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OBJECTIVE: Electrohysterography is a non-invasive technique to monitor uterine activity and has a significantly higher sensitivity compared to conventional external tocodynamometry. Whether this technique could lead to improved obstetrical outcomes is still unknown. In this propensity score matched study, clinical results of the first pilot implementing electrohysterography during labor were evaluated. The hypothesis tested is that electrohysterography will help to optimize uterine activity and thereby lead to fewer obstetric interventions. Secondary outcomes were Apgar score, arterial umbilical pH values, first stage labor duration, episiotomy rate and postpartum vaginal blood loss.STUDY DESIGN: From November 2017 until October 2018, electrohysterography was introduced as a standard alternative for monitoring uterine activity in high-risk deliveries. It could be applied in case of induced labor, previous cesarean delivery, body mass index ≥30 kg/m2 or an inadequate external tocodynamometry monitoring. Outcomes were compared to a matched group of women in which external tocodynamometry was applied for uterine activity monitoring during labor. These women were identified using propensity scores.RESULTS: A total of 348 women received electrohysterography as standard method of uterine monitoring during labor. A match (1:1 ratio) was found for 317 women, resulting in a total population of 634 women. No significant differences were seen in obstetric interventions (i.e. cesarean deliveries and assisted vaginal deliveries) between the electrohysterography and tocodynamometry group (P = 0.80). No statistically significant differences were seen regarding the secondary outcomes.CONCLUSIONS: This first pilot study implementing electrohysterography as monitoring method during labor in a high-risk population did not result in statistically significant differences regarding obstetric interventions, low Apgar scores or low umbilical artery pH values. Therefore, we suggest that electrohysterography causes no harm and we recommend further implementation and evaluation in clinical practice.

The proposed dataset provides annotations for the 552 cardiotocographic (CTG) recordings included in the publicly available "CTU-CHB intra-partum CTG database" from Physionet (https://physionet.org/content/ctu-uhb-ctgdb/1.0.0/). Each CTG recording is composed by two simultaneously acquired signals: i) the fetal heart rate (FHR) and ii) the maternal tocogram (representing uterine activity). Annotations consist in the detection of starting and ending points of specific CTG events on both FHR signal and maternal tocogram. Annotated events for the FHR signal are the bradycardia, tachycardia, acceleration and deceleration episodes. Annotated events for the maternal tocogram are the uterine contractions. The dataset also reports classification of each deceleration as early, late, variable or prolonged, in relation to the presence of a uterine contraction. Annotations were obtained by an expert gynecologist with the support of CTG Analyzer, a dedicated software application for automatic analysis of digital CTG recordings. These annotations can be useful in the development, testing and comparison of algorithms for the automatic analysis of digital CTG recordings, which can make CTG interpretation more objective and independent from clinician's experience.

The Cardiotocography is the most broadly utilized technique in obstetrics practice to monitor fetal health condition. The foremost motive of monitoring is to detect the fetal hypoxia at early stage. This modality is also widely used to record fetal heart rate and uterine activity. The exact analysis of cardiotocograms is critical for further treatment. In this manner, fetal state evaluation utilizing machine learning technique using cardiotocogram data has achieved significant attention. In this paper, we implement a model based CTG data classification system utilizing a supervised Random Forest (RF) which can classify the CTG data based on its training data. As per the showed up results, the overall performance of the supervised machine learning based classification approach provided significant performance. In this study, Precision, Recall and F-Score has been employed as the metric to evaluate the performance. It was found that, the RF based classifier could identify normal, suspicious and pathologic condition, from the nature of CTG data with 94.8% accuracy. We also highlight the major features based on Mean Decrease Accuracy and Mean Decrease Gini.

[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)

Fetal electrocardiography is a valuable alternative to standard fetal monitoring. Suppression of the maternal electrocardiogram (ECG) in the abdominal measurements, results in fetal ECG signals, from which the fetal heart rate (HR) can be determined. This HR detection typically requires fetal R-peak detection, which is challenging, especially during low signal-to-noise ratio periods, caused for example by uterine activity. In this paper, we propose the combination of a convolutional neural network and a long short-term memory network that directly predicts the fetal HR from multichannel fetal ECG. The network is trained on a dataset, recorded during labor, while the performance of the method is evaluated both on a test dataset and on set-A of the 2013 Physionet /Computing in Cardiology Challenge. The algorithm achieved a positive percent agreement of 92.1% and 98.1% for the two datasets respectively, outperforming a top-performing state-of-the-art signal processing algorithm.

Murray and Heulsmann present a clinically oriented, practical guidebook that will maximize maternal and fetal outcomes. The text features 12 sections covering important issues such as the labor curve and protraction disorders, rupture of the membranes, and uterine activity patterns.While similar to the author's "Essentials of Fetal Monitoring", this book places significant emphasis on how to approach these labor and delivery issues with care and efficiency. It also features more references and convenient review questions at the end of each section. An excellent reference and resource on all labor and delivery issues, this book serves as an easy-to-read manual for RNs who practice in, or are new to, labor and delivery settings.

Background Increased uterine activity (UA) may not allow adequate recovery time for foetal oxygenation. Methods The aim of the study was to determine if increased UA during labour is associated with an increased risk of either short- or long-term neurological injury in term neonates, or with neonatal proxy measures of intrapartum hypoxia-ischemia. MEDLINE, CINAHL, and ClinicalTrials.gov were searched using the following terms: uterine activity, excessive uterine activity, XSUA, uterine hyperstimulation, and tachysystole. Any study that analysed the relationship between UA during term labour and neurological outcomes/selected proxy neurological outcomes was eligible for inclusion. Outcomes from individual studies were reported in tables and presented descriptively with odds ratios (OR) and 95% confidence intervals (CI) for dichotomous outcomes and means with standard deviations for continuous outcomes. Where group numbers were provided, ORs and their CIs were calculated according to Altman. Main results Twelve studies met the inclusion criteria. Seven studies featured umbilical artery pH as an individual outcome. Umbilical artery base excess and Apgar scores were both reported as individual outcomes in four studies. No study examined long term neurodevelopmental outcomes and only one study reported on encephalopathy as an outcome. The evidence for a relationship between UA and adverse infant outcomes was inconsistent. The reported estimated effect size varied from non-existent to clinically significant. Conclusions There is some evidence that increased UA may be a non-specific predictor of depressed neurological function in the newborn, but it is inconsistent and insufficient to support the conclusion that an association generally exists.

A novel technique for detection and recognition of normal and premature births is proposed and experimentally examined. It is based on the extraction of novel class of informative features contained in higher-order spectrum, namely, bispectrum of the abdominal electromyography signals registered on the abdominal surface of pregnant woman. It is demonstrated that the amplitude bispectrum, phase bispectrum and bicoherence signatures computed for electromyography signals can serve as the perspective facilities for detection and recognition of the normal and premature births. The proposed bispectrum-based information features were studied by real-life experimental data processing. Uterine activity corresponding to two weeks and one week before birth for several patients has been investigated. Experimental results obtained for a number of the patients demonstrate the possibility to extract novel classification features contained in the computed biamplitude, biphase and bicoherence signatures. acceptedVersion

Monitoring the progression of uterine activity provides important prognostic information during pregnancy and delivery. Currently, uterine activity monitoring relies on direct or indirect mechanical measurements of intrauterine pressure (IUP), which are unsuitable for continuous long-term observation. The electrohysterogram (EHG) provides a non-invasive alternative to the existing methods and is suitable for long-term ambulatory use. Several published state-of-the-art methods for EHG-based IUP estimation are here discussed, analyzed, optimized, and compared. By means of parameter space exploration, key parameters of the methods are evaluated for their relevance and optimal values. We have optimized all methods towards higher IUP estimation accuracy and lower computational complexity. Their accuracy was compared with the gold standard accuracy of internally measured IUP. Their computational complexity was compared based on the required number of multiplications per second (MPS). Significant reductions in computational complexity have been obtained for all published algorithms, while improving IUP estimation accuracy. A correlation coefficient of 0.72 can be obtained using fewer than 120 MPS. We conclude that long-term ambulatory monitoring of uterine activity is possible using EHG-based methods. Furthermore, the choice of a base method for IUP estimation is less important than the correct selection of electrode positions, filter parameters, and postprocessing methods. The presented review of state-of-the-art methods and applied optimizations show that long-term ambulatory IUP monitoring is feasible using EHG measurements.

