Caracterización y utilidad de la electromiografía uterina en diferentes escenarios obstétricos: partos inducidos y estimación de presión intrauterina

[ES] La monitorización de la frecuencia cardíaca fetal y de la actividad uterina es una práctica clínica habitual para obtener información del estado del feto durante el embarazo y el parto. Para la monitorización de la dinámica uterina tradicionalmente se han empleado técnicas como la tocodinamometría (TOCO) y la medida de la presión intrauterina mediante catéter. Sin embargo, ambas técnicas presentan limitaciones que hacen que se requiera la búsqueda de otras alternativas. En este sentido para solventar los problemas relacionados con estas técnicas se plantea el registro de la señal electrohisterográfica (EHG) como una alternativa para monitorizar de forma precisa y no invasiva la actividad mioeléctrica uterina. La técnica ha sido ampliamente estudiada en diferentes condiciones obstétricas como es el caso de la predicción del parto prematuro y en la detección de contracciones de parto; y unos pocos en la predicción del éxito de la inducción del parto y en la estimación de la presión intrauterina. A pesar de que el registro EHG ha demostrado que aporta información relevante sobre las propiedades bioeléctricas del útero, existen pocos estudios sobre la respuesta mioeléctrica uterina a los medicamentos empleados en la inducción del parto que puedan servir como herramienta de ayuda en la predicción del resultado de la inducción del parto. En la presente tesis se abordó este problema mediante dos objetivos generales: 1) caracterizar la respuesta electrofisiológica uterina a los fármacos de inducción del parto a partir de registros EHG y 2) desarrollar y valorar sistemas de ayuda al diagnóstico para predecir el éxito de inducción del parto. Los resultados del primer objetivo revelaron una diferente evolución de los parámetros EHG entre los grupos de éxito y fracaso, revelando que podría ser útil para una predicción de inducción exitosa en las primeras etapas de la inducción, especialmente cuando se usa misoprostol. Para el segundo objetivo se diseñaron sistemas predictores del éxito de la inducción del parto mediante técnicas de machine learning valorando su capacidad predictora. Los resultados mostraron que el EHG puede usarse potencialmente para predecir la inducción exitosa del parto y supera al uso de las características obstétricas tradicionales. El uso clínico del sistema de predicción propuesto ayudaría a mejorar el bienestar materno-fetal y optimizar los recursos hospitalarios. Por otra parte, en la presente tesis también se abordó el registro EHG como una técnica no invasiva para la estimación de la presión intrauterina. Diversos estudios han intentado estimar la señal IUP a partir de parámetros extraídos de la señal EHG. A pesar de estos esfuerzos, existen limitaciones no abordadas específicamente en dichos estudios como es el caso de la gran variabilidad entre pacientes. Por ello, se propuso mejorar la estimación de presión uterina reportada en la literatura mediante un enfoque de interés clínico y abordando la problemática de la variabilidad entre pacientes. Se diseñaron modelos para la estimación de IUP, utilizando diferentes tipos de criterios de optimización y se desarrollaron modelos individuales (mono-paciente) y globales (con el conjunto de pacientes). Finalmente, se abordó el problema de la variabilidad entre sujetos mediante el desarrollo de algoritmos adaptativos para mejorar la exactitud de las estimaciones de IUP derivadas de los modelos globales. Los modelos adaptativos desarrollados superaron los modelos globales, proporcionando un mejor balance para estimar la señal continua de IUP, el tono y la máxima presión. Los modelos de estimación de IUP basados en EHG propuestos en la presente tesis permiten una monitorización no invasiva de la actividad uterina más precisa y, por lo tanto, una mejor evaluación del progreso del parto y del bienestar materno y fetal.[EN] Monitoring fetal heart rate and uterine activity is a common clinical practice to obtain information on the status of the fetus during pregnancy and delivery. Techniques such as tocodynamometry (TOCO) and measurement of intrauterine pressure using a catheter have traditionally been used to monitor uterine dynamics. However, both techniques have limitations that require the search for other alternatives. In this sense, to solve the problems related to these techniques, the recording of the electrohysterographic signal (EHG) is proposed as an alternative to monitor uterine myoelectrical activity accurately and noninvasively. The technique has been extensively studied in different obstetric conditions, such as the prediction of preterm labor and the detection of labor contractions; and a few in predicting the success of labor induction and in estimating intrauterine pressure. Despite the fact that the EHG record has been shown to provide relevant information on the bioelectric properties of the uterus, there are few studies on the uterine myoelectrical response to the medications used to induce labor that can serve as a tool to help predict the outcome of induction of labor. In the present thesis, this problem was addressed through two general objectives: 1) to characterize the uterine electrophysiological response to labor induction drugs from EHG records and 2) to develop and assess diagnostic aid systems to predict the success of induction of labor. The results of the first objective revealed a different evolution of the EHG parameters between the success and failure groups, revealing that it could be useful for a successful induction prediction in the early stages of induction, especially when misoprostol is used. For the second objective, predictive systems for the success of labor induction were designed using machine learning techniques, evaluating its predictive capacity. The results showed that EHG can potentially be used to predict successful induction of labor and outperforms the use of traditional obstetric features. The clinical use of the proposed prediction system would help improve maternal-fetal well-being and optimize hospital resources. On the other hand, in this thesis, EHG recording was also addressed as a non-invasive technique for estimating intrauterine pressure. Various studies have attempted to estimate the IUP signal from parameters extracted from the EHG signal. Despite these efforts, there are limitations not specifically addressed in these studies, such as the great variability between patients. Therefore, it was proposed to improve the estimation of uterine pressure reported in the literature using an approach of clinical interest and addressing the problem of variability between patients. Models were designed for the estimation of IUP, using different types of optimization criteria, and individual (single-patient) and global models (with the set of patients) were developed. Finally, the problem of variability between subjects was addressed through the development of adaptive algorithms to improve the accuracy of IUP estimates derived from global models. The adaptive models developed outperformed the global models, providing better balance to estimate continuous IUP signal, tonus, and maximum pressure. The EHG-based IUP estimation models proposed in this thesis allow more precise non-invasive monitoring of uterine activity and, therefore, a better evaluation of labor progress and maternal and fetal well-being[CA] La monitorització de la freqüència cardíaca fetal i de l'activitat uterina és una pràctica clínica habitual per a obtindre informació de l'estat del fetus durant l'embaràs i el part. Per a la monitorització de la dinàmica uterina tradicionalment s'han empleat tècniques com la tocodinamometría (TOQUE) i la mesura de la pressió intrauterina per mitjà de catèter. No obstant això, ambdós tècniques presenten limitacions que fan que es requerisca la busca d'altres alternatives. En este sentit per a resoldre els problemes relacionats amb estes tècniques es planteja el registre del senyal electrohisterográfica (EHG) com una alternativa per a monitoritzar de forma precisa i no invasiva l'activitat mioeléctrica uterina. La tècnica ha sigut àmpliament estudiada en diferents condicions obstétricas com és el cas de la predicció del part prematur i en la detecció de contraccions de part; i uns pocs en la predicció de l'èxit de la inducció del part i en l'estimació de la pressió intrauterina. A pesar que el registre EHG ha demostrat que aporta informació rellevant sobre les propietats bioeléctricas de l'úter, hi ha pocs estudis sobre la resposta mioeléctrica uterina als medicaments empleats en la inducció del part que puguen servir com a ferramenta d'ajuda en la predicció del resultat de la inducció del part. En la present tesi es va abordar este problema per mitjà de dos objectius generals: 1) caracteritzar la resposta electrofisiològica uterina als fàrmacs d'inducció del part a partir de registres EHG i 2) desenrotllar i valorar sistemes d'ajuda al diagnòstic per a predir l'èxit d'inducció del part. Els resultats del primer objectiu van revelar una diferent evolució dels paràmetres EHG entre els grups d'èxit i fracàs, revelant que podria ser útil per a una predicció d'inducció exitosa en les primeres etapes de la inducció, especialment quan s'usa misoprostol. Per al segon objectiu es van dissenyar sistemes predictors de l'èxit de la inducció del part per mitjà de tècniques de machine learning valorant la seua capacitat predictora. Els resultats van mostrar que l'EHG pot usar-se potencialment per a predir la inducció exitosa del part i supera a l'ús de les característiques obstétricas tradicionals. L'ús clínic del sistema de predicció proposat ajudaria a millorar el benestar matern-fetal i optimitzar els recursos hospitalaris. D'altra banda, en la present tesi també es va abordar el registre EHG com una tècnica no invasiva per a l'estimació de la pressió intrauterina. Diversos estudis han intentat estimar el senyal IUP a partir de paràmetres extrets del senyal EHG. A pesar d'estos esforços, hi ha limitacions no abordades específicament en els dits estudis com és el cas de la gran variabilitat entre pacients. Per això, es va proposar millorar l'estimació de pressió uterina reportada en la literatura per mitjà d'un enfocament d'interés clínic i abordant la problemàtica de la variabilitat entre pacients. Es van dissenyar models per a l'estimació d'IUP, utilitzant diferents tipus de criteris d'optimització i es van desenrotllar models individuals (mona-pacient) i globals (amb el conjunt de pacients). Finalment, es va abordar el problema de la variabilitat entre subjectes per mitjà del desenrotllament d'algoritmes adaptatius per a millorar l'exactitud de les estimacions d'IUP derivades dels models globals. Els models adaptatius desenrotllats van superar els models globals, proporcionant un millor balanç per a estimar el senyal continu d'IUP, el to i la màxima pressió. Els models d'estimació d'IUP basats en EHG proposats en la present tesi permeten una monitorització no invasiva de l'activitat uterina més precisa i, per tant, una millor avaluació del progrés del part i del benestar matern i fetal.Benalcazar Parra, CA. (2020). Caracterización y utilidad de la electromiografía uterina en diferentes escenarios obstétricos: partos inducidos y estimación de presión intrauterina [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149403TESI

