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Objective: A cesarean delivery (CD) can affect health of both mother and child and future pregnancies. Since the abandonment of the one-child policy in China, obstetricians tend to perform a repeat CD rather than a trial of labor after cesarean (TOLAC). This study aims to reduce CD rates by increasing vaginal births after cesarean (VBAC) rates and introducing electrohysterography (EHG) for accurate monitoring. Methods: In total, 82 women received counseling regarding TOLAC at the Shijiazhuang Sixth Hospital in China. Women opting for TOLAC were randomized for either external tocodynamometry (TOCO, i.e. standard care) or EHG. The primary outcome was the VBAC rate. Secondary outcomes were indications for CD, percentage of assisted vaginal deliveries, labor duration, maternal blood loss, complications and neonatal outcomes. Results: After accounting for preterm delivery and dropouts, all counseled women opted for a TOLAC (100%). After randomization, 42 women were included in the TOCO-group and 37 in the EHG-group. Women did not receive pain medication and labor was not augmented with oxytocin. The VBAC rate was 71.4% in the TOCO-group, versus 78.4% in the EHG-group (p =.48). Birth was assisted with forceps in 11.9% of TOCO-group versus 2.7% of EHG-group (p =.21). One secondary CD (i.e. a shift from intended vaginal delivery to surgical delivery within the same labor) was performed because of a suspicion of uterine rupture (TOCO-group). Other indications for CD were: fetal distress, labor dystocia, fetal position, cephalopelvic disproportion. There were no significant differences in secondary study outcomes. No complications were reported. Conclusion: This study showed an average VBAC rate of 75%, without any complications, in a hospital with no previous experience with TOLAC. The VBAC rate with EHG-monitoring was higher than TOCO, although this difference was not significant. To demonstrate a significant difference, larger clinical studies are necessary. Trial registration: The Daily Board of the Medical Ethics Committee of The Maternal and Child Hospital of Shijiazhuang approved the study protocol (number 20171018, Dutch Trial Register NL8199). [ABSTRACT FROM AUTHOR]

The evaluation of uterine contraction offers significant information regarding the progression of labour. The occurrence of deliveries before the expected dates leads to undesirable consequences for the mother and fetus. Electrohysterography (EHG) is a non-invasive monitoring scheme generally preferred to detect preterm delivery and reduce the hostile consequences. This paper proposed a novel term and preterm prediction method for predicting preterm deliveries by employing EHG signals. The proposed scheme involves three phases: pre-processing, feature extraction and prediction. Initially, the acquired EHG signals are pre-processed using a band pass filter and wavelet transform to remove noise and artefacts from the EHG signal. The second stage is feature extraction, where the representative features, including Shannon energy, median frequency, time-varying centroid frequency, etc., are extracted. At last, the prediction is carried out using an enhanced sheep flock optimized hybrid extreme artificial neural learning network (ESFHEANL). ESFHEANL is the combination of a hybrid extreme artificial neural learning network (HEANL) and enhanced sheep flock optimization (ESFO) algorithm. It assists in diagnosing the term and preterm birth more accurately. The proposed scheme is implemented in the Python platform and assessed the performance in terms of accuracy, recall, specificity and f-measure using the term-preterm EHG (TPEHG) database. Finally, the experimental outcomes evidenced that the proposed scheme achieved better performance and was employed to diagnose term and preterm births accurately. [ABSTRACT FROM AUTHOR]

Investigating the fluctuations of uterine contractions is an indispensable diagnostic practice to evaluate the onset of labor. This work aims to differentiate the fluctuations associated with Term (gestation ≥ 37 weeks) pregnancies during varied gestational ages using electrohysterography (EHG) signals. The signals in the second and third trimesters are subjected to Multifractal-Empirical Mode Decomposition-based Detrended Fluctuation Analysis (MF-EMDDFA). Multifractal spectrum is estimated and features namely, Holder exponents ( α L , α R ), broadness (BD), root slopes (LS, RS) and tangential slopes (TL, TR) are extracted. Machine learning methods, such as Naïve Bayes, adaptive boosting and random forest (RF) classifiers, are utilized to discriminate the contractions during different weeks of gestation. Results show that five multifractal features, namely α L , α R , BD, LS and TL, show significant differences between the considered gestational ages. An increase in regularity of the uterine contractions is observed as the gestational age increases. These features along with the RF classifier provide an accuracy, sensitivity, precision, recall and F1-score of greater than 98.30%. As the differentiation of uterine contractions during different weeks of gestation is vital to understanding the pregnancy progress, the proposed multifractal features could be used as potential biomarkers in clinical practices. [ABSTRACT FROM AUTHOR]

• Currently there is no accurate method available for postpartum uterine monitoring. • Uterine atony causing postpartum hemorrhage is associated with maternal mortality and morbidity. • Electrohysterography could be a first method to objectify postpartum uterine activity. Uterine contractions are essential for childbirth, but also for expulsion of the placenta and for limiting postpartum blood loss. Postpartum hemorrhage is associated with almost 25% of the maternal deaths worldwide and the leading cause of maternal death in most low-income countries. Little is known about the physiology of the uterus postpartum, particularly due to the lack of an accurate measurement tool. The primary objective of this pilot study is to explore the potential of using electrohysterography to detect postpartum uterine contractions. If postpartum uterine activity can be objectified, this could contribute to understanding the physiology of the uterus and improve diagnosis and treatment of postpartum hemorrhage. In this observational study we included women aiming for a vaginal birth in two large maternity clinics in the Netherlands, Amphia Hospital Breda (group A, N2018-0161) and Máxima Medical Center Veldhoven (group B, N17.149). An electrode patch was placed on the maternal abdomen to record real-time electrical uterine activity until one hour postpartum continuously. In group A, the placement of the patch was lower than in group B. For analysis, tracings were divided into five different phases (1: dilatation until start pushing, 2: from start pushing until childbirth, 3: from childbirth until placental expulsion, 4: first hour after placental expulsion and 5: after one hour postpartum). Readability, signal quality and contraction frequency per hour were assessed. Additionally, patient satisfaction was evaluated through a survey. In total 91 pregnant women were included of whom 45 in group A and 46 women in group B. Complete registrations were obtained throughout the five labor phases with very little artefacts or signal loss. The readability of the tracings decreased after childbirth. A significantly better readability was found in tracings where the patch placement was lower on the abdomen for phases 4 and 5. Contraction frequency was highest during phase 2 and decreased towards phase 5. Women rated the satisfaction with electrohysterography as high and mostly did not notice the patch. It is possible to detect uterine activity postpartum with electrohysterography. Further investigation is recommended to improve diagnosis and treatment of postpartum hemorrhage. [ABSTRACT FROM AUTHOR]

