Uterine Myoelectric Activity Spectral Analysis. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies. doi:10.5220/0006649400700077Euliano, T., Skowronski, M., Marossero, D., Shuster, J., & Edwards, R. (2006). Prediction of intrauterine pressure waveform from transabdominal electrohysterography. The Journal of Maternal-Fetal & Neonatal Medicine, 19(12), 803-808. doi:10.1080/14767050601023657Benalcazar-Parra, C., Garcia-Casado, J., Ye-Lin, Y., Alberola-Rubio, J., Lopez, Á., Perales-Marin, A., & Prats-Boluda, G. (2019). New electrohysterogram-based estimators of intrauterine pressure signal, tonus and contraction peak for non-invasive labor monitoring. Physiological Measurement, 40(8), 085003. doi:10.1088/1361-6579/ab37dbRooijakkers, M. J., Rabotti, C., Oei, S. G., Aarts, R. M., & Mischi, M. (2014). Low-complexity intrauterine pressure estimation using the Teager energy operator on electrohysterographic recordings. Physiological Measurement, 35(7), 1215-1228. doi:10.1088/0967-3334/35/7/1215Schlembach, 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.2009.02.016Fele-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-yHassan, 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.2011.03.010Mas-Cabo, J., Prats-Boluda, G., Perales, A., Garcia-Casado, J., Alberola-Rubio, J., & Ye-Lin, Y. (2018). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing, 57(2), 401-411. doi:10.1007/s11517-018-1888-yLemancewicz, 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.2015.11.005Mas-Cabo, J., Prats-Boluda, G., Garcia-Casado, J., Alberola-Rubio, J., Perales, A., & Ye-Lin, Y. (2019). Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records. Journal of Sensors, 2019, 1-13. doi:10.1155/2019/5373810Acharya, 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.2017.04.013Fergus, 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.0077154Ren, P., Yao, S., Li, J., Valdes-Sosa, P. A., & Kendrick, K. M. (2015). Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLOS ONE, 10(7), e0132116. doi:10.1371/journal.pone.0132116Benalcazar-Parra, C., Ye-Lin, Y., Garcia-Casado, J., Monfort-Ortiz, R., Alberola-Rubio, J., Perales, A., & Prats-Boluda, G. (2019). Prediction of Labor Induction Success from the Uterine Electrohysterogram. Journal of Sensors, 2019, 1-12. doi:10.1155/2019/6916251Sammali, F., Kuijsters, N. P. M., Schoot, B. C., Mischi, M., & Rabotti, C. (2018). Feasibility of Transabdominal Electrohysterography for Analysis of Uterine Activity in Nonpregnant Women. Reproductive Sciences, 25(7), 1124-1133. doi:10.1177/1933719118768700Ye-Lin, Y., Bueno-Barrachina, J. M., Prats-boluda, G., Rodriguez de Sanabria, R., & Garcia-Casado, J. (2017). Wireless sensor node for non-invasive high precision electrocardiographic signal acquisition based on a multi-ring electrode. Measurement, 97, 195-202. doi:10.1016/j.measurement.2016.11.009Maul, H., Maner, W., Olson, G., Saade, G., & Garfield, R. (2004). Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. 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Social Support and Mental Health in the Postpartum Period in Times of SARS-CoV-2 Pandemic: Spanish Multicentre Cohort Study

COVID-19; Anxiety; PregnancyCOVID-19; Ansiedad; EmbarazoCOVID-19; Ansietat; EmbaràsBackground: To explore the depression and anxiety symptoms in the postpartum period during the SARS-CoV-2 pandemic and to identify potential risk factors. Methods: A multicentre observational cohort study including 536 women was performed at three hospitals in Spain. The Edinburgh Postnatal Depression Scale (EPDS), the State-Trait Anxiety Inventory (STAI) Scale, the Medical Outcomes Study Social Support Survey (MOS-SSS), and the Postpartum Bonding Questionnaire (PBQ) were assessed after birth. Depression (EPDS) and anxiety (STAI) symptoms were measured, and the cut-off scores were set at 10 and 13 for EPDS, and at 40 for STAI. Results: Regarding EPDS, 32.3% (95% CI, 28% to 36.5%) of women had a score ≥ 10, and 17.3% (95% CI, 13.9% to 20.7%) had a score ≥ 13. Women with an STAI score ≥ 40 accounted for 46.8% (95% CI, 42.3% to 51.2%). A lower level of social support (MOS-SSS), a fetal malformation diagnosis and a history of depression (p = 0.000, p = 0.019 and p = 0.043) were independent risk factors for postpartum depression. A lower level of social support and a history of mental health disorders (p = 0.000, p = 0.003) were independent risk factors for postpartum anxiety. Conclusion: During the SARS-CoV-2 pandemic, an increase in symptoms of anxiety and depression were observed during the postpartum period

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models&rsquo; real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 &plusmn; 4.6%, average precision of 84.5 &plusmn; 11.7%, maximum F1-score of 79.6 &plusmn; 13.8%, and recall of 89.8 &plusmn; 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics