Diseño y desarrollo de un sistema para el registro y monitorización de la actividad mioeléctrica uterina

El nacimiento prematuro está entre los mayores problemas de salud en el mundo. Las técnicas usadas actualmente para la monitorización del parto son poco fiables y subjetivas.El objetivo de este proyecto es el desarrollo de un sistema que permita el registro de la actividad electrica uterina junto con las señales de uso clínco habitual.Alberola Rubio, J. (2010). Diseño y desarrollo de un sistema para el registro y monitorización de la actividad mioeléctrica uterina. http://hdl.handle.net/10251/14211Archivo delegad

Estudio electrofisiológico del útero humano durante el embarazo a partir de registros no invasivos del electrohisterograma

Título: “Estudio electrofisiológico del útero humano durante el embarazo a partir de registros no invasivos del electrohisterograma” La actividad uterina se monitoriza durante el embarazo y el parto con el objetivo de obtener información sobre las contracciones uterinas, para ayudar a la estimación del inicio y el progreso del trabajo de parto y para evaluar el estado de salud del conjunto materno-fetal. Actualmente, las tocografía externa e interna, son las técnicas más extendidas para la monitorización de la actividad uterina. Éstas técnicas manométricas de uso clínico habitual se limitan a monitorizar las contracciones uterinas a partir de resultante mecánica de la actividad bioeléctrica contráctil. Además, la tocografía externa aporta información poco fiable y dependiente de la subjetividad del examinador. Recientemente, el análisis de la actividad mioeléctrica uterina captada en superficie (electrohisterograma, EHG) ha demostrado ser uno de los marcadores biofísicos más prometedores no sólo de la dinámica uterina sino también del estado electrofisiológico del útero. Sin embargo, a pesar de haberse demostrando que el EHG aporta información muy valiosa, no ha habido una traslación de esta técnica al uso clínico. Introducir el EHG en la práctica clínica requiere atender las principales demandas de los obstetras, como son: simplificar los protocolos y sistemas de adquisición de señal, facilitar la interpretación de la información adquirida y verificar su utilidad en escenarios clínicos. Por ello, en esta Tesis Doctoral se ha comparado la capacidad de detección de la actividad contráctil uterina de los sistemas utilizados en la práctica clínica habitual, respecto de diferentes configuraciones de registros de EHG en superficie abdominal y con el objetivo de acercar lo máximo posible la electrohisterografía a la clínica, se ha desarrollado electrodos más cómodos y de mejor contacto, un monitor obstétrico de EHG amigable en entorno clínico, se ha estudiado la generación de señales fácilmente interpretables por el personal clínico, se han implementado sistemas automáticos de detección de artefactos para así evitar la confusión en la interpretación de las mismas y sistemas de diagnóstico automáticos para la predicción de parto en menos de 24h

Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records

[EN] Preterm labor is one of the major causes of neonatal deaths and also the cause of significant health and development impairments in those who survive. However, there are still no reliable and accurate tools for preterm labor prediction in clinical settings. Electrohysterography (EHG) has been proven to provide relevant information on the labor time horizon. Many studies focused on predicting preterm labor by using temporal, spectral, and nonlinear parameters extracted from single EHG recordings. However, multichannel analysis, which includes information from the whole uterus and about coupling between the recording areas, may provide better results. The cross validation method is often used to design classifiers and evaluate their performance. However, when the validation dataset is used to tune the classifier hyperparameters, the performance metrics of this dataset may not properly assess its generalization capacity. In this work, we developed and compared different classifiers, based on artificial neural networks, for predicting preterm labor using EHG features from single and multichannel recordings. A set of temporal, spectral, nonlinear, and synchronization parameters computed from EHG recordings was used as the input features. All the classifiers were evaluated on independent test datasets, which were never ¿seen¿ by the models, to determine their generalization capacity. Classifiers¿ performance was also evaluated when obstetrical data were included. The experimental results show that the classifier performance metrics were significantly lower in the test dataset (AUC range 76-91%) than in the train and validation sets (AUC range 90-99%). The multichannel classifiers outperformed the single-channel classifiers, especially when information was combined into mean efficiency indexes and included coupling information between channels. Including obstetrical data slightly improved the classifier metrics and reached an AUC of for the test dataset. These results show promise for the transfer of the EHG technique to preterm labor prediction in clinical practice.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (DPI2015-68397-R, MINECO/FEDER, and RTI2018-094449-A-I00-AR); Generalitat Valenciana (AICO/2019/220); and the VLC/Campus (UPV-FE-2018-B03).Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Alberola Rubio, J.; Perales Marín, AJ.; 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. https://doi.org/10.1155/2019/5373810S1132019Goldenberg, R. L., Culhane, J. F., Iams, J. D., & Romero, R. (2008). Epidemiology and causes of preterm birth. 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Computers in Biology and Medicine, 85, 33-42. doi:10.1016/j.compbiomed.2017.04.013Fergus, P., Idowu, I., Hussain, A., & Dobbins, C. (2016). Advanced artificial neural network classification for detecting preterm births using EHG records. Neurocomputing, 188, 42-49. doi:10.1016/j.neucom.2015.01.107Ren, 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.0132116Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159. doi:10.1016/s0031-3203(96)00142-2Maner, 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/s10439-006-9248-8Smrdel, A., & Jager, F. (2015). 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(2015). ANN, SVM and KNN Classifiers for Prognosis of Cardiac Ischemia- A Comparison. Bonfring International Journal of Research in Communication Engineering, 5(2), 07-11. doi:10.9756/bijrce.8030Ren, J. (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 26, 144-153. doi:10.1016/j.knosys.2011.07.016Maul, 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. The Journal of Maternal-Fetal & Neonatal Medicine, 15(5), 297-301. doi:10.1080/14767050410001695301Mas-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. 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Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics

Non-invasive recording of uterine myoelectric activity (electrohysterogram, EHG) could provide an alternative to monitoring uterine dynamics by systems based on tocodynamometer (TOCO). Laplacian recording of bioelectric signals has been shown to give better spatial resolution and less interference than mono and bipolar surface recordings. The aim of this work was to study the signal quality obtaines from monopolar, bipolar and Laplacian techniques in EHG recordings, as well as to assess their ability to detect uterine contractions. Twenty-two recording sessions were carried out on singleton pregnant women during the active phase of labour. In each session the following simultaneous recordings were obtained: internal uterine pressure (IUP), external tension of abdominal wall (TOCO) and EHG signals (5 monopolar and 4 bipolar recordings, 1 discrete aproximation to the Laplacian of the potential and 2 estimates of the Laplacian from two active annular electrodes). The results obtained show that EHG is able to detect a higher number of uterine contractions than TOCO. Laplacian recordings give improved signal quality over monopolar and bipolar techniques, reduce maternal cardiac interference and improve the signal-to-noise ratio. The optimal position for recording EHG was found to be the uterine median axis and the lower centre-right umbilical zone.Research partly supported by the Spanish Ministerio de Ciencia y Tecnologia (TEC2010-16945) and the Universitat Politecnica de Valencia (PAID 2009/10-2298). The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain.Alberola Rubio, J.; Prats Boluda, G.; Ye Lin, Y.; Valero, J.; Perales Marin, AJ.; Garcia Casado, FJ. (2013). Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics. Medical Engineering and Physics. 35(12):1736-1743. https://doi.org/10.1016/j.medengphy.2013.07.008S17361743351

Uterine contractile efficiency indexes for labor prediction: a bivariate approach from multichannel electrohysterographic records

[EN] Labor prediction is one of the most challenging goals in obstetrics, mainly due to the poor understanding of the factors responsible for the onset of labor. The electrohysterogram (EHG) is the recording of the myoelectrical activity of myometrial cells and has been shown to provide relevant information on the electrophysiological state of the uterus. This information could be used to obtain more accurate labor predictions than those of the currently used techniques, such as the Bishop score, tocography or biochemical markers. Indeed, a number of efforts have already been made to predict labor by this method, separately characterizing the intensity, the coupling degree of the EHG signals and myometrial cell excitability, these being the cornerstones on which contraction efficiency is built. Although EHG characterization can distinguish between different obstetric situations, the reported results have not been shown to provide a practical tool for the clinical detection of true labor. The aim of this work was thus to define and calculate indexes from multichannel EHG recordings related to all the phenomena involved in the efficiency of uterine myoelectrical activity (intensity, excitability and synchronization) and to combine them to form global efficiency indexes (GEI) able to predict delivery in less than 7/14 days. Four EHG synchronization indexes were assessed: linear correlation, the imaginary part of the coherence, phase synchronization and permutation cross mutual information. The results show that even though the synchronization and excitability efficiency indexes can detect increasing trends as labor approaches, they cannot predict labor in less than 7/14 days. However, intensity seems to be the main factor that contributes to myometrial efficiency and is able to predict labor in less than 7/14 days. All the GEls present increasing monotonic trends as pregnancy advances and are able to identify (p < 0.05) patients who will deliver in less than 7/14 days better than single channel and single phenomenon parameters. The GEI based on the permutation cross mutual information shows especially promising results. A simplified EHG recording protocol is proposed here for clinical practice, capable of predicting deliveries in less than 7/14 days, consisting of 4 electrodes vertically aligned with the median line of the uterus. (C) 2018 Elsevier Ltd. All rights reserved.The authors are grateful to Zhenhu Liang, of the Yanshan University, who provided essential information for computing the PLV and NPCMI synchronization indexes. This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (DPI2015-68397-R, MINECO/FEDER).Mas-Cabo, J.; Ye Lin, Y.; Garcia-Casado, J.; Alberola Rubio, J.; Perales Marín, AJ.; Prats-Boluda, G. (2018). Uterine contractile efficiency indexes for labor prediction: a bivariate approach from multichannel electrohysterographic records. Biomedical Signal Processing and Control. 46:238-248. https://doi.org/10.1016/j.bspc.2018.07.018S2382484