Effective and safe labour requires good cooperation of all the physiological systems. A proper synchronization of uterine and abdominal muscles is necessary for labour progression. Therefore, a new method for simultaneous monitoring of uterine activities and parturient’s pushing efforts is presented. A high sampled, rectified electrohysterographic signal is divided into a low, uterine passband (0.1-3.00Hz) and a high, muscular (40-100Hz) one. The time-dependent mean frequencies arse estimated for each passband separately. At the moments of uterine contraction the time-dependent LOW mean frequency was locally increased. During parturient’s pushing effort the HIGH mean frequency was increased in the manner typical for the skeletal muscles. It seems that the proposed method would be less sensitive to a measuring noise than the previously published RMS based estimators. Moreover, the proposed method enables to monitor fatigue of a uterus or abdominal muscles during the prolonged 2nd stage of a labour. It can be helpful to make a decision of Caesarean section.

Introduction: Fetal electrocardiography (NI‐fECG) and electrohysterography (EHG) have been proven more accurate and reliable than conventional non‐invasive methods (doppler ultrasound and tocodynamometry) and are less affected by maternal obesity. It is still unknown whether NI‐fECG and EHG will eliminate the need for invasive methods, such as the intrauterine pressure catheter and fetal scalp electrode. We studied whether NI‐fECG and EHG can be successfully used during labor. Material and Methods: A prospective clinical pilot study was performed in a tertiary care teaching hospital. A total of 50 women were included with a singleton pregnancy with a gestational age between 36+0 and 42+0 weeks and had an indication for continuous intrapartum monitoring. The primary study outcome was the percentage of women with NI‐fECG and EHG monitoring throughout the whole delivery. Secondary study outcomes were reason and timing of a switch to conventional monitoring methods (i.e., tocodynamometry and fetal scalp electrode or doppler ultrasound), repositioning of the abdominal electrode patch, success rates (i.e., the percentage of time with signal output), and obstetric and neonatal outcomes. Clinical trial registration: Dutch trial register (NL8024). Results: In 45 women (90%), NI‐fECG and EHG monitoring was used throughout the whole delivery. In the other five women (10%), there was a switch to conventional methods: in two women because of insufficient registration quality of uterine contractions and in three women because of insufficient registration quality of the fetal heart rate. In three out of five cases, the switch was after full dilation was reached. Repositioning of the abdominal electrode patch occurred in two women. The overall success rate was 94.5%. In 16% (n = 8) of women, a cesarean delivery was performed due to non‐progressing dilation (n = 7) and due to suspicion of fetal distress (n = 1). Neonatal metabolic acidosis did not occur. Two neonates (4%) were admitted to the neonatal intensive care unit for complications not related to intrapartum monitoring. Conclusions: NI‐fECG and EHG can be successfully used during labor in 90% of women. Future research is needed to conclude whether implementation of electrophysiological monitoring can improve obstetric and neonatal outcomes. [ABSTRACT FROM AUTHOR]

Effective and safe labour requires good cooperation of all the physiological systems. A proper synchronization of uterine and abdominal muscles is necessary for labour progression. Therefore, a new method for simultaneous monitoring of uterine activities and parturient's pushing efforts is presented. A high sampled, rectified electrohysterographic signal is divided into a low, uterine passband (0.1-3.00Hz) and a high, muscular (40-100Hz) one. The time-dependent mean frequencies arse estimated for each passband separately. At the moments of uterine contraction the time-dependent LOW mean frequency was locally increased. During parturient's pushing effort the HIGH mean frequency was increased in the manner typical for the skeletal muscles. It seems that the proposed method would be less sensitive to a measuring noise than the previously published RMS based estimators. Moreover, the proposed method enables to monitor fatigue of a uterus or abdominal muscles during the prolonged 2nd stage of a labour. It can be helpful to make a decision of Caesarean section. [ABSTRACT FROM AUTHOR]

The analysis of the uterine electrical activity and its propagation patterns could potentially predict the risk of prolonged/arrested progress of labor. In our study, the Electrohysterography (EHG) signals of 83 participants in labor at around 3-4 cm of cervical dilatation, were recorded for about 30 minutes each. These signals were analyzed for predicting prolonged labor. Out of the 83 participants, 70 participants had normal progress of labor and delivered vaginally. The remaining 13 participants had prolonged/ arrested progress of labor and had to deliver through a cesarean section. In this paper, we propose an algorithm to identify contractions from the acquired EHG signals based on the energy of the signals. The role of contraction consistency and fundal dominance was evaluated for impact on progress of the labor. As per our study, the correlation of contractions was higher in case of normal progress of labor. We also observed that the upper uterine segment was dominant in cases with prolonged/arrested progress of labor.

Introduction: Despite vast research, premature birth's electrophysiological mechanisms are not fully understood. Prediction of preterm birth contributes to child survival by providing timely and skilled care to both mother and child. Electrohysterography is an affordable, noninvasive technique that has been highly sensitive in diagnosing preterm labor. This study aimed to choose the more appropriate combination of characteristics, such as electrode channel and bandwidth, as well as those linear, time-frequency, and nonlinear features of the electrohysterogram (EHG) for predicting preterm birth using classifiers. Methods: We analyzed two open-access datasets of 30 minutes of EHG obtained in regular checkups of women around 31 weeks of pregnancy who experienced premature labor (P) and term labor (T). The current approach filtered the raw EHGs in three relevant frequency subbands (0.3-1 Hz, 1-2 Hz, and 2-3Hz). The EHG time series were then segmented to create 120-second windows, from which individual characteristics were calculated. The linear, time-frequency, and nonlinear indices of EHG of each combination (channelfilter) were fed to different classifiers using feature selection techniques. Results: The best performance, i.e., 88.52% accuracy, 83.83% sensitivity, and 93.22% specificity, was obtained in the 2-3 Hz bands using Medium Frequency, Continuous Wavelet Transform (CWT), and entropy-based indices. Interestingly, CWT features were significantly different in all filter-channel combinations. The proposed study uses small samples of EHG signals to diagnose preterm birth accurately, showing their potential application in the clinical environment. Discussion: Our results suggest that CWT and novel entropy-based features of EHG could be suitable descriptors for analyzing and understanding the complex nature of preterm labor mechanisms. [ABSTRACT FROM AUTHOR]

The prolonged latent phase of Induction of Labour (IOL) is associated with increased risks of maternal mortality and morbidity. Electrohysterography (EHG) has outperformed traditional clinical measures monitoring labour progress. Although parity is agreed to be of particular relevance to the success of IOL, no previous EHG‐related studies have been found in the literature. We thus aimed to identify EHG‐biomarkers to predict IOL success (active phase of labour in ≤ 24 h) and determine the influence of the myoelectrical response on the parity of this group. Statistically significant and sustained differences between the successful and failed groups were found from 150 min in amplitude and non‐linear parameters, especially in Spectral Entropy and in their progression rates. In the nulliparous‐parous comparison, parous women showed statistically significantly higher amplitude progression rate. These biomarkers would therefore be useful for early detection of the risk of induction failure and would help to develop more robust and generalizable IOL success‐prediction systems. [ABSTRACT FROM AUTHOR]

Objective: Preterm birth is the leading cause of morbidity and mortality involving over 10% of infants. Tools for timely diagnosis of preterm birth are lacking and the underlying physiological mechanisms are unclear. The aim of the present study is to improve early assessment of pregnancy progression by combining and optimizing a large number of electrohysterography (EHG) features with a dedicated machine learning framework. Methods: A set of reported EHG features are extracted. In addition, novel cross and multichannel entropy and mutual information are employed. The optimal feature set is selected using a wrapper method according to the accuracy of the leave-one-out cross validation. An annotated database of 74 EHG recordings in women with preterm contractions was employed to test the ability of the proposed method to recognize the onset of labor and the risk of preterm birth. Difference between using the contractile segments only and the whole EHG signal was compared. Results: The proposed method produces an accuracy of 96.4% and 90.5% for labor and preterm prediction, respectively, much higher than that reported in previous studies. The best labor prediction was observed with the contraction segments and the best preterm prediction achieved with the whole EHG signal. Entropy features, particularly the newly-employed cross entropy contribute significantly to the optimal feature set for both labor and preterm prediction. Significance: Our results suggest that changes in the EHG, particularly the regularity, might manifest early in pregnancy. Single-channel and cross entropy may therefore provide relevant prognostic opportunities for pregnancy monitoring. [ABSTRACT FROM AUTHOR]

Introduction: Worldwide, cardiotocography is used for continuous monitoring of fetal heart rate (FHR) and uterine contractions during labour. Different methods for FHR registration and registration of contractions are available. Literature about the frequency of use of different fetal monitoring methods is lacking.Objective: To evaluate the use of and preferences for fetal monitoring methods for intrapartum fetal monitoring among Dutch obstetric care providers.Study Design: Between October and November 2020 the Dutch Society of Obstetrics and Gynaecology sent an email invitation to all secondary care midwives and gynaecologists (in training) in the Netherlands to complete an online survey regarding the use and personal experience with fetal monitoring methods. The survey mainly consisted of multiple choice questions. Descriptive statistics are reported. Continuous variables were presented as median with interquartile ranges (IQR). Categorical variables were expressed as numbers with percentages.Results: The response rate was 29 % (n/N = 510/1748). All Dutch hospitals were represented. The respondents estimated the use of fetal scalp electrode (FSE) at 71 % (IQR 58-85 %) of deliveries. The most common indication for use of the FSE was inadequate external FHR registration (94 %). More than half (54 %) of the respondents reported to use intrauterine pressure catheter with an estimated use of 5 % (IQR 2-8 %) of deliveries. The most common indication for use of intrauterine pressure catheter was inadequate external contraction registration (75 %). The use of ST-analysis was reported in 25 % of the respondents with an estimated use of 60 % (IQR 30-72 %) of deliveries. Almost all respondents (99 %) reported to use fetal blood sampling with an estimated use of 15 % (IQR 10-23 %) of deliveries. Ninety percent of respondents would prefer a valid and reliable external monitoring technique during labour. Thirty-one percent of respondents assume that external fetal monitoring with non-invasive fetal electrocardiography and electrohysterography will become standard care within the next 5 years.Conclusions: Currently, the FSE is the most used technique for FHR monitoring during labour in the Netherlands. The most common indication for use of FSE is inadequate external FHR registration. Obstetric care providers would prefer a non-invasive external registration method that provides reliable data. [ABSTRACT FROM AUTHOR]