Detecting bearing failures in wind energy parks: A main bearing early damage detection method using SCADA data and a convolutional autoencoder

Wind energy maintenance and operation costs can total millions of dollars each year in an average industrial-size wind park. Therefore, moving from preventive and corrective maintenance to predictive maintenance is imperative in the wind energy sector. This paper contributes to this challenge by providing a main bearing early damage detection technique that exclusively uses standard supervisory control and data acquisition (SCADA) data (10-min average) and a convolutional autoencoder with the following contributions. (i) Entirely semisupervised (not requiring the labeling of data through work order logs and avoiding the problem of data imbalance between classes) based only on healthy data, thus expanding its range of application (even when the failure of interest has never occurred in the park before). (ii) Validated using real-world SCADA data and shown to be resistant to seasonality, and operational and environmental conditions. (iii) Reliable predictions with minimum false alarms thanks to specially designed fault prognosis indicators based on the image mean square error metric. (vi) The early warning is achieved months in advance, thus providing adequate time for plant operators to plan properly. (v) The main use of exogenous variables in the model (variables that are not affected by other variables, e.g., wind speed, wind turbulence, and ambient temperature) guarantees the detection of damage directly related only to the low-speed shaft temperature (the only nonexogenous variable used by the stated model). (vi) Finally, the proposed strategy is validated in a wind park made up of 12 wind turbines.Postprint (published version

New electrohysterogram-based estimators of intrauterine pressure signal, tonus and contraction peak for non-invasive labor monitoring

[EN] Background: Uterine activity monitoring is an essential part of managing the progress of pregnancy and labor. Although intrauterine pressure (IUP) is the only reliable method of estimating uterine mechanical activity, it is highly invasive. Since there is a direct relationship between the electrical and mechanical activity of uterine cells, surface electrohysterography (EHG) has become a noninvasive monitoring alternative. The Teager energy (TE) operator of the EHG signal has been used for IUP continuous pressure estimation, although its accuracy could be improved. We aimed to develop new optimized IUP estimation models for clinical application. Approach: We first considered enhancing the optimal estimation of IUP clinical features (maximum pressure and tonus) rather than optimizing the signal only (continuous pressure). An adaptive algorithm was also developed to deal with inter-patient variability. For each optimizing signal feature (continuous pressure, maximum pressure and tonus), individual (single patient), global (full database) and adaptive models were built to estimate the recorded IUP signal. The results were evaluated by computing the root mean square errors (RMSe): continuous pressure error (CPe), maximum pressure error (MPe) and tonus error (TOe). Main results: The continuous pressure global model yielded IUP estimates with Cpe = 14.61mm Hg, MPe = 29.17mm Hg and Toe = 7.8mm Hg. The adaptive models significantly reduced errors to CPe = 11.88, MPe = 16.02 and Toe = 5.61mm Hg. The EHG-based IUP estimates outperformed those from traditional tocographic recordings, which had significantly higher errors (CPe = 21.93, MPe = 26.97, and TOe = 13.96). Significance: Our results show that adaptive models yield better IUP estimates than the traditional approaches and provide the best balance of the different errors computed for a better assessment of the labor progress and maternal and fetal wellbeing.This research project was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (DPI2015-68397-R), and by the projects UPV_ FE-2018-C03 and GV/2018/104.Benalcazar-Parra, C.; Garcia-Casado, J.; Ye Lin, Y.; Alberola-Rubio, J.; López-Corral, A.; Perales Marin, AJ.; 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):1-12. https://doi.org/10.1088/1361-6579/ab37dbS11240

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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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_