During labour, the uterus itself is vulnerable to hypoxia/ischemia that can occur with each strong contraction and this may ultimately cause dysfunctional labour in some women. Periods of Intermittent re-oxygenations are beneficial to tissues subjected to hypoxia to wash out metabolic by-products that have been accumulated during hypoxic stresses which may affect the tissue viability. We proposed that short intermittent hypoxic episodes may protect the uterus from subsequent sustained long hypoxia. To investigate this, two sets of experiments were performed on term-pregnant and nonpregnant rat uterine tissues. In one set of experiment the uterus was subjected to sustained long hypoxia for 40 min and then allowed to recover in 100% O2. In the other set of experiment the uterus was subjected to 3 cycles of 2 min hypoxia each separated by 20 min reoxygenation and followed by a sustained long hypoxia for 40 min and then allowed to recover. We found that challenging the uterine tissues with intermittent short hypoxic episodes improved the uterine contractility significantly after the sustained long hypoxia in term-pregnant but not in non-pregnant tissues. These results suggest that a mechanism of uterine tolerance (preconditioning) is confined to uterine tissues very close to labour and it is a protective phenomenon to improve the uterine activity despite the long-lasting paradoxical metabolic challenges that occur during the repeated strong labour contractions.

Uterine activity plays a crucial role in labor, especially when utero-tonic materials are administered. We aimed to determine the electrical responsiveness of the uterine musculature to labor augmentation with oxytocin using electrical uterine myography (EUM) technology, and to assess whether the kinetics of the EUM device may serve as a predictor for successful vaginal delivery. EUM prospectively measured electrical uterine activity in women with singleton gestations at term (≥ 37 + 0 weeks) undergoing labor augmentation by oxytocin administration. The results were reported as the EUM index, which represented the mean electrical activity in 10-min intervals and measured in units of microwatt per second (mW/s). Measurements were performed at least 30 min before oxytocin initiation and until at least four contractions per 10 min were recorded by standard tocodynamometry. The delta EUM index was defined as the difference between the mean EUM index before and after the initiation of oxytocin. The mean EUM index increased significantly during oxytocin augmentation in all the parturients (P

In this issue, Hautakangas et al report that "we found significantly lower intrauterine pressure (IUP) in failed Trial Of Labour After Caesarean (TOLAC), compared with Vaginal Birth After Caesarean", although "the dosages of oxytocin tended to be higher among failed TOLACs". They commented that "it seems that raising the oxytocin dosage does not necessarily improve uterine contractile activity in this group". Why is this?

What you need to know Preterm birth and its complications account for nearly one million neonatal deaths each year globally.1 Women who present with a dilated cervix in the second trimester are at increased risk of pregnancy loss and preterm birth (fig 1). They generally have no or minimal preceding symptoms such as change in discharge or mild abdominal pain and are detected to have a dilated cervix on examination. Cervical dilatation may also be detected in the anomaly scan. Fig 1 Premature cervical dilatation and exposed unruptured fetal membranes International guidelines from the US, UK, and Canada suggest considering emergency cervical cerclage, or rescue cerclage, to prevent preterm birth in women presenting with cervical dilatation and exposed fetal membranes between 16 and 28 weeks of gestation if there are no signs of bleeding, infection, or uterine activity (see box 1).2 Box 1 ### NICE guidelines for management of preterm labour and birth (NG25)2 #### Women with a closed cervixRETURN TO TEXT