Preterm birth a common and severe pregnancy complications, causing significant health, development, and economic problems. Accurate diagnosis of imminent labor for women with threatened preterm labor (TPL) is crucial. Electrohysterography (EHG), which represents uterine myometrial electrical activity, is a potential tool for predicting preterm birth. Increased cell synchronization is fundamental to generating high-intensity and coordinated uterine myometrial electrical activity as labor approaches. The present work aimed to evaluate the synchronization measures from multichannel EHG signals to predict labor in less than 24 h (time to delivery, TTD <24 h vs. TTD≥24 h), and between imminent labor (TTD <1 week) and non-imminent labor (TTD≥1 week) in women with TPL. We computed three synchronization measures: the imaginary component of coherence, phase lag index, and weighted phase lag index (wPLI) within three specific frequency bandwidths (fast wave low (FWL): 0.1-0.34 Hz, fast wave high (FWH): 0.34-1 Hz, and whole bandwidth: 0.1-1 Hz) from 115 pregnant women (26-41 weeks of gestation). Our results revealed that multichannel EHG synchronization measures significantly increased closer to delivery (labor > non-labor, imminent > non-imminent). Indeed, wPLI in the FWH bandwidth exhibited a positive correlation with gestational age (p < 0.001，correlation coefficient = 0.35) and an inverse relationship with time to delivery (p < 0.001，correlation coefficient = -0.33). wPLI allows for better distinguishing imminent from non-imminent in women with TPL, especially for those electrode pairs in the vertical direction, which has been reported as the predominant direction of uterine activity propagation. The three synchronization measures computed in FWL and FWH bandwidth provided complementary information for predicting labor in less than 24 h and also imminent labor in women with TPL, achieving an F1-score of 93 % (84.2-93 %) and 99.5 % (85.2-99.5 %) respectively. Our results suggest that EHG synchronization analysis constitutes a new sensitive metrics to discriminate imminent labor which can be potentially used for improving preterm birth prediction and understand uterine electrical activity dynamics., Competing Interests: Declaration of competing interest All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: The postpartum period can be complicated by hemorrhage, frequently caused by uterine atony. Electrohysterography, allowing continuous monitoring of uterine activity, may be a promising alternative for early detection of uterine atony, and thereby contribute to the prevention of postpartum hemorrhage. Associations between electrohysterographic parameters postpartum and total blood loss were studied., Methods: In this prospective explorative study, women were included with a vaginal delivery between 36 +0 and 42 +0 weeks of gestation. Linear regression analysis was used to describe the association between electrohysterographic parameters (i.e. area under the contraction curve (AUC) total (in arbitrary units), AUC from baseline (in arbitrary units), maximum amplitude (in arbitrary units) and baseline tone (in arbitrary units)) and total blood loss (in mL) during the first 30 min postpartum., Results: In total, 25 women were included for analysis, of whom three had postpartum hemorrhage. A moderate positive linear correlation was found between the logarithmically transformed total blood loss and the AUC total (r = 0.44, p = 0.03), AUC from baseline (r = 0.43, p = 0.03) and baseline tone (r = 0.43, p = 0.03). There was no significant linear correlation between the logarithmically transformed total blood loss and maximum amplitude. Explorative analysis revealed a significantly higher AUC total, AUC from baseline and baseline tone in women with postpartum hemorrhage compared to women without postpartum hemorrhage. (p = 0.02, p = 0.02 and p = 0.03, respectively)., Discussion: There is a moderate positive correlation between the logarithmically transformed total blood loss and the AUC total, AUC from baseline and baseline tone. Also, these parameters were all significantly higher in women with postpartum hemorrhage compared to women without postpartum hemorrhage., Competing Interests: Declaration of competing interest Author G.O. initiated the scientific research from which Nemo Healthcare B.V. (Veldhoven, the Netherlands) and the described NFMS device have originated. There are no financial relationships between Nemo Healthcare B.V. (Veldhoven, the Netherlands) and any of the authors., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Abstract Background Worldwide, hypertensive disorders of pregnancy (HDP), fetal growth restriction (FGR) and preterm birth remain the leading causes of maternal and fetal pregnancy-related mortality and (long-term) morbidity. Fetal cardiac deformation changes can be the first sign of placental dysfunction, which is associated with HDP, FGR and preterm birth. In addition, preterm birth is likely associated with changes in electrical activity across the uterine muscle. Therefore, fetal cardiac function and uterine activity can be used for the early detection of these complications in pregnancy. Fetal cardiac function and uterine activity can be assessed by two-dimensional speckle-tracking echocardiography (2D-STE), non-invasive fetal electrocardiography (NI-fECG), and electrohysterography (EHG). This study aims to generate reference values for 2D-STE, NI-fECG and EHG parameters during the second trimester of pregnancy and to investigate the diagnostic potential of these parameters in the early detection of HDP, FGR and preterm birth. Methods In this longitudinal prospective cohort study, eligible women will be recruited from a tertiary care hospital and a primary midwifery practice. In total, 594 initially healthy pregnant women with an uncomplicated singleton pregnancy will be included. Recordings of NI-fECG and EHG will be made weekly from 22 until 28 weeks of gestation and 2D-STE measurements will be performed 4-weekly at 16, 20, 24 and 28 weeks gestational age. Retrospectively, pregnancies complicated with pregnancy-related diseases will be excluded from the cohort. Reference values for 2D-STE, NI-fECG and EHG parameters will be assessed in uncomplicated pregnancies. After, 2D-STE, NI-fCG and EHG parameters measured during gestation in complicated pregnancies will be compared with these reference values. Discussion This will be the a large prospective study investigating new technologies that could potentially have a high impact on antepartum fetal monitoring. Trial registration Registered on 26 March 2020 in the Dutch Trial Register (NL8769) via https://www.trialregister.nl/trials and registered on 21 October 2020 to the Central Committee on Research Involving Human Subjects (NL73607.015.20) via https://www.toetsingonline.nl/to/ccmo_search.nsf/Searchform?OpenForm .

IntroductionDespite vast research, premature birth's electrophysiological mechanisms are not fully understood. Prediction of preterm birth contributes to child survival by providing timely and skilled care to both mother and child. Electrohysterography is an affordable, noninvasive technique that has been highly sensitive in diagnosing preterm labor. This study aimed to choose the more appropriate combination of characteristics, such as electrode channel and bandwidth, as well as those linear, time-frequency, and nonlinear features of the electrohysterogram (EHG) for predicting preterm birth using classifiers.MethodsWe analyzed two open-access datasets of 30 minutes of EHG obtained in regular checkups of women around 31 weeks of pregnancy who experienced premature labor (P) and term labor (T). The current approach filtered the raw EHGs in three relevant frequency subbands (0.3–1 Hz, 1–2 Hz, and 2–3Hz). The EHG time series were then segmented to create 120-second windows, from which individual characteristics were calculated. The linear, time-frequency, and nonlinear indices of EHG of each combination (channel-filter) were fed to different classifiers using feature selection techniques. ResultsThe best performance, i.e., 88.52% accuracy, 83.83% sensitivity, and 93.22% specificity, was obtained in the 2–3 Hz bands using Medium Frequency, Continuous Wavelet Transform (CWT), and entropy-based indices. Interestingly, CWT features were significantly different in all filter-channel combinations. The proposed study uses small samples of EHG signals to diagnose preterm birth accurately, showing their potential application in the clinical environment.DiscussionOur results suggest that CWT and novel entropy-based features of EHG could be suitable descriptors for analyzing and understanding the complex nature of preterm labor mechanisms.

• A deep learning-based automatic semantic segmentation of EHG recordings is proposed. • A screening tool for physiological EHG epoch segmentation in clinical practice. • The best architecture for detecting physiological epochs was UNET 3+ with 91.4 % AUC. • Automatic and double-blind segmentation perform similarly in preterm labor detection. Preterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sensitivity. Despite the technological progress made in predicting preterm labor, its use in clinical practice is still limited, one of the main barriers being the lack of tools for automatic signal processing without expert supervision, i.e. automatic screening of motion and respiratory artifacts in EHG records. Our main objective was thus to design and validate an automatic system of segmenting and screening the physiological segments of uterine origin in EHG records for robust characterization of uterine myoelectric activity, predicting preterm labor and help to promote the transferability of the EHG technique to clinical practice. For this, we combined 300 EHG recordings from the TPEHG DS database and 69 EHG recordings from our own database (Ci2B-La Fe) of women with singleton gestations. This dataset was used to train and evaluate U-Net, U-Net++, and U-Net 3+ for semantic segmentation of the physiological and artifacted segments of EHG signals. The model's predictions were then fine-tuned by post-processing. U-Net 3+ outperformed the other models, achieving an area under the ROC curve of 91.4 % and an average precision of 96.4 % in detecting physiological activity. Thresholds from 0.6 to 0.8 achieved precision from 93.7 % to 97.4 % and specificity from 81.7 % to 94.5 %, detecting high-quality physiological segments while maintaining a trade-off between recall and specificity. Post-processing improved the model's adaptability by fine-tuning both the physiological and corrupted segments, ensuring accurate artifact detection while maintaining physiological segment integrity in EHG signals. As automatic segmentation proved to be as effective as double-blind manual segmentation in predicting preterm labor, this automatic segmentation tool fills a crucial gap in the existing preterm delivery prediction system workflow by eliminating the need for double-blind segmentation by experts and facilitates the practical clinical use of EHG. This work potentially contributes to the early detection of authentic preterm labor women and will allow clinicians to design individual patient strategies for maternal health surveillance systems and predict adverse pregnancy outcomes. [ABSTRACT FROM AUTHOR]