Comparison of labour induction with misoprostol and dinoprostone and characterization of uterine response based on electrohysterogram

[EN] Objective: The objective of this study is to compare the uterine activity response between women administered dinoprostone (prostaglandin E2) and misoprostol (prostaglandin E1) for induction of labour (IOL) by analysing not only the traditional obstetric data but also the parameters extracted from uterine electrohysterogram (EHG). Methods: Two cohorts were defined: misoprostol (25-mg vaginal tablets; 251 women) and dinoprostone cohort (10 mg vaginal inserts; 249 women). All the mothers were induced by a medical indication of a Bishop Score < ¿ 6. Results: The misoprostol cohort was associated with a shorter time to achieve active labour (p ¿ .017) and vaginal delivery (p ¿ .009) and with a higher percentage of vaginal delivery in less than 24 h in mothers with a very unfavourable cervix score (risk ratio (RR): 1.41, IC95% 1.17¿1.69, p ¿ .002). Successful inductions with misoprostol showed EHG parameter values significantly higher than basal state for amplitude and pseudo Montevideo units (PMU) 60¿ after drug administration, while spectral parameters significantly increased after 150¿. This response was not observed in failed inductions. In the successful dinoprostone group, the duration and number of contractions increased significantly after 120¿, PMU did so after 180¿, and no significant differences were found for spectral parameters, possibly due to the slower pharmacokinetics of this drug. Conclusion: Successful inductions of labour by misoprostol are associated with earlier effective contractions than in labours induced by dinoprostone.This work was partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under grant [DPI2015-68397-R] and by the company Bial SA.Benalcazar-Parra, C.; Monfort-Orti, R.; Ye Lin, Y.; Prats-Boluda, G.; Alberola Rubio, J.; Perales Marín, AJ.; Garcia-Casado, J. (2019). 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. https://doi.org/10.1080/14767058.2017.1410791S15861594321

Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs

[EN] Labor induction is a common practice to promote uterine contractions and labor onset. Uterine electrohysterogram (EHG) has proved its suitability for characterizing the uterus electrophysiological condition in women with spontaneous labor. The aim of this study was to characterize and compare uterine myoelectrical activity during the first 4h in response to labor induction drugs, Misoprostol (G1) and Dinoprostone (G2), by analyzing the differences between women who achieved active phase of labor and those who did not (successful and failed inductions). A set of temporal, spectral and complexity parameters were computed from the EHG-bursts. As for successful inductions, statistical significant and sustained increases with respect to basal period were obtained for EHG amplitude, mean frequency, uterine activity index (UAI) and Teager, after 60¿ for the G1 group; duration, amplitude, number of contractions and UAI for the G2 group, after 120¿. Moreover, Teager showed statistical significant and sustained differences between successful and failed inductions (1.43±1.45 µV2.Hz2.105 vs. 0.40±0.26 µV2.Hz2.105 after 240¿) for the G1 group, but not in the G2 group, probably due to the slower pharmacokinetics of this drug. These results revealed that EHG could be useful for successful induction prediction in the early stages of induction, especially when using Misoprostol.This research project was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (DPI2015-68397-R) and by the company Bial SA.Benalcazar-Parra, C.; Ye Lin, Y.; Garcia Casado, J.; Monfort-Orti, R.; Alberola Rubio, J.; Perales Marín, AJ.; Prats-Boluda, G. (2018). Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Medical Engineering & Physics. 56:27-35. https://doi.org/10.1016/j.medengphy.2018.04.002S27355

Potencial de la gramática del MuSIASEM en la representación del análisis de la sostenibilidad

Una alta capacidad para representar el análisis de la sostenibilidad es la exigencia central de cualquier metodología que pretenda encontrar en dicho análisis una herramienta para el desarrollo social y un diagnóstico de relación de las sociedades humanas con el entorno. Los requerimientos de dicha capacidad representadora se cifran en un lenguaje lo suficientemente recursivo y sintético en sus rasgos y principios como para ser flexible y en ello ofrecer pertinencia y operacionalidad para diversidad de escenarios y situaciones que, en todo caso, se caracterizan por la complejidad del tejido entre sus múltiples niveles y dimensiones. A través de una gramática el MUSIASEM ha cifrado en ello su potencial analítico y predictivo. El presente artículo argumenta las ventajas y alcances de las gramáticas como alternativa a los modelos en la representación del análisis de la sostenibilidad, explica cómo está fundamentado y construido el caso de la gramática del MUSIASEM y expone aplicaciones ya elaboradas a los temas de alimentos, suelos, energía, construcción y energía