Objective To describe the outcomes of patients transferred to King Edward Memorial Hospital (KEMH) with signs of labour at preterm gestations. Design A retrospective observational study of the 69 cases transferred to KEMH during 2015. Setting Patient transfers from all locations across Western Australia (WA) to the sole tertiary perinatal centre in Perth. Participants Pregnant women within WA with threatened or actual preterm labour (PTL) or preterm prelabour rupture of membranes (PPROM) between 23 and 32 weeks gestation. Main outcome measures The occurrence of delivery during the admission and time-to-delivery as well as length of admission and association between clinical factors and time-to-delivery. Results The percentage of the study population delivered during the admission following transfer was 72.5%. Eighty-six per cent of those who delivered did so within 72 hours of transfer. The median time from transfer to delivery was 1 day. Sixty-three per cent of those who did not deliver during the admission progressed to 36 weeks gestation. Patients transferred with PPROM were less likely to deliver during the admission compared to those with uterine activity (50% versus 19.6%, P = 0.007) and nulliparas were more likely to deliver (93.5% versus 55.3%, P Conclusion The majority of women transferred with signs of PTL progress to delivery during the same admission with the highest risk of delivery being the first 72 hours following transfer. If the pregnancy is ongoing at 72 hours, there is a reasonable chance of progression to late preterm gestation supporting the return of woman to their place of origin for antenatal care following discharge.

Antenatal corticosteroids before preterm births are considered to be most effective 48 h after and up to 1 week after the first dose. We aim to evaluate: (1) our practice of administration of antenatal corticosteroids in Singapore; (2) admission characteristics of women for discriminatory factors of spontaneous preterm delivery within 1 week. This is a retrospective study in a tertiary obstetrics unit in Singapore. 3044 women who delivered live births (at term or preterm) from 1st November 2014 to 31st January 2015, who were at risk of spontaneous or induced preterm delivery, were included. Data related to the time of and indications for antenatal corticosteroids administration, characteristics assessed at the time of presentation, and time of delivery were collected and analysed. 85.9% of women who delivered at gestational age ≤ 34+6 received at least one dose. 22.1% had delivered within the window of efficacy. Gestational age > 32 weeks at presentation, uterine activity on tocography and cervical dilation with effacement were associated with preterm delivery within 1 week of presentation. Overall, our antenatal corticosteroids administration rate is comparable to that attained by centres in other developed countries. It is difficult but important to discriminate women who will benefit from this timely intervention.

Objective: Electronic fetal monitoring (EFM) correlates poorly with neonatal outcome. We present a new metric: the “Fetal Reserve Index” (FRI), formally incorporating EFM with maternal, obstetrical, fetal risk factors, and excessive uterine activity for assessment of risk for cerebral palsy (CP). Methods: We performed a retrospective, case-control series of 50 term CP cases with apparent intrapartum neurological injury and 200 controls. All were deemed neurologically normal on admission. We compared the FRI against ACOG Category (I-III) system and long-term outcome parameters against ACOG monograph (NEACP) requirements for labor-induced fetal neurological injury. Results: Abnormal FRI's identified 100% of CP cases and did so hours before injury. ACOG Category III identified only 44% and much later. Retrospective ACOG monograph criteria were found in at most 30% of intrapartum-acquired CP patients; only 27% had umbilical or neonatal pH Conclusions: In this initial, retrospective trial, an abnormal FRI identified all cases of labor-related neurological injury more reliably and earlier than Category III, which may allow fetal therapy by intrauterine resuscitation. The combination of traditional EFM with maternal, obstetrical, and fetal risk factors creating the FRI performed much better as a screening test than EFM alone. Our quantified screening system needs further evaluation in prospective trials.

Monitoring uterine contractions is essential during pregnancy and labor to obtain information on time-to-delivery and maternal and fetal wellbeing Intrauterine pressure (IUP) is considered the "gold standard" to monitor uterine activity, though it requires membrane rupture and is highly invasive. Considering that uterine mechanical activity is a direct consequence of uterine myoelectrical activity, IUP signal can be non-invasively estimated from abdominal electrohysterogram (EHG) recordings. Previous works have reported EHG-based IUP estimates with linear parameters as root-mean-square or Teager energy. Due to non-linear nature of biological processes, the aim of this study was to test the performance of different non-linear EHG parameters to estimate IUP signal. Simultaneous EHG and IUP recordings were conducted in 17 women during labour. Teager energy (TE), Sample entropy (SampEn), Spectral entropy (SpEn), Lempel-Ziv (LZ), and Poincare parameters: SD1, SD2, SDRR and SD1/SD2 were computed from the EHG. Different window lengths for computation and for a smoothing moving average filter were tested. Mono-variable linear regression models were used to obtain IUP estimates. The best results were obtained with TE and SD1, both computed and filtered with windows of 5 s and 20 s, respectively. In the latter case, the RMSerror was 12.25 +/- 4.03 mmHg, which points that non-linear EHG parameters can provide relevant information for non-invasive uterine activity monitoring.