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models' real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics. [ABSTRACT FROM AUTHOR]

In-vitro fertilization (IVF) is the most advanced treatment for infertility problems; however, its failure rate is still above 70% and the exact causes are often unknown. There is increasing evidence of the involvement of uterine contractions in IVF failure, especially during and after embryo transfer (ET). In this paper, we propose a new method to predict the success of IVF based on quantitative features extracted from electrohysterography (EHG) and B-mode transvaginal ultrasound (TVUS) recordings. To this end, probabilistic classification of the uterine activity, as either favorable or adverse to embryo implantation, is investigated using machine learning. Prior to machine learning, an additional method for EHG and TVUS feature extraction is here proposed that is based on singular value decomposition of the acquired EHG and TVUS recordings. Sixteen women were measured during three phases of the IVF treatment: follicular stimulation (FS), one hour before embryo transfer (ET1), and five to seven days after ET (ET5-7). After feature space reduction by correlation filtering, three machine-learning models, namely, support vector machine (SVM), K-nearest neighbors (KNN), and Gaussian mixture model (GMM), were optimized and tested by nested leave-one-out cross validation for their ability to predict successful embryo implantation. The highest accuracy (93.8%) was achieved by KNN in all phases and by SVM and in the FS and ET1 phases. Contraction frequency, unnormalized first moment and standard deviation, obtained from EHG and TVUS analysis, were the best features selected by the three classifiers. Our results show a multi-modal, multi-parametric strategy based on quantitative features to represent a novel, promising option for prediction of successful embryo implantation, overcoming the limitations of alternative approaches based on qualitative assessment of clinical variables. Yet, a larger dataset is required for improved training of the classifiers, as well as to assess their clinical value in the context of IVF procedures.

Early detection of preterm labor is important to avoid neonatal death and mortality. Uterine electromyography (UEMG) or electrohysterography is a non-invasive method of extracting electrical activity signal from the abdominal part during pregnancy, which helps in early detection. This signal can be used to classify term and preterm labors. Herein, the performances of four classifiers have been evaluated using seven nonlinear features extracted from UEMG signals. They were then compared with four features analyzed from different literature. The results show that with the Elman neural network classifier, the bi-spectrum feature, which has phase information, outperforms other features with 99.8875% accuracy, 100% sensitivity, and 99.77% specificity. [ABSTRACT FROM AUTHOR]

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.

Objective. This study aims to use recurrence quantification analysis (RQA) of uterine vectormyometriogram (VMG) created from the slow wave (SW) and high wave (HW) bands of electrohysterogram (EHG) signals and assess the directionality of the EHG activity (horizontal or X , vertical or Y ) in normal-weight (NW) and overweight (OW) women during the first stage of labor. Approach . The study involved 41 parturient women (NW = 21 and OW = 20) during the first stage of labor, all of whom were attended at the Gynecology and Obstetrics Hospital of the Maternal and Child Institute of the State of Mexico in Toluca, Mexico. Twenty-minute EHG signals were analyzed in horizontal and vertical directions. Linear and nonlinear indices such as dominant frequency (Dom), Sample Entropy (SampEn), and RQA measures of VMG were computed for SW and HW bands. Main results . Significant differences in SampEn and Dom were observed in the SW band between NW and OW in both X and Y directions, indicating more regular dynamics of electrical uterine activity and a higher Dom in NW parturient women compared to OW women. Additionally, the RQA indices calculated from the VMG of SW were consistent and revealed that NW women exhibit more regular dynamics compared to OW women. Significance . The study demonstrates that RQA of VMG signals and EHG directionality differentiate uterine activity between NW and OW women during the first stage of labor. These findings suggest that the uterine vector may become more periodic, predictable, and stable in NW women compared to OW women. This highlights the importance of tailored clinical strategies for managing labor in OW women to improve maternal and infant outcomes., (Creative Commons Attribution license.)

Background: Worldwide, hypertensive disorders of pregnancy (HDP), fetal growth restriction (FGR) and preterm birth remain the leading causes of maternal and fetal pregnancy-related mortality and (long-term) morbidity. Fetal cardiac deformation changes can be the first sign of placental dysfunction, which is associated with HDP, FGR and preterm birth. In addition, preterm birth is likely associated with changes in electrical activity across the uterine muscle. Therefore, fetal cardiac function and uterine activity can be used for the early detection of these complications in pregnancy. Fetal cardiac function and uterine activity can be assessed by two-dimensional speckle-tracking echocardiography (2D-STE), non-invasive fetal electrocardiography (NI-fECG), and electrohysterography (EHG). This study aims to generate reference values for 2D-STE, NI-fECG and EHG parameters during the second trimester of pregnancy and to investigate the diagnostic potential of these parameters in the early detection of HDP, FGR and preterm birth.Methods: In this longitudinal prospective cohort study, eligible women will be recruited from a tertiary care hospital and a primary midwifery practice. In total, 594 initially healthy pregnant women with an uncomplicated singleton pregnancy will be included. Recordings of NI-fECG and EHG will be made weekly from 22 until 28 weeks of gestation and 2D-STE measurements will be performed 4-weekly at 16, 20, 24 and 28 weeks gestational age. Retrospectively, pregnancies complicated with pregnancy-related diseases will be excluded from the cohort. Reference values for 2D-STE, NI-fECG and EHG parameters will be assessed in uncomplicated pregnancies. After, 2D-STE, NI-fCG and EHG parameters measured during gestation in complicated pregnancies will be compared with these reference values.Discussion: This will be the a large prospective study investigating new technologies that could potentially have a high impact on antepartum fetal monitoring.Trial Registration: Registered on 26 March 2020 in the Dutch Trial Register (NL8769) via https://www.trialregister.nl/trials and registered on 21 October 2020 to the Central Committee on Research Involving Human Subjects (NL73607.015.20) via https://www.toetsingonline.nl/to/ccmo_search.nsf/Searchform?OpenForm . [ABSTRACT FROM AUTHOR]

