Influence of voluntary contractions on the basal sEMG activity of the pelvic floor muscles

[EN] Chronic pelvic pain (CPP) is a complex clinical condition that affects many women, being sometimes misdiagnosed or mistreated,which can be treated with the infiltration of botulinum toxin (BoNTA). The pelvic floor musculature (PFM) condition from CPP patients can be assessed by means of surface electromyography (sEMG). The evaluation of the basal activity can help to detect a muscular dysfunction, therefore it is important to ensure that the PFM shows a minimum activation when its sEMG is being analysed. In this study, we recorded the sEMG of 25 women with CPP before and 8, 12 and 24 weeks after their treatment with BoNTA while they performed a protocol of 5 voluntary contractions. The root mean square (RMS) and sample entropy (SampEn) of basal segments pre- (B[PRE]), inter- (B[I]) and post- (B[POST]) contractions of the sEMG were computed and normalized according to the minimum (RMSnorm) and maximum (SampEnorm) of the recording. B(PRE) showed the lowest RMSnorm median both before and after the treatment with BoNTA, which proved that the activity of the PFM is minimum before the first contraction. As for SampEnnorm, although results were not so conclusive, they also indicated that B(PRE) should be taken as a reference to analyse the PFM function at its state of minimum activity. Future works aiming to characterize the effects of BoNTA in PFM by means of sEMG should consider basal segments before contractions to assess basal tone conditions.This study was funded by ISCIII, MCIU, VLC Campus in Convocatoria Ayudas: UPV-La Fe (INBIO): 2016 SPEHG (ID:C18), 2019 sEMG_BONTAv (ID:C06) and with funds from private contracts with Merz Pharma España S.L.Albaladejo-Belmonte, M.; Tarazona-Motes, M.; Nohales-Alfonso, FJ.; Alberola-Rubio, J.; Garcia-Casado, J. (2020). Influence of voluntary contractions on the basal sEMG activity of the pelvic floor muscles. Sociedad Española de Ingeniería Biomédica. 240-243. http://hdl.handle.net/10251/178256S24024

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

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

Treatment of Dyspareunia with Botulinum Neurotoxin Type A: Clinical Improvement and Influence of Patients' Characteristics

[EN] The treatment of chronic pelvic pain (CPP) with botulinum neurotoxin type A (BoNT/A) has increased lately, but more studies assessing its effect are needed. This study aimed to evaluate the evolution of patients after BoNT/A infiltration and identify potential responders to treatment. Twenty-four women with CPP associated with dyspareunia were treated with 90 units of BoNT/A injected into their pelvic floor muscle (PFM). Clinical status and PFM activity were monitored in a previous visit (PV) and 12 and 24 weeks after the infiltration (W12, W24) by validated clinical questionnaires and surface electromyography (sEMG). The influence of patients' characteristics on the reduction in pain at W12 and W24 was also assessed. After treatment, pain scores and the impact of symptoms on quality of life dropped significantly, sexual function improved and sEMG signal amplitude decreased on both sides of the PFM with no adverse events. Headaches and bilateral pelvic pain were risk factors for a smaller pain improvement at W24, while lower back pain was a protective factor. Apart from reporting a significant clinical improvement of patients with CPP associated with dyspareunia after BoNT/A infiltration, this study shows that clinical characteristics should be analyzed in detail to identify potential responders to treatment.This study was funded by Universitat Politecnica de Valencia in Programa de Ayudas de Investigacion y Desarrollo (PAID-01-20), ISCIII, MCIU, VLC Campus in Convocatoria Ayudas: UPV-La Fe (INBIO): 2016 SPEHG (ID:C18), 2019 sEMG_BONTAv (ID:C06) and funds from private contracts with Merz Pharmaceuticals GmbH S.L.Tarazona-Motes, M.; Albaladejo-Belmonte, M.; Nohales-Alfonso, FJ.; De-Arriba, M.; Garcia-Casado, J.; Alberola-Rubio, J. (2021). Treatment of Dyspareunia with Botulinum Neurotoxin Type A: Clinical Improvement and Influence of Patients' Characteristics. International Journal of Environmental research and Public Health. 18(16):1-12. https://doi.org/10.3390/ijerph18168783S112181

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

Characterization of Pelvic Floor Activity in Healthy Subjects and with Chronic Pelvic Pain: Diagnostic Potential of Surface Electromyography