Stillbirths are a worldwide challenge, with an estimated 2.6 million stillbirths in 2015, of these 1.3 million are estimated to have died during labour and birth, i.e. fresh stillbirth. In addition to the 2.6 million, one million newborns die within their first and only day of life. Complications due to birth asphyxia are the primary cause of these deaths. The vast majority, 98%, of stillbirths and early neonatal deaths are found in low resource settings. This thesis investigates two main challenges related to neonatal deaths, 1) fetal heart rate (FHR) and labour analysis, and 2) improving newborn resuscitation. The FHR is known to be important for effectively assessing the well-being of the fetus during labour. In high resource countries, the FHR is measured using cardiotocography for all high-risk labours. While in low income countries, assessment of the FHR is often done manually using a Pinard. With the use of continuous FHR monitoring in low income countries, abnormalities in FHR could potentially be identified at an earlier stage. In this thesis, we facilitate for further analysis of FHR signals by proposing a method to remove less trustworthy time periods of the measured signal, such as noise. And how missing data can be estimated using dictionary learning to allow for continuous analysis. The FHR signals should be interpreted in combination with the uterine activity. We therefore propose a method for detecting uterine contractions using an accelerometer mounted together with the Doppler ultrasound FHR sensor. Finally, we explore how FHR develops during labour and how this trend differs for labours with a normal and adverse outcomes. For newborns who are unable to start breathing themselves after birth, immediate help from the healthcare workers are crucial. We therefore explore which parameters during newborn resuscitation are important for the resuscitation outcome. One of the identified parameters is the amount of stimulation, i.e. rubbing the back of the newborn. To get a greater understanding of how stimulation affects newborn resuscitation, and how it should be applied, a large number of annotated resuscitation episodes are required. Manual annotation is both a time consuming and challenging process for the reviewer. We have proposed a complete system for automatically annotating stimulation by using ECG and accelerometer signals measured on the abdomen of the newborn.

In this chapter, we shall deal with the mechanism of normal labour, how to diagnose labour and the influence the power (uterine activity), passage (maternal pelvis) and passenger (fetus) have on this process. What are the signs of true labour? What do the guidelines say regarding false labour pains, causes of onset of labour, normal physiology, stages of labour, mechanism of labour clinical course of stages of labour and its management?

The rate of labor induction is steadily increasing and, in industrialized countries, approximately one out of four pregnant women has their labor induced. Induction of labor should be considered when the benefits of prompt vaginal delivery outweigh the maternal and/or fetal risks of waiting for the spontaneous onset of labor. However, this procedure is not free of risks, which include an increase in operative vaginal or caesarean delivery and excessive uterine activity with risk of fetal heart rate abnormalities. A search for “Induction of Labor” retrieves more than 18,000 citations from 1844 to the present day. The aim of this review is to summarize the controversies concerning the indications, the methods, and the tools for evaluating the success of the procedure, with an emphasis on the scientific evidence behind each.

Because of the trend to postpone childbirth, the rate of couples dealing with infertility is rapidly increasing and approaching 20%. In-vitro fertilization (IVF) represents the only reproduction option in Europe for 2.5 million couples. However, its success rate remains below 30%. There is clear consensus on a major involvement of uterine contractions in IVF failure, especially during and after embryo transfer. Quantitative and non-invasive measurement of uterine (peristaltic) activity, combined with accurate interpretation and classification methods, can provide an important contribution towards improved IVF success rates. Therefore, this study investigates the use of machine learning for probabilistic classification of the uterine activity, as either favorable or adverse to embryo implantation. The results obtained in 16 patients undergoing an IVF cycle confirm the ability to predict successful embryo implantation by ultrasound uterine motion analysis combined with machine learning.

