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

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. The Lancet, 371(9606), 75-84. doi:10.1016/s0140-6736(08)60074-4Zeitlin, J., Szamotulska, K., Drewniak, N., Mohangoo, A., Chalmers, J., … Sakkeus, L. (2013). Preterm birth time trends in Europe: a study of 19 countries. BJOG: An International Journal of Obstetrics & Gynaecology, 120(11), 1356-1365. doi:10.1111/1471-0528.12281Fitzpatrick, K., Tuffnell, D., Kurinczuk, J., & Knight, M. (2016). Pregnancy at very advanced maternal age: a UK population-based cohort study. BJOG: An International Journal of Obstetrics & Gynaecology, 124(7), 1097-1106. doi:10.1111/1471-0528.14269Haas, D., Benjamin, T., Sawyer, R., & Quinney, S. (2014). Short-term tocolytics for preterm delivery &ndash; current perspectives. International Journal of Women’s Health, 343. doi:10.2147/ijwh.s44048Garfield, 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.xDevedeux, 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.2013.04.007Fuchs, A.-R., Fuchs, F., Husslein, P., & Soloff, M. S. (1984). Oxytocin receptors in the human uterus during pregnancy and parturition. American Journal of Obstetrics and Gynecology, 150(6), 734-741. doi:10.1016/0002-9378(84)90677-xTezuka, N., Ali, M., Chwalisz, K., & Garfield, R. E. (1995). Changes in transcripts encoding calcium channel subunits of rat myometrium during pregnancy. 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Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-yDiab, A., Hassan, M., Marque, C., & Karlsson, B. (2014). Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals. Medical Engineering & Physics, 36(6), 761-767. doi:10.1016/j.medengphy.2014.01.009Lucovnik, 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.2010.09.024Acharya, 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., 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). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Aditya, S., & Tibarewala, D. N. (2012). Comparing ANN, LDA, QDA, KNN and SVM algorithms in classifying relaxed and stressful mental state from two-channel prefrontal EEG data. International Journal of Artificial Intelligence and Soft Computing, 3(2), 143. doi:10.1504/ijaisc.2012.049010Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002Li, Z., Zhang, Q., & Zhao, X. (2017). Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries. International Journal of Distributed Sensor Networks, 13(9), 155014771773339. doi:10.1177/1550147717733391Murthy, H. S. N., & Meenakshi, D. M. (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. Medical & Biological Engineering & Computing, 57(2), 401-411. doi:10.1007/s11517-018-1888-yGarfield, R. E., & Maner, W. L. (2007). Physiology and electrical activity of uterine contractions. Seminars in Cell & Developmental Biology, 18(3), 289-295. doi:10.1016/j.semcdb.2007.05.004Ahmed, M., Chanwimalueang, T., Thayyil, S., & Mandic, D. (2016). A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis. Entropy, 19(1), 2. doi:10.3390/e19010002Brennan, M., Palaniswami, M., & Kamen, P. (2001). Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48(11), 1342-1347. doi:10.1109/10.959330Cui, D., Pu, W., Liu, J., Bian, Z., Li, Q., Wang, L., & Gu, G. (2016). A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information. Neural Networks, 82, 30-38. doi:10.1016/j.neunet.2016.06.004Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Bottou, L. (2012). Stochastic Gradient Descent Tricks. Neural Networks: Tricks of the Trade, 421-436. doi:10.1007/978-3-642-35289-8_25Lim, K., Butt, K., & Crane, J. M. (2018). No. 257-Ultrasonographic Cervical Length Assessment in Predicting Preterm Birth in Singleton Pregnancies. Journal of Obstetrics and Gynaecology Canada, 40(2), e151-e164. doi:10.1016/j.jogc.2017.11.01

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

[EN] Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 +/- 4.3% and 76.2 +/- 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.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).Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Alberola Rubio, J.; Monfort-Ortiz, R.; Martinez-Saez, C.; Perales, A.... (2020). Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy. Sensors. 20(9):1-16. https://doi.org/10.3390/s20092681S116209Beck, S., Wojdyla, D., Say, L., Pilar Bertran, A., Meraldi, M., Harris Requejo, J., … Van Look, P. (2010). The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bulletin of the World Health Organization, 88(1), 31-38. doi:10.2471/blt.08.062554Zeitlin, J., Szamotulska, K., Drewniak, N., Mohangoo, A., Chalmers, J., … Sakkeus, L. (2013). Preterm birth time trends in Europe: a study of 19 countries. BJOG: An International Journal of Obstetrics & Gynaecology, 120(11), 1356-1365. doi:10.1111/1471-0528.12281Goldenberg, R. L., Culhane, J. F., Iams, J. D., & Romero, R. (2008). Epidemiology and causes of preterm birth. The Lancet, 371(9606), 75-84. doi:10.1016/s0140-6736(08)60074-4Petrou, S. (2005). The economic consequences of preterm birth duringthe first 10 years of life. 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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-yHoroba, 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.2016.06.004Vinken, 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.0b013e3181a8c6b1Vrhovec, J., Macek-Lebar, A., & Rudel, D. (s. f.). Evaluating Uterine Electrohysterogram with Entropy. IFMBE Proceedings, 144-147. doi:10.1007/978-3-540-73044-6_36Diab, A., Hassan, M., Marque, C., & Karlsson, B. (2014). Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals. Medical Engineering & Physics, 36(6), 761-767. doi:10.1016/j.medengphy.2014.01.009Lemancewicz, 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.2015.11.005Hassan, 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.2011.03.010Fergus, 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.2015.01.107Acharya, 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.0132116Degbedzui, D. K., & Yüksel, M. E. (2020). Accurate diagnosis of term–preterm births by spectral analysis of electrohysterography signals. Computers in Biology and Medicine, 119, 103677. doi:10.1016/j.compbiomed.2020.103677Borowska, M., Brzozowska, E., Kuć, P., Oczeretko, E., Mosdorf, R., & Laudański, P. (2018). Identification of preterm birth based on RQA analysis of electrohysterograms. Computer Methods and Programs in Biomedicine, 153, 227-236. doi:10.1016/j.cmpb.2017.10.018Vinken, M. P. G. C., Rabotti, C., Mischi, M., van Laar, J. O. E. H., & Oei, S. G. (2010). Nifedipine-Induced Changes in the Electrohysterogram of Preterm Contractions: Feasibility in Clinical Practice. Obstetrics and Gynecology International, 2010, 1-8. doi:10.1155/2010/325635Mas-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-yBradley, 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)00142-2Alexandersson, A., Steingrimsdottir, T., Terrien, J., Marque, C., & Karlsson, B. (2015). The Icelandic 16-electrode electrohysterogram database. Scientific Data, 2(1). doi:10.1038/sdata.2015.17Maner, W. (2003). Predicting term and preterm delivery with transabdominal uterine electromyography. Obstetrics & Gynecology, 101(6), 1254-1260. doi:10.1016/s0029-7844(03)00341-7Maner, 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/s10439-006-9248-8Mas-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:10.1155/2019/5373810Terrien, J., Marque, C., & Karlsson, B. (2007). Spectral characterization of human EHG frequency components based on the extraction and reconstruction of the ridges in the scalogram. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2007.4352680Rooijakkers, M. J., Rabotti, C., Oei, S. G., Aarts, R. M., & Mischi, M. (2014). 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Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Kl. Comparison between Using Linear and Non-linear Features to Classify Uterine Electromyography Signals of Term and Preterm Deliveries. (2013). 2013 30th National Radio Science Conference (NRSC). doi:10.1109/nrsc.2013.6587953Aditya, S., & Tibarewala, D. N. (2012). Comparing ANN, LDA, QDA, KNN and SVM algorithms in classifying relaxed and stressful mental state from two-channel prefrontal EEG data. International Journal of Artificial Intelligence and Soft Computing, 3(2), 143. doi:10.1504/ijaisc.2012.049010Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. 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American Journal of Obstetrics and Gynecology, 199(4), 378.e1-378.e6. doi:10.1016/j.ajog.2008.08.003Mas-Cabo, J., Prats-Boluda, G., Ye-Lin, Y., Alberola-Rubio, J., Perales, A., & Garcia-Casado, J. (2019). Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor. Biomedical Signal Processing and Control, 52, 198-205. doi:10.1016/j.bspc.2019.04.001You, J., Kim, Y., Seok, W., Lee, S., Sim, D., Park, K. S., & Park, C. (2019). Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor. Journal of Electrical Engineering & Technology, 14(2), 897-916. doi:10.1007/s42835-019-00118-9Schuit, E., Scheepers, H., Bloemenkamp, K., Bolte, A., Duvekot, H., … van Eyck, J. (2015). Predictive Factors for Delivery within 7 Days after Successful 48-Hour Treatment of Threatened Preterm Labor. American Journal of Perinatology Reports, 05(02), e141-e149. doi:10.1055/s-0035-1552930Liao, J. B., Buhimschi, C. S., & Norwitz, E. R. (2005). Normal Labor: Mechanism and Duration. Obstetrics and Gynecology Clinics of North America, 32(2), 145-164. doi:10.1016/j.ogc.2005.01.001Ye-Lin, Y., Garcia-Casado, J., Prats-Boluda, G., Alberola-Rubio, J., & Perales, A. (2014). Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions. Computational and Mathematical Methods in Medicine, 2014, 1-11. doi:10.1155/2014/47078