[EN] Chronic pelvic pain (CPP) is a highly disabling disorder in women usually associated with hypertonic dysfunction of the pelvic floor musculature (PFM). The literature on the subject is not conclusive about the diagnostic potential of surface electromyography (sEMG), which could be due to poor signal characterization. In this study, we characterized the PFM activity of three groups of 24 subjects each: CPP patients with deep dyspareunia associated with a myofascial syndrome (CPP group), healthy women over 35 and/or parous (>35/P group, i.e., CPP counterparts) and under 35 and nulliparous (RMS), a predominance of low-frequency components (DI), greater complexity (>SampEn) and lower synchronization on the same side (35/P group. The same trend in differences was found between healthy women (35/P) associated with aging and parity. These results show that sEMG can reveal alterations in PFM electrophysiology and provide clinicians with objective information for CPP diagnosis.This study was funded by Universitat Politecnica de Valencia in Programa de Ayudas de Investigacion y Desarrollo (PAID-01-20), ISCIII, MCIU, VLC Campus in Convocatoria Ayudas: UPV-La Fe (INBIO): 2016 SPEHG (ID:C18), 2019 sEMG_BONTAv (ID:C06) and funds from private contracts with Merz Pharmaceuticals GmbH S.Albaladejo-Belmonte, M.; Tarazona-Motes, M.; Nohales-Alfonso, FJ.; De-Arriba, M.; Alberola-Rubio, J.; Garcia-Casado, J. (2021). Characterization of Pelvic Floor Activity in Healthy Subjects and with Chronic Pelvic Pain: Diagnostic Potential of Surface Electromyography. Sensors. 21(6):1-17. https://doi.org/10.3390/s21062225S11721