Alvarez waves are local rhythmic contractions of the myometrium with high frequency and low intensity. They can be detected using internal or external tocography and electrohysterography. Some researchers correlate these small contractions with the initiation of labor, since they have been described as a pattern representing the uterine response to prostaglandin production. Other authors either do not validate a causality relation between Alvarez waves and labor or suggest that they have low predictive value for preterm labor. Alvarez waves' research has become a multidisciplinary subject with inputs ranging from medical science, biomedical engineering, and related areas. A comprehensive review is herein conducted to summarize the state of the art regarding Alvarez waves and their role in the initiation of labor, namely in preterm birth. The results show that a large number of studies have analyzed and characterized Alvarez waves without necessarily digging into their relationship with labor. Publications were categorized in three groups: (A) reports about morphology and characterization of Alvarez waves; (B) publications reporting a positive causality relation between Alvarez waves and labor; and (C) publications reporting an absence of causality regarding the previous hypothesis. Studies in group B outnumbered those in group C. A critical analysis is presented. [ABSTRACT FROM AUTHOR]

Premature birth and its associated complications are one of the biggest global health problems, since there is currently no effective screening method in clinical practice to accurately identify the true Preterm Birth (PTB) from the false threatened ones. Despite the high prevalence of PTB in multiple gestation (MG) women which amounted up to 60%, in the literature there is any work about their uterine myoelectric activities in vivo system. Electrohysterography (EHG) has been emerged as an alternative technique for predicting PTB in single gestation (SG) women. The aim of this study was to characterize and compare the uterine myoelectrical activity in vivo system of SG and MG women in regular check-ups, to provide the basis for early detection and prevention of preterm labour in MG. A prospective observational cohort study was conducted on 31 SG and 18 MG women between the 28th and 32th WoG who underwent regular check-ups in the Polytechnic and University Hospital La Fe (Valencia, Spain). The 30-minute bipolar recording was filtered in the 0.1-4 Hz bandwidth and downsampled to 20 Hz. Signal analysis was performed in 120-second moving windows with 50% overlap, after removing artefacts by a double- blind expert process. A set of 8 temporal, spectral and non-linear parameters were calculated: root mean square (RMS), kurtosis of the Hilbert envelope (KHE), median frequency (MDF), H/L ratio, and sample entropy (SampEn) and bubble entropy (BubbEn) calculated in the whole bandwidth (WBW) and the fast wave high (FWH). The 10th, 50th and 90th percentiles of all windows analysed were calculated to obtain representative values of the recordings. For each parameter and percentile, statistically significant differences between the SG and MG groups and their statistical power (SP) were analysed to determine both the existence of an effect and substantive significance, respectively. In comparison to SG, MG EHG exhibited significant higher impulsiveness and higher predictability than SG which was reflected in the KHE (SP 10 = 85.2, p 10 < 0.001) and entropy measures (SampEn FWH: SP 50 = 62.0, p 50 = 0.0.016; SP 90 = 52.5, p 90 = 0.059. BubbEn FWH: SP 50 = 75.2, p 50 < 0.001; SP 90 = 60.3, p 90 = 0.002), suggesting an accelerated evolution of uterine electrophysiological condition. In addition, several EHG parameters were found to significantly correlate with foetal weight such as amplitude (RMS: r 90 = 0.311, p 90 = 0.006), signal impulsiveness (KHE: r 10 = 0.311, p 10 = 0.006) and entropy measures (SampEn FWH: r 50 = −0.317, p 50 = 0.005*; r 90 = −0.279, p 90 = 0.013*. BubbEn FWH: r 50 = −0.370, p 50 = 0.001*; r 90 = −0.313, p 90 = 0.005*), suggesting an electromechanical coupling between uterine overdistension and contractile activity in vivo system. In comparison to SG women, MG showed higher impulsiveness and predictability in early third gestational trimester, as reflected in KHE, SampEn and BubbEn, respectively. We found similar cell excitability between SG and MG women far from delivery. In addition, we confirmed the relationship between uterine overdistension and surface myoelectric activity, revealing the electromechanical coupling pathway in uterine smooth muscle. Therefore, contextualized EHG-biomarkers would provide valuable information for early detection of PTB risk, which would allow clinicians better PTB management through personalised therapeutic interventions. • EHG features in multiple pregnancy differ from singleton ones in third trimester. • Multiple pregnancies show greater impulsivity and predictability than singletons. • Uterine overdistension may cause accelerated biophysical process toward labour. • Overdistension should be considered as a risk factor for preterm birth prediction. [ABSTRACT FROM AUTHOR]

Uterine contractions monitoring is essential during pregnancy progression for due date prediction and the screening of preterm deliveries, i.e., those related to labour contractions occurring before 37 weeks of gestation. As there are different kinds of uterine contraction, distinguishing between true labour ones and normal physiological ones during pregnancy is not trivial. Thus, early identification is necessary for the effective and efficient care of pregnant women to avoid unnecessary and costly hospitalization. In this regard, while classical clinical methods proved their limitations, real-time monitoring of uterine contractions is now possible thanks to a new technique called ElectroHysteroGraphy which measures the uterine electrical activity that is a proxy for the mechanical activity of the muscles of the uterus. An exhaustive and comprehensive literature review was conducted to retrieve and report the state-of-the-art of the methods to distinguish labour contractions from pregnancy contractions. A systematic search was run on different search engines using a search string. A snowball sampling approach was applied to the references of the retrieved articles to identify further appropriate papers. According to this, the relevant references included in the bibliography of each analysed article led to other appropriate articles. Thus, papers dealing with the methods to distinguish labour from pregnancy (normal physiological) contractions and published between 2001 and 2020 were selected. Linear and nonlinear methods have been developed for uterine contractions signals (EHG/EMG) analysis to distinguish labour from pregnancy contractions. Nonlinear methods yielded better results compared to the linear methods, but not all nonlinear methods are promising in terms of clinical application. [ABSTRACT FROM AUTHOR]