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. 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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. 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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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IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Aboy, M., Hornero, R., Abasolo, D., & Alvarez, D. (2006). Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis. IEEE Transactions on Biomedical Engineering, 53(11), 2282-2288. doi:10.1109/tbme.2006.883696Katz, M. J. (1988). Fractals and the analysis of waveforms. Computers in Biology and Medicine, 18(3), 145-156. doi:10.1016/0010-4825(88)90041-8De Lau, H., Yang, K. T., Rabotti, C., Vlemminx, M., Bajlekov, G., Mischi, M., & Oei, S. G. (2016). Toward a new modality for detecting a uterine rupture: electrohysterogram propagation analysis during trial of labor after cesarean. The Journal of Maternal-Fetal & Neonatal Medicine, 30(5), 574-579. doi:10.1080/14767058.2016.1178227Benalcazar-Parra, C., Ye-Lin, Y., Garcia-Casado, J., Monfort-Orti, R., Alberola-Rubio, J., Perales, A., & Prats-Boluda, G. (2018). 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Computational and Mathematical Methods in Medicine, 2014, 1-11. doi:10.1155/2014/470786Alberola-Rubio, J., Garcia-Casado, J., Prats-Boluda, G., Ye-Lin, Y., Desantes, D., Valero, J., & Perales, A. (2017). Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography? Computer Methods and Programs in Biomedicine, 144, 127-133. doi:10.1016/j.cmpb.2017.03.018Maner, W. L., MacKay, L. B., Saade, G. R., & Garfield, R. E. (2006). Characterization of abdominally acquired uterine electrical signals in humans, using a non-linear analytic method. Medical & Biological Engineering & Computing, 44(1-2), 117-123. doi:10.1007/s11517-005-0011-3Marchini, G., Lagercrantz, H., Winberg, J., & Uvnäs-Moberg, K. (1988). Fetal and maternal plasma levels of gastrin, somatostatin and oxytocin after vaginal delivery and elective cesarean section. Early Human Development, 18(1), 73-79. doi:10.1016/0378-3782(88)90044-8Pickering, K., Gallos, I. D., Williams, H., Price, M. 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Elevated bradykinin receptor type 1 expression in postpartum acute myometritis: Possible involvement in augmented interstitial edema of the atonic gravid uterus. Journal of Obstetrics and Gynaecology Research, 45(8), 1553-1561. doi:10.1111/jog.1401

Mathematical modelling of the waning of anti-RBD IgG SARS-CoV-2 antibody titers after a two-dose BNT162b2 mRNA vaccination

BackgroundAfter exposure to SARS-CoV-2 and/or vaccination there is an increase in serum antibody titers followed by a non-linear waning. Our aim was to find out if this waning of antibody titers would fit to a mathematical model.MethodsWe analyzed anti-RBD (receptor binding domain) IgG antibody titers and the breakthrough infections over a ten-month period following the second dose of the mRNA BNT162b2 (Pfizer-BioNtech.) vaccine, in a cohort of 54 health-care workers (HCWs) who were either never infected with SARS-CoV-2 (naïve, nHCW group, n=27) or previously infected with the virus (experienced, eHCW group, n=27). Two mathematical models, exponential and power law, were used to quantify antibody waning kinetics, and we compared the relative quality of the goodness of fit to the data between both models was compared using the Akaik Information Criterion.ResultsWe found that the waning slopes were significantly more pronounced for the naïve when compared to the experienced HCWs in exponential (p-value: 1.801E-9) and power law (p-value: 9.399E-13) models. The waning of anti-RBD IgG antibody levels fitted significantly to both exponential (average-R2: 0.957 for nHCW and 0.954 for eHCW) and power law (average-R2: 0.991 for nHCW and 0.988 for eHCW) models, with a better fit to the power law model. In the nHCW group, titers would descend below an arbitrary 1000-units threshold at a median of 210.6 days (IQ range: 74.2). For the eHCW group, the same risk threshold would be reached at 440.0 days (IQ range: 135.2) post-vaccination.ConclusionTwo parsimonious models can explain the anti-RBD IgG antibody titer waning after vaccination. Regardless of the model used, eHCWs have lower waning slopes and longer persistence of antibody titers than nHCWs. Consequently, personalized vaccination booster schedules should be implemented according to the individual persistence of antibody levels

Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment

[EN] As one of the main aims of obstetrics is to be able to detect imminent delivery in patients with threatened preterm labor, the techniques currently used in clinical practice have serious limitations in this respect. The electrohysterogram (EHG) has now emerged as an alternative technique, providing relevant information about labor onset when recorded in controlled checkups without administration of tocolytic drugs. The studies published to date mainly focus on EHG-burst analysis and, to a lesser extent, on whole EHG window analysis. The study described here assessed the ability of EHG signals to discriminate imminent labor (The ability of EHG recordings to predict imminent labor (<7days) was analyzed in preterm threatened patients undergoing tocolytic therapies by means of EHG-burst and whole EHG window analysis. The non-linear features were found to have better performance than the temporal and spectral parameters in separating women who delivered in less than 7days from those who did not.Mas-Cabo, J.; Prats-Boluda, G.; Perales Marín, AJ.; Garcia-Casado, J.; Alberola Rubio, J.; Ye Lin, Y. (2019). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing. 57:401-411. https://doi.org/10.1007/s11517-018-1888-yS40141157Aboy M, Cuesta-Frau D, Austin D, Micó-Tormos P (2007) Characterization of sample entropy in the context of biomedical signal analysis. Conf Proc IEEE Eng Med Biol Soc:5942–5945. https://doi.org/10.1109/IEMBS.2007.4353701Aboy M, Hornero R, Abásolo D, Álvarez D (2006) Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. 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Med Biol Eng Comput 46:911–922. https://doi.org/10.1007/s11517-008-0350-yFergus 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:e77154. https://doi.org/10.1371/journal.pone.0077154Garfield RE, Maner WL (2006) Biophysical methods of prediction and prevention of preterm labor: uterine electromyography and cervical light-induced fluorescence—new obstetrical diagnostic techniques. In: Preterm Birth pp 131–144Garfield RE, Maner WL (2007) Physiology and electrical activity of uterine contractions. Semin Cell Dev Biol 18:289–295. https://doi.org/10.1016/j.semcdb.2007.05.004Garfield RE, Maner WL, MacKay LB et al (2005) Comparing uterine electromyography activity of antepartum patients versus term labor patients. Am J Obstet Gynecol 193:23–29. https://doi.org/10.1016/j.ajog.2005.01.050Goldenberg RL, Culhane JF, Iams JD, Romero R (2008) Epidemiology and causes of preterm birth. 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Med Eng Phys 33:980–986. https://doi.org/10.1016/j.medengphy.2011.03.010Hassan M, Terrien J, Muszynski C et al (2013) Better pregnancy monitoring using nonlinear correlation analysis of external uterine electromyography. IEEE Trans Biomed Eng 60:1160–1166. https://doi.org/10.1109/TBME.2012.2229279Horoba 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. Biocybern Biomed Eng 36:574–583. https://doi.org/10.1016/j.bbe.2016.06.004Lawn JE, Wilczynska-Ketende K, Cousens SN (2006) Estimating the causes of 4 million neonatal deaths in the year 2000. Int J Epidemiol 35:706–718. https://doi.org/10.1093/ije/dyl043Lemancewicz 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. Biocybern Biomed Eng 36:302–307. https://doi.org/10.1016/j.bbe.2015.11.005M L WLM, LR C (2012) Noninvasive uterine electromyography for prediction of preterm delivery. Am J Obstet Gynecol 204:1–20. https://doi.org/10.1016/j.ajog.2010.09.024.NoninvasiveManer WL, Garfield RE (2007) Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng 35:465–473. https://doi.org/10.1007/s10439-006-9248-8Maner WL, Garfield RE, Maul H, Olson G, Saade G (2003) Predicting term and preterm delivery with transabdominal uterine electromyography. Obstet Gynecol 101:1254–1260. https://doi.org/10.1016/S0029-7844(03)00341-7Marque C, Gondry J (1999) Use of the electrohysterogram signal for characterization of contractions during pregnancy. 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Diseño y desarrollo de un prototipo de sistema multicanal de alta resolución, para la captación y visualización de señales electrocardiograficas

Consulta en la Biblioteca ETSI Industriales (Riunet)Mas Cabo, J. (2015). Diseño y desarrollo de un prototipo de sistema multicanal de alta resolución, para la captación y visualización de señales electrocardiograficas. http://hdl.handle.net/10251/56087.Archivo delegad

Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios

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&ndash;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