Fetal well-being is commonly assessed by monitoring the fetal heart rate (fHR). In clinical practice, the de facto standard technology for fHR monitoring is based on the Doppler ultrasound (US). Continuous monitoring of the fHR before and during labor is performed using a US transducer fixed on the maternal abdomen. The continuous fHR monitoring, together with simultaneous monitoring of the uterine activity, is referred to as cardiotocography (CTG). In contrast, for intermittent measurements of the fHR, a handheld Doppler US transducer is typically used. In this article, the technology of Doppler US for continuous fHR monitoring and intermittent fHR measurements is described, with emphasis on fHR monitoring for CTG. Special attention is dedicated to the measurement environment, which includes the clinical setting in which fHR monitoring is commonly performed. In addition, to understand the signal content of acquired Doppler US signals, the anatomy and physiology of the fetal heart and the surrounding maternal abdomen are described. The challenges encountered in these measurements have led to different technological strategies, which are presented and critically discussed, with a focus on the US transducer geometry, Doppler signal processing, and fHR extraction methods.

[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.

Objetivo: describir las características del proceso de inducción del trabajo de parto relacionadas con el exceso de cesáreas en las mujeres a quienes se les realiza este procedimiento. Materiales y Métodos: cohorte histórica descriptiva en la que se incluyeron las gestantes sin antecedente de cesárea, con embarazo único, a término y presentación cefálica, sometidas a inducción del trabajo de parto en un hospital de tercer nivel de complejidad, en Medellín, Colombia, en el periodo de mayo de 2015 a octubre de 2016. Se excluyeron mujeres con feto muerto antes de la inducción. Muestreo consecutivo. Se midieron: edad materna, paridad, edad gestacional, indicación de la inducción del trabajo de parto, favorabilidad del cérvix, tiempo de la inducción, calidad de la actividad uterina lograda, tipo de parto, momento de la inducción en que se decide la cesárea. Para definir el cumplimiento de las recomendaciones de inducción se tuvieron como referencia las guías de práctica clínica de organizaciones internacionales de la especialidad y las nuevas directrices generadas en la propuesta de reducción de la primera cesárea del año 2012. Se utilizó estadística descriptiva. Resultados: de 2402 nacimientos se seleccionaron 289 que cumplieron con los criterios de inclusión. Se realizó cesárea al 48 % de las gestantes, a 60,8 % de las nulíparas y a 32,1 % de las multíparas someti- das a inducción. El 72,2 % de las que tenían cérvix desfavorable recibieron oxitocina como método de maduración cervical. A 108 (37%) de las mujeres llevadas a inducción de parto se les realizó cesárea por diagnóstico de inducción fallida que se conside- ró inadecuado en todas ellas, ya que el diagnóstico se realizó antes de alcanzar 6 cm de dilatación en 88 (81,5 %), con membranas íntegras en 67 (62 %), sin actividad uterina en 42 (38,9 %), con actividad uterina de mala calidad en 23 (21,3 %) y 55 (61 %), no tuvieron al menos 24 horas de fase latente antes de realizar la cesárea. Conclusión: se encontró falta de cumplimiento de las recomendaciones para una adecuada inducción que lleva a un diagnóstico errado de inducción fallida.

Recent warnings postulate a possible damaging effect of volatile anesthetics on the fetus. In our archive of fetal surgeries, we found wide variation in dosing of volatile anesthetics during spina bifida surgeries. We hypothesized that there was an association between volatile anesthetic exposure and uterine activity.Sixty anesthesia records from spina bifida operations were assessed. We analyzed the course of the administered volatile anesthetic during surgery and calculated from each patient's anesthesia record the volatile anesthetic exposure expressed in vol%h. We divided the records into two post hoc groups of the 20 lowest exposure (Group L) versus the 20 highest exposure (Group H), and compared them for uterine activity and fetal heart rate.The number of contractions per hour was significantly greater in Group H (mean 1.3, SD ± 1.2) compared with Group L (mean 0.5, SD ± 0.6, P=0.049). There was no difference between the groups for the administration of the tocolytic drug atosiban (P=0.29). The course of the mean arterial pressure did not significantly differ but group H needed significantly more vasoactive medication (P0.05).We found that a lower intra-operative volatile anesthetic exposure than recommended in the MOMS-trial (i.e.2.0 minimum alveolar concentration [MAC]) was not associated with an increase in intra-operative uterine activity. This is an indication that during spina bifida surgery, 2.0 MAC may not be necessary to avoid potentially harmful uterine activity.