Electrohysterography in the diagnosis of preterm birth: a review

This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at http://doi.org/10.1088/1361-6579/aaad56.[EN] Preterm birth (PTB) is one of the most common and serious complications in pregnancy. About 15 million preterm neonates are born every year, with ratios of 10-15% of total births. In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period. Diagnostic means for its timely risk assessment are lacking and the underlying physiological mechanisms are unclear. Surface recording of the uterine myoelectrical activity (electrohysterogram, EHG) has emerged as a better uterine dynamics monitoring technique than traditional surface pressure recordings and provides information on the condition of uterine muscle in different obstetrical scenarios with emphasis on predicting preterm deliveries. Objective: A comprehensive review of the literature was performed on studies related to the use of the electrohysterogram in the PTB context. Approach: This review presents and discusses the results according to the different types of parameter (temporal and spectral, non-linear and bivariate) used for EHG characterization. Main results: Electrohysterogram analysis reveals that the uterine electrophysiological changes that precede spontaneous preterm labor are associated with contractions of more intensity, higher frequency content, faster and more organized propagated activity and stronger coupling of different uterine areas. Temporal, spectral, non-linear and bivariate EHG analyses therefore provide useful and complementary information. Classificatory techniques of different types and varying complexity have been developed to diagnose PTB. The information derived from these different types of EHG parameters, either individually or in combination, is able to provide more accurate predictions of PTB than current clinical methods. However, in order to extend EHG to clinical applications, the recording set-up should be simplified, be less intrusive and more robust-and signal analysis should be automated without requiring much supervision and yield physiologically interpretable results. Significance: This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under grant DPI2015-68397-R.Garcia-Casado, J.; Ye Lin, Y.; Prats-Boluda, G.; Mas-Cabo, J.; Alberola Rubio, J.; Perales Marin, AJ. (2018). Electrohysterography in the diagnosis of preterm birth: a review. Physiological Measurement. 39(2). https://doi.org/10.1088/1361-6579/aaad56S39

Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography?

Background and objective Induction of labor (IOL) is a medical procedure used to initiate uterine contractions to achieve delivery. IOL entails medical risks and has a significant impact on both the mother's and newborn's well-being. The assistance provided by an automatic system to help distinguish patients that will achieve labor spontaneously from those that will need late-term IOL would help clinicians and mothers to take an informed decision about prolonging pregnancy. With this aim, we developed and evaluated predictive models using not only traditional obstetrical data but also electrophysiological parameters derived from the electrohysterogram (EHG). Methods EHG recordings were made on singleton term pregnancies. A set of 10 temporal and spectral parameters was calculated to characterize EHG bursts and a further set of 6 common obstetrical parameters was also considered in the predictive models design. Different models were implemented based on single layer Support Vector Machines (SVM) and with aggregation of majority voting of SVM (double layer), to distinguish between the two groups: term spontaneous labor (&#8804;41 weeks of gestation) and IOL late-term labor. The areas under the curve (AUC) of the models were compared. Results The obstetrical and EHG parameters of the two groups did not show statistically significant differences. The best results of non-contextualized single input parameter SVM models were achieved by the Bishop Score (AUC =&#8201;0.65) and GA at recording time (AUC =&#8201;0.68) obstetrical parameters. The EHG parameter median frequency, when contextualized with the two obstetrical parameters improved these results, reaching AUC =&#8201;0.76. Multiple input SVM obtained AUC =&#8201;0.70 for all EHG parameters. Aggregation of majority voting of SVM models using contextualized EHG parameters achieved the best result AUC =&#8201;0.93. Conclusions Measuring the electrophysiological uterine condition by means of electrohysterographic recordings yielded a promising clinical decision support system for distinguishing patients that will spontaneously achieve active labor before the end of full term from those who will require late term IOL. The importance of considering these EHG measurements in the patient's individual context was also shown by combining EHG parameters with obstetrical parameters. Clinicians considering elective labor induction would benefit from this technique.General Electric HealthcareAlberola Rubio, J.; Garcia Casado, FJ.; Prats-Boluda, G.; Ye Lin, Y.; Desantes, D.; Valero, J.; Perales Marin, AJ. (2017). Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography?. Computer Methods and Programs in Biomedicine. 144:127-133. https://doi.org/10.1016/j.cmpb.2017.03.018S12713314

Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions

Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique

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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