Background: In low-income countries, pregnant women do not have easy access to health care, especially in rural and peri-urban areas. In this context, they can be surprised by the uterine contractions that precede childbirth and sometimes find themselves giving birth at home or on the way to the nearest health facility (located miles away from their home). In view of the development of an external uterine electrohysterogram acquisition system for labour prediction, a review of the literature on electrodes and their characteristics is necessary. Methods: A comprehensive literature review was conducted to collate information on the use of electrodes in external EHG recording and their characteristics. Results: Wet electrodes based on Ag/AgCl redox chemistry are the most common type of electrodes for EHG, employed in different configurations on the pregnant woman's abdomen. All positioning configurations are around the vertical median axis if they are not placed directly on it. Positioning below the navel seems to be the most efficient. The number of source, reference, and ground electrodes used varies from one author to another, as does the distance between the electrodes. Conclusion: Two well-positioned source electrodes on the vertical median axis, with ground electrode on the right side of the hip and reference one on the left side, are able to generate a good external EHG recording signal. The minimum allowed inter-electrode distance is approximately 17.5 to 25mm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: To analyze and compare the bioelectric and mechanical activity of the uterus in pregnant women with threatening preterm delivery treated with tocolysis. Additionally, auxiliary parameters of the bioelectric signal, as registered by electrohysterography and characteristic only for this method, were measured and analyzed. Material and methods: Forty-five women with pregnancies from 24 to 36 weeks of gestation with typical clinical symptoms of threatening preterm delivery were given tocolytic therapy. Registration and analysis of bioelectric activity with electrohysterography was performed simultaneously with registration and analysis of mechanical activity with tocography. Results: After administration of tocolytic treatment, the presence of bioelectric activity was accompanied by the lack of or minimal occurrence of mechanical activity. All parameters of contraction recorded by electrohysterography had significantly greater values than those recorded by tocography. Conclusions: Measurement of bioelectric activity is more sensitive than measurement of mechanical activity of the uterus. Elevated bioelectric activity of the uterine muscle was observed despite the use of tocolysis, a lack of symptoms of threatening preterm delivery, as well as a lack of contraction in tocography. The presence of bioelectric activity may precede the occurrence of mechanical activity of the uterus, but further research is required on larger groups of patients. [ABSTRACT FROM AUTHOR]

Uterine contraction (UC) is an essential clinical indicator in the progress of labour and delivery. Electrohysterogram (EHG) signals recorded on the abdomen of pregnant women reflect the uterine electrical activity. This study proposes a novel algorithm for automatic recognition of UCs with EHG signals to improve the accuracy of detecting UCs. EHG signals by electrodes, the tension of the abdominal wall by tocodynamometry (TOCO) and maternal perception were recorded simultaneously in 54 pregnant women. The zero-crossing rate (ZCR) of the EHG signal and its power were calculated to modulate the raw EHG signal and highlight the EHG bursts. Then the envelope was extracted from the modulated EHG for UC recognition. Besides, UC was also detected by the conventional TOCO signal. Taking maternal perception as a reference, the UCs recognized by EHG and TOCO were evaluated with the sensitivity, positive predictive value (PPV), and UC parameters. The results show that the sensitivity and PPV are 87.8% and 93.18% for EHG, and 84.04% and 90.89% for TOCO. EHG detected a larger number of UCs than TOCO, which is closer to maternal perception. The duration and frequency of UC obtained from EHG and TOCO were not significantly different (p > 0.05). In conclusion, the proposed UC recognition algorithm has high accuracy and simple calculation which could be used for real-time analysis of EHG signals and long-term monitoring of UCs. [ABSTRACT FROM AUTHOR]

Accurate prediction of preterm birth is a global, public health priority. This necessitates the need for an efficient technique that aids in early diagnosis. The objective of this study is to develop an automated system for an effective detection of preterm (weeks of gestation < 37) condition using Electrohysterography (EHG) and topological features associated with the frequency components of signals. The EHG signals recorded prior to gestational age of 26 weeks are considered. The pre-processed signals are subjected to discrete Fourier transform to obtain the Fourier coefficients. The envelope is computed from the boundary of the complex Fourier coefficients identified using the α-shape method. Topological features namely, area, perimeter, circularity, convexity, ellipse variance and bending energy are extracted from the envelope. Classifications based on threshold-determination method and machine learning algorithms namely, naïve Bayes, decision tree and random forest are employed to differentiate the term and preterm conditions. The results show that the Fourier coefficients of EHG signals exhibit different shapes in the term and preterm conditions. The regularity of signals is found to increase in preterm condition. All the features are found to have significant differences between these two conditions. Bending energy as a single biomarker achieves a maximum accuracy of 80.7%. The random forest model based on the topological features detects the conditions with the maximum accuracy and positive predictive value of about 98.6%. Therefore, the proposed automated system seems to be effective and could be used for the accurate detection of term and preterm conditions. [ABSTRACT FROM AUTHOR]

Objective: The aim of this research was to assess the quality and inter- and intra-observer agreement of tracings obtained by three different techniques for uterine contraction monitoring: the external tocodynamometer (TOCO), the intrauterine pressure catheter (IUPC) and a recently introduced method based on electrohysterography (EHG).Study Design: We included 150 uterine activity registrations from a previous prospective observational study (W3 study), conducted at Máxima Medical Centre in Veldhoven, the Netherlands. Term singleton pregnant women were simultaneously monitored with TOCO, IUPC and EHG during labor. Six clinicians, blinded to the source (TOCO, IUPC, or EHG) and subject, evaluated all tracings that were subsequently presented in random order. They annotated contractions and assigned each tracing a score for interpretability of 2 (good), 1 (moderate) or 0 (poor). To evaluate inter-observer agreement, we calculated kappa values for the qualitative assessment, and intraclass correlation coefficients (ICC) for the number of contractions annotated by clinicians. Four clinicians repeated this procedure to evaluate intra-observer agreement.Results: IUPC tracings received the highest quality rating, with a mean score of 1.95, followed by a mean score of 1.60 for EHG and 0.80 for TOCO (p < 0.05). Mean weighted kappa values were 0.63 for TOCO and 0.45 for EHG. The average number of contractions that was picked up by clinicians was 59.8 for the intrauterine pressure catheter, 49.8 for EHG and 26.4 for TOCO. The ICC of the intrauterine pressure catheter was significantly higher than the external methods, regarding both inter- and intra-observer agreement (0.98 and 0.99 respectively).Conclusion: IUPC recordings scored best regarding quality, inter- and intra-observer agreement. However, due to safety issues, in many countries this technique is not used anymore. The quality of TOCO was rated as poor and many contractions were missed as compared to the gold standard. From a clinical interpretational point of view, EHG is favorable to TOCO. EHG recordings were assigned higher quality scores, but with less agreement between clinicians. An explanation could be that EHG is a relatively new technique, while IUPC and the TOCO are being used for decades. Building experience with EHG (training) is therefore recommended. [ABSTRACT FROM AUTHOR]