Cardiotocography is the most widely used method in obstetrics practice for monitoring fetal health status. The main goal of monitoring is early detection of fetal hypoxia. A cardiotocogram is a recording of fetal heart rate and uterine activity signals. The accurate analysis of cardiotocograms is critical for further treatment. Therefore, fetal state assessment using machine learning methods from cardiotocogram data has received significant attention in the literature. In this paper, a comparative study of fetal state assessment is presented by using three artificial neural network models, namely the multilayer perceptron neural network, probabilistic neural network, and generalized regression neural network. The performances of the models are evaluated using publicly available cardiotocogram data by running a tenfold cross-validation procedure. The models' performances are compared in terms of overall classification accuracy. For further analysis, receiver operation characteristic analysis and the cobweb representation technique are used.

Evaluation of synchronization between myoelectric signals can give new insights into the functioning of the complex system of porcine myometrium. We propose a model of uterine contractions according to the hypothesis of action potentials similarity which is possible to detect during propagation in the uterine wall. We introduce similarity measures based on the concept of synchronization as used in matching linear signals such as electromyographic (EMG) time series data. The aim was to present linear measures to assess synchronization between contractions in different topographic regions of the uterus. We use the cross-correlation function (ƒx,y[l], ƒy,z[l]) and the cross-coherence function (Cxy[ƒ], Cyz[ƒ]) to assess synchronization between three data series of a diestral uterine EMG bundles in porcine reproductive tract. Spontaneous uterine activity was recorded using telemetry method directly by three-channel transmitter and three silver bipolar needle electrodes sutured on different topographic regions of the reproductive tract in the sow. The results show the usefulness of the cross-coherence function in that synchronization between uterine horn and corpus uteri for multiple action potentials (bundles) could be observed. The EMG bundles synchronization may be used to investigate the direction and velocity of EMG signals propagation in porcine reproductive tract.

Optic neuritis secondary to systemic lupus erythematosus is a rare manifestation with a prevalence of 1%. The case described concerns a patient who presented with optic neuritis associated with SLE. She was 19 weeks pregnant, and required pulses with methylprednisolone and cyclophosphamide, which are within the category D drugs used during pregnancy. Three weeks later, she presented with uterine activity, and went into labor, with a fetus of 22 weeks gestation and weighing 430 g being obtained, which died 48 h later. In medical practice there are ethical guidelines and economic obstacles to carrying out diagnostic and therapeutic protocols established by limiting clinical practice.

Background Studies which use external tocography to explore the relationship between increased intrapartum uterine activity and foetal outcomes are feasible because the technology is safe and ubiquitous. However, periods of poor signal quality are common. We developed an algorithm which aims to calculate tocograph summary variables based on well-recorded contractions only, ignoring artefact and excluding sections deemed uninterpretable. The aim of this study was to test that algorithm's reliability. Methods Whole recordings from labours at ≥35 weeks of gestation were randomly selected without regard to quality. Contractions and rest intervals were measured by two humans independently, and by the algorithm using two sets of models; one based on a series of pre-defined thresholds, and another trained to imitate one of the human interpreters. The absolute agreement intraclass correlation coefficient (ICC) was calculated using a two-way random effects model. Results The training dataset included data from 106 tocographs. Of the tested algorithms, AdaBoost showed the highest initial cross-validated accuracy and proceeded to optimization. Forty tocographs were included in the validation set. The ICCs for the per tocograph mean contraction rates were; human B to human A: 0.940 (0.890–0.968), human A to initial models: 0.944 (0.898–0.970), human A to trained models 0.962 (0.927–0.980), human B to initial models: 0.930 (0.872–0.962), human B to trained models: 0.948 (0.903–0.972). Conclusions The algorithm described approximates interpretation of external tocography performed by trained humans. The performance of the AdaBoost trained models was marginally superior compared to the initial models.