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

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). Prediction of Labor Induction Success from the Uterine Electrohysterogram. Journal of Sensors. 2019:1-12. https://doi.org/10.1155/2019/6916251S1122019Filho, O. B. M., Albuquerque, R. M., & Cecatti, J. G. (2010). A randomized controlled trial comparing vaginal misoprostol versus Foley catheter plus oxytocin for labor induction. Acta Obstetricia et Gynecologica Scandinavica, 89(8), 1045-1052. doi:10.3109/00016349.2010.499447Seyb, S. (1999). Risk of cesarean delivery with elective induction of labor at term in nulliparous women. Obstetrics & Gynecology, 94(4), 600-607. doi:10.1016/s0029-7844(99)00377-4Hou, L., Zhu, Y., Ma, X., Li, J., & Zhang, W. (2012). Clinical parameters for prediction of successful labor induction after application of intravaginal dinoprostone in nulliparous Chinese women. Medical Science Monitor, 18(8), CR518-CR522. doi:10.12659/msm.883273Pitarello, P. da R. P., Tadashi Yoshizaki, C., Ruano, R., & Zugaib, M. (2012). Prediction of successful labor induction using transvaginal sonographic cervical measurements. Journal of Clinical Ultrasound, 41(2), 76-83. doi:10.1002/jcu.21929Prado, C. A. de C., Araujo Júnior, E., Duarte, G., Quintana, S. M., Tonni, G., Cavalli, R. de C., & Marcolin, A. C. (2016). Predicting success of labor induction in singleton term pregnancies by combining maternal and ultrasound variables. The Journal of Maternal-Fetal & Neonatal Medicine, 1-35. doi:10.3109/14767058.2015.1135124Sievert, R. A., Kuper, S. G., Jauk, V. C., Parrish, M., Biggio, J. R., & Harper, L. M. (2017). Predictors of vaginal delivery in medically indicated early preterm induction of labor. American Journal of Obstetrics and Gynecology, 217(3), 375.e1-375.e7. doi:10.1016/j.ajog.2017.05.025Garfield, 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.xFergus, 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.0077154Aviram, A., Melamed, N., Hadar, E., Raban, O., Hiersch, L., & Yogev, Y. (2013). Effect of Prostaglandin E2 on Myometrial Electrical Activity in Women Undergoing Induction of Labor. American Journal of Perinatology, 31(05), 413-418. doi:10.1055/s-0033-1352486Benalcazar-Parra, C., Ye-Lin, Y., Garcia-Casado, J., Monfort-Orti, R., Alberola-Rubio, J., Perales, A., & Prats-Boluda, G. (2018). Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Medical Engineering & Physics, 56, 27-35. doi:10.1016/j.medengphy.2018.04.002Benalcazar-Parra, C., Monfort-Orti, R., Ye-Lin, Y., Prats-Boluda, G., Alberola-Rubio, J., Perales, A., & Garcia-Casado, J. (2017). Comparison of labour induction with misoprostol and dinoprostone and characterization of uterine response based on electrohysterogram. The Journal of Maternal-Fetal & Neonatal Medicine, 32(10), 1586-1594. doi:10.1080/14767058.2017.1410791Maner, 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/s10439-006-9248-8Diab, M. O., Marque, C., & Khalil, M. (2009). An unsupervised classification method of uterine electromyography signals: Classification for detection of preterm deliveries. Journal of Obstetrics and Gynaecology Research, 35(1), 9-19. doi:10.1111/j.1447-0756.2008.00981.xShi, S.-Q., Maner, W. L., Mackay, L. B., & Garfield, R. E. (2008). Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals. American Journal of Obstetrics and Gynecology, 198(2), 235.e1-235.e4. doi:10.1016/j.ajog.2007.08.039Østborg, T. B., Romundstad, P. R., & Eggebø, T. M. (2016). Duration of the active phase of labor in spontaneous and induced labors. Acta Obstetricia et Gynecologica Scandinavica, 96(1), 120-127. doi:10.1111/aogs.13039Baños, N., Migliorelli, F., Posadas, E., Ferreri, J., & Palacio, M. (2015). Definition of Failed Induction of Labor and Its Predictive Factors: Two Unsolved Issues of an Everyday Clinical Situation. Fetal Diagnosis and Therapy, 38(3), 161-169. doi:10.1159/000433429Bueno, B., San-Frutos, L., Salazar, F., Pérez-Medina, T., Engels, V., Archilla, B., … Bajo, J. (2005). Variables that predict the success of labor induction. Acta Obstetricia et Gynecologica Scandinavica, 84(11), 1093-1097. doi:10.1111/j.0001-6349.2005.00881.xWare, V., & Raynor, B. D. (2000). Transvaginal ultrasonographic cervical measurement as a predictor of successful labor induction. American Journal of Obstetrics and Gynecology, 182(5), 1030-1032. doi:10.1067/mob.2000.105399Rooijakkers, M. J., Song, S., Rabotti, C., Oei, S. G., Bergmans, J. W. M., Cantatore, E., & Mischi, M. (2014). Influence of Electrode Placement on Signal Quality for Ambulatory Pregnancy Monitoring. Computational and Mathematical Methods in Medicine, 2014, 1-12. doi:10.1155/2014/960980Garfield, 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.2007.05.004Leman, 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:10.1109/10.790499BUHIMSCHI, C., BOYLE, M., & GARFIELD, R. (1997). Electrical activity of the human uterus during pregnancy as recorded from the abdominal surface. Obstetrics & Gynecology, 90(1), 102-111. doi:10.1016/s0029-7844(97)83837-9Schlembach, 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.2009.02.016Alamedine, D., Diab, A., Muszynski, C., Karlsson, B., Khalil, M., & Marque, C. (2014). Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor. Signal, Image and Video Processing, 8(6), 1169-1178. doi:10.1007/s11760-014-0655-2Fele-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-yTerrien, J., Marque, C., Gondry, J., Steingrimsdottir, T., & Karlsson, B. (2010). Uterine electromyogram database and processing function interface: An open standard analysis platform for electrohysterogram signals. Computers in Biology and Medicine, 40(2), 223-230. doi:10.1016/j.compbiomed.2009.11.019Hassan, 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.2015.11.005Weiting Chen, Zhizhong Wang, Hongbo Xie, & Wangxin Yu. (2007). Characterization of Surface EMG Signal Based on Fuzzy Entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266-272. doi:10.1109/tnsre.2007.897025Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Blanco, S., Garay, A., & Coulombie, D. (2013). Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction. ISRN Neurology, 2013, 1-5. doi:10.1155/2013/287327Brennan, 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:10.1109/10.959330Chawla, 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.953Makond, B., Wang, K.-J., & Wang, K.-M. (2015). Probabilistic modeling of short survivability in patients with brain metastasis from lung cancer. Computer Methods and Programs in Biomedicine, 119(3), 142-162. doi:10.1016/j.cmpb.2015.02.005Gori, M., & Tesi, A. (1992). On the problem of local minima in backpropagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(1), 76-86. doi:10.1109/34.107014Zong, W., Huang, G.-B., & Chen, Y. (2013). Weighted extreme learning machine for imbalance learning. Neurocomputing, 101, 229-242. doi:10.1016/j.neucom.2012.08.010Acharya, 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.2017.04.013Taft, L. M., Evans, R. S., Shyu, C. R., Egger, M. J., Chawla, N., Mitchell, J. A., … Varner, M. (2009). Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery. Journal of Biomedical Informatics, 42(2), 356-364. doi:10.1016/j.jbi.2008.09.001Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Blagus, R., & Lusa, L. (2015). Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models. BMC Bioinformatics, 16(1). doi:10.1186/s12859-015-0784-9Loughrey, J., & Cunningham, P. (s. f.). Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets. Research and Development in Intelligent Systems XXI, 33-43. doi:10.1007/1-84628-102-4_

Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions

Electrohysterography (EHG) is a non-invasive 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 non-artifacted signals. To develop a classifier, a total of eleven spectral, temporal and non-linear 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.The authors are grateful to the R + D + I Linguistic Assistance Office at the UPV for their help in proofreading this paper. The work was supported by the Ministerio de Ciencia e Innovacion de Espana (TEC2010-16945).Ye-Lin, Y.; Garcia Casado, FJ.; Prats Boluda, G.; Alberola Rubio, J.; Perales Marin, AJ. (2014). Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions. Computational and Mathematical Methods in Medicine. 2014:1-11. https://doi.org/10.1155/2014/470786S1112014Wilmink, 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:10.1080/14767050802220508Vinken, 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.0b013e3181a8c6b1Schlembach, 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.2009.02.016Miles, A. M., Monga, M., & Richeson, K. S. (2001). Correlation of External and Internal Monitoring of Uterine Activity in a Cohort of Term Patients. American Journal of Perinatology, 18(03), 137-140. doi:10.1055/s-2001-14522Devedeux, 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-sGarfield, 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.2007.05.004Marque, C. K., Terrien, J., Rihana, S., & Germain, G. (2007). Preterm labour detection by use of a biophysical marker: the uterine electrical activity. BMC Pregnancy and Childbirth, 7(S1). doi:10.1186/1471-2393-7-s1-s5Lucovnik, 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.2010.09.024Euliano, T. Y., Marossero, D., Nguyen, M. T., Euliano, N. R., Principe, J., & Edwards, R. K. (2009). Spatiotemporal electrohysterography patterns in normal and arrested labor. American Journal of Obstetrics and Gynecology, 200(1), 54.e1-54.e7. doi:10.1016/j.ajog.2008.09.008Rabotti, C., Mischi, M., van Laar, J. O. E. H., Oei, G. S., & Bergmans, J. W. M. (2009). Inter-electrode delay estimators for electrohysterographic propagation analysis. Physiological Measurement, 30(8), 745-761. doi:10.1088/0967-3334/30/8/002Jezewski, J., Horoba, K., Matonia, A., & Wrobel, J. (2005). Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach. Physiological Measurement, 26(5), 753-767. doi:10.1088/0967-3334/26/5/014Euliano, 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:10.1080/14767050601023657Euliano, 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.0000258799.24496.93Rabotti, C., Mischi, M., van Laar, J. O. E. H., Oei, G. S., & Bergmans, J. W. M. (2008). Estimation of internal uterine pressure by joint amplitude and frequency analysis of electrohysterographic signals. Physiological Measurement, 29(7), 829-841. doi:10.1088/0967-3334/29/7/011Euliano, 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.2012.10.873Hassan, M., Boudaoud, S., Terrien, J., Karlsson, B., & Marque, C. (2011). Combination of Canonical Correlation Analysis and Empirical Mode Decomposition Applied to Denoising the Labor Electrohysterogram. IEEE Transactions on Biomedical Engineering, 58(9), 2441-2447. doi:10.1109/tbme.2011.2151861Liang, J., Cheung, J. Y., & Chen, J. D. Z. (1997). Detection and deletion of motion artifacts in electrogastrogram using feature analysis and neural networks. Annals of Biomedical Engineering, 25(5), 850-857. doi:10.1007/bf02684169Verhagen, M. A. M. T., Van Schelven, L. J., Samsom, M., & Smout, A. J. P. M. (1999). Pitfalls in the analysis of electrogastrographic recordings. Gastroenterology, 117(2), 453-460. doi:10.1053/gast.1999.0029900453Conforto, S., D’Alessio, T., & Pignatelli, S. (1999). Optimal rejection of movement artefacts from myoelectric signals by means of a wavelet filtering procedure. Journal of Electromyography and Kinesiology, 9(1), 47-57. doi:10.1016/s1050-6411(98)00023-6Liang, H., Lin, Z., & McCallum, R. W. (2000). Artifact reduction in electrogastrogram based on empirical mode decomposition method. Medical & Biological Engineering & Computing, 38(1), 35-41. doi:10.1007/bf02344686Maner, 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/s10439-006-9248-8Moslem, B. (2010). Journal of Medical and Biological Engineering, 30(6), 361. doi:10.5405/jmbe.768Hassan, M. M., Terrien, J., Muszynski, C., Alexandersson, A., Marque, C., & Karlsson, B. (2013). Better Pregnancy Monitoring Using Nonlinear Correlation Analysis of External Uterine Electromyography. IEEE Transactions on Biomedical Engineering, 60(4), 1160-1166. doi:10.1109/tbme.2012.2229279Leman, 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:10.1109/10.790499Maul, 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:10.1080/14767050410001695301Terrien, J., Steingrimsdottir, T., Marque, C., & Karlsson, B. (2010). Synchronization between EMG at Different Uterine Locations Investigated Using Time-Frequency Ridge Reconstruction: Comparison of Pregnancy and Labor Contractions. EURASIP Journal on Advances in Signal Processing, 2010(1). doi:10.1155/2010/242493LUCOVNIK, M., KUON, R. J., CHAMBLISS, L. R., MANER, W. L., SHI, S.-Q., SHI, L., … GARFIELD, R. E. (2010). Use of uterine electromyography to diagnose term and preterm labor. Acta Obstetricia et Gynecologica Scandinavica, 90(2), 150-157. doi:10.1111/j.1600-0412.2010.01031.xVerdenik, 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)00418-8Fele-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.2011.03.010Irimia, A., & Bradshaw, L. A. (2005). Artifact reduction in magnetogastrography using fast independent component analysis. Physiological Measurement, 26(6), 1059-1073. doi:10.1088/0967-3334/26/6/015Milanesi, M., Martini, N., Vanello, N., Positano, V., Santarelli, M. F., & Landini, L. (2007). Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals. Medical & Biological Engineering & Computing, 46(3), 251-261. doi:10.1007/s11517-007-0293-8Daly, I., Billinger, M., Scherer, R., & Muller-Putz, G. (2013). On the Automated Removal of Artifacts Related to Head Movement From the EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(3), 427-434. doi:10.1109/tnsre.2013.2254724Ye-Lin, Y., Garcia-Casado, J., Prats-Boluda, G., & Martinez-de-Juan, J. L. (2010). Combined Method for Reduction of High Frequency Interferences in Surface Electroenterogram (EEnG). Annals of Biomedical Engineering, 38(7), 2358-2370. doi:10.1007/s10439-010-9991-8Schreiber, T., & Schmitz, A. (2000). Surrogate time series. Physica D: Nonlinear Phenomena, 142(3-4), 346-382. doi:10.1016/s0167-2789(00)00043-9Van Gestel, T., Suykens, J. A. K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., … Vandewalle, J. (2004). Benchmarking Least Squares Support Vector Machine Classifiers. Machine Learning, 54(1), 5-32. doi:10.1023/b:mach.0000008082.80494.e0Leman, H., & Marque, C. (2000). Rejection of the maternal electrocardiogram in the electrohysterogram signal. IEEE Transactions on Biomedical Engineering, 47(8), 1010-1017. doi:10.1109/10.855927Marque, C., Bisch, C., Dantas, R., Elayoubi, S., Brosse, V., & Pérot, C. (2005). Adaptive filtering for ECG rejection from surface EMG recordings. Journal of Electromyography and Kinesiology, 15(3), 310-315. doi:10.1016/j.jelekin.2004.10.00