Electrohysterography (EHG) is a promising technique for pregnancy monitoring and preterm risk evaluation. It allows for uterine contraction monitoring as early as the 20th gestational week, and it is a non-invasive technique based on recording the electric signal of the uterine muscle activity from electrodes located in the abdominal surface. In this work, EHG-based contraction detection methodologies are applied using signal envelope features. Automatic contraction detection is an important step for the development of unsupervised pregnancy monitoring systems based on EHG. The exploratory methodologies include wavelet energy, Teager energy, root mean square (RMS), squared RMS, and Hilbert envelope. In this work, two main features were evaluated: contraction detection and its related delineation accuracy. The squared RMS produced the best contraction (97.15 ± 4.66%) and delineation (89.43 ± 8.10%) accuracy and the lowest false positive rate (0.63%). Despite the wavelet energy method having a contraction accuracy (92.28%) below the first-rated method, its standard deviation was the second best (6.66%). The average false positive rate ranged between 0.63% and 4.74%—a remarkably low value. [ABSTRACT FROM AUTHOR]

Preterm birth is the leading cause of perinatal morbidity and mortality. In clinical practice, the information of uterine contraction is an important reference for preterm delivery. The commonly used tocography method for detecting uterine contractions has low sensitivity and is not suitable for long-term measurement. The electrohysterography (EHG) can record the electrical activity of uterine muscle cells associated with contractions through electrodes on the abdomen. Therefore, this paper proposes a new automatic detection method of uterine contractions based on EHG signals. Specifically, utilizing the nonlinear property of entropy analysis, contraction and spike noise are distinguished in the complexity domain to highlight contraction activity. Then, the location of the contraction is detected by an adaptive thresholding method. Finally, the detected contractions are analyzed for pregnancy and labor. The results show that compared with the existing root mean square methods, the proposed method is less affected by spike noise, and the contraction detection rate on the Icelandic EHG database reaches 87.9%. Meanwhile, features extracted from detected contractions have significant differences between non-labor and labor categories. This demonstrates the feasibility of contraction detection by EHG signal, which can contribute to better care for high-risk pregnant women. • We propose an automatic uterine contraction detection algorithm based EHG. • The sample entropy with global tolerance is adopted to suppress noises within EHG. • The algorithm was validated on the public Icelandic EHG database and showed superiority. [ABSTRACT FROM AUTHOR]

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.

More than one in ten babies are born prematurely worldwide, resulting in nearly one million deaths each year. Furthermore, surviving babies face lifelong health-related disabilities, such as difficulties in learning or hearing and vision loss. Monitoring uterine contractions can evaluate the health and progress of the pregnancy. This monitoring can help in determining if the pregnant woman is in labour, thus assisting them to go to the hospital, which will help in reducing premature birth issues. In this paper, we aim to mitigate the consequences of premature birth for both the pregnant woman and the fetus by proposing a safe, simple, home-comfortable, low-cost, and reliable monitoring framework. The system uses a non-invasive method to monitor the uterine electrohysterography (EHG) contractions using a wireless body sensor (WBS) and a smartphone. The smartphone will analyze uterine EHG contractions readings, and if they contain a premature labour pattern, a warning notification will be triggered. A proof-of-concept prototype of the smartphone application was designed and tested for reliability, performance and power consumption using three uterine contractions databases. The results show that the application was able to meet the framework objectives in detecting the labour pattern.

Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.

Objective  To evaluate if fundal (F) dominance of the electrohysterogram is associated with vaginal delivery and lack of F dominance is associated with cesarean for labor dystocia. Study Design  We conducted a prospective cohort study of nulliparous women in spontaneous labor at ≥36 weeks. Clinicians were blinded to electrohysterography data which were in addition to standard cardiotocography. All contractions in the hour preceding diagnosis of complete cervical dilation (for women delivering vaginally) or the hour preceding the decision for cesarean were analyzed. Results  Of 224 patients, 167 had evaluable data. The proportion of F dominant contractions was not different for women undergoing cesarean for labor dystocia (n  = 11) compared with all others (n  = 156)—88.7 ± 10.2 versus 86.0 ± 11.4%; p  = 0.44. Results were similar when comparing the cesarean for labor dystocia group to those undergoing cesarean for other indications (n  = 10) and vaginal deliveries (n  = 146)—88.7 ± 10.2 versus 86.5 ± 10.0 versus 85.9 ± 11.5%; p  = 0.74. Conclusion  We were unable to confirm our earlier finding that F dominance of the electrohysterogram is associated with vaginal delivery and lack of F dominance is associated with cesarean for dystocia. [ABSTRACT FROM AUTHOR]

In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age > 37 weeks) and preterm (gestational age ≤ 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant ( p < 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women. [ABSTRACT FROM AUTHOR]

Objective: It has been shown that noninvasive uterine electromyography (EMG) can identify true preterm labor more accurately than methods available to clinicians today. The objective of this study was to evaluate the effect of body mass index (BMI) on the accuracy of uterine EMG in predicting preterm delivery. Materials and Methods: Predictive values of uterine EMG for preterm delivery were compared in obese versus overweight/normal BMI patients. Hanley–McNeil test was used to compare receiver operator characteristics curves in these groups. Previously reported EMG cutoffs were used to determine groups with false positive/false negative and true positive/true negative EMG results. BMI in these groups was compared with Student t test (p

Electrohysterography (EHG) is a promising technique for pregnancy monitoring and preterm risk evaluation. It allows for uterine contraction monitoring as early as the 20th gestational week, and it is a non-invasive technique based on recording the electric signal of the uterine muscle activity from electrodes located in the abdominal surface. In this work, EHG-based contraction detection methodologies are applied using signal envelope features. Automatic contraction detection is an important step for the development of unsupervised pregnancy monitoring systems based on EHG. The exploratory methodologies include wavelet energy, Teager energy, root mean square (RMS), squared RMS, and Hilbert envelope. In this work, two main features were evaluated: contraction detection and its related delineation accuracy. The squared RMS produced the best contraction (97.15 ± 4.66%) and delineation (89.43 ± 8.10%) accuracy and the lowest false positive rate (0.63%). Despite the wavelet energy method having a contraction accuracy (92.28%) below the first-rated method, its standard deviation was the second best (6.66%). The average false positive rate ranged between 0.63% and 4.74%—a remarkably low value.