Noninvasive uterine electromyography for prediction of preterm delivery

An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery

The existing non-invasive automated preterm birth prediction methods rely on the use of uterine electrohysterogram (EHG) records coming from spontaneous preterm and term deliveries, and are indifferent to term induced and cesarean section deliveries. In order to enhance current publicly available pool of term EHG records, we developed a new EHG dataset, Induced Cesarean EHG DataSet (ICEHG DS), containing 126 30-minute EHG records, recorded early (23rd week), and/or later (31st week) during pregnancy, of those pregnancies that were expected to end in spontaneous term delivery, but ended in induced or cesarean section delivery. The records were collected at the University Medical Center Ljubljana, Ljubljana, Slovenia. The dataset includes 38 and 43, early and later, induced; 11 and 8, early and later, cesarean; and 13 and 13, early and later, induced and cesarean EHG records. This dataset enables better understanding of the underlying physiological mechanisms involved during pregnancies ending in induced and cesarean deliveries, and provides a robust and more realistic assessment of the performance of automated preterm birth prediction methods.

A Portable and a Scalable Multi-Channel Wireless Recording System for Wearable Electromyometrial Imaging

Electromyometrial imaging (EMMI) technology has emerged as one of the promising technology that can be used for non-invasive pregnancy risk stratification and for preventing complications due to pre-term birth. Current EMMI systems are bulky and require a tethered connection to desktop instrumentation, as a result, the system cannot be used in non-clinical and ambulatory settings. In this article, we propose an approach for designing a scalable, portable wireless EMMI recording system that can be used for in-home and remote monitoring. The wearable system uses a non-equilibrium differential electrode multiplexing approach to enhance signal acquisition bandwidth and to reduce the artifacts due to electrode drifts, amplifier 1/f noise, and bio-potential amplifier saturation. A combination of active shielding, a passive filter network, and a high-end instrumentation amplifier ensures sufficient input dynamic range ([Formula: see text]) such that the system can simultaneously acquire different bio-potential signals like maternal electrocardiogram (ECG) in addition to the EMMI electromyogram (EMG) signals. We show that the switching artifacts and the channel cross-talk introduced due to non-equilibrium sampling can be reduced using a compensation technique. This enables the system to be potentially scaled to a large number of channels without significantly increasing the system power dissipation. We demonstrate the feasibility of the proposed approach in a clinical setting using an 8-channel battery-powered prototype which dissipates less than 8 μW per channel for a signal bandwidth of 1 KHz.

Assessing uterine electrophysiology prior to elective term induction of labor

OBJECTIVE

The purpose of this study was to determine if uterine electrophysiological signals gathered from 151 non-invasive biomagnetic sensors spread over the abdomen were associated with successful induction of labor (IOL).

STUDY DESIGN

Uterine magnetomyogram (MMG) signals were collected using the SARA (SQUID Array for Reproductive Assessment) device from 33 subjects between 37 and 42 weeks gestational age. The signals were post-processed, uterine contractile related MMG bursts were detected, and parameters in the time and frequency domain were extracted. The modified Bishop score calculated at admission was used to determine the method of IOL. Wilcoxon's rank-sum test was used to compare IOL successes and failures for differences in gestational age (GA), parity, modified Bishop's score, maximum oxytocin, and electrophysiological parameters extracted from MMG.

RESULTS

The average parity was three times (3x) higher (1.53 versus 0.50; p = 0.039), and the average modified Bishop score was 2x higher (3.32 versus 1.63; p = 0.032) amongst IOL successes than failures, while the average GA and maximum oxytocin showed a small difference. For the MMG parameters, successful IOLs had, on average, 3.5x greater mean power during bursts (0.246 versus 0.070; p = 0.034) and approximately 1.2x greater mean number of bursts (2.05 versus 1.68; p = 0.036) compared to the failed IOLs, but non-significant differences were observed in mean peak frequency, mean burst duration, and mean duration between bursts.

CONCLUSION

The study showed that inductions of labor that took less than 24 h to deliver have a higher mean power in the baseline electrophysiological activity of the uterus when recorded prior to planned induction. The results are indicative that baseline electrophysiological activity measured prior to induction is associated with successful induction.

Noninvasive electromyometrial imaging of human uterine maturation during term labor

Electromyometrial imaging (EMMI) was recently developed to image the three-dimensional (3D) uterine electrical activation during contractions noninvasively and accurately in sheep. Herein we describe the development and application of a human EMMI system to image and evaluate 3D uterine electrical activation patterns at high spatial and temporal resolution during human term labor. We demonstrate the successful integration of the human EMMI system during subjects' clinical visits to generate noninvasively the uterine surface electrical potential maps, electrograms, and activation sequence through an inverse solution using up to 192 electrodes distributed around the abdomen surface. Quantitative indices, including the uterine activation curve, are developed and defined to characterize uterine surface contraction patterns. We thus show that the human EMMI system can provide detailed 3D images and quantification of uterine contractions as well as novel insights into the role of human uterine maturation during labor progression.

End-to-end learning with interpretation on electrohysterography data to predict preterm birth

Prediction of preterm birth is a difficult task for clinicians. By examining an electrohysterogram, electrical activity of the uterus that can lead to preterm birth can be detected. Since signals associated with uterine activity are difficult to interpret for clinicians without a background in signal processing, machine learning may be a viable solution. We are the first to employ Deep Learning models, a long-short term memory and temporal convolutional network model, on electrohysterography data using the Term-Preterm Electrohysterogram database. We show that end-to-end learning achieves an AUC score of 0.58, which is comparable to machine learning models that use handcrafted features. Moreover, we evaluate the effect of adding clinical data to the model and conclude that adding the available clinical data to electrohysterography data does not result in a gain in performance. Also, we propose an interpretability framework for time series classification that is well-suited to use in case of limited data, as opposed to existing methods that require large amounts of data. Clinicians with extensive work experience as gynaecologist used our framework to provide insights on how to link our results to clinical practice and stress that in order to decrease the number of false positives, a dataset with patients at high risk of preterm birth should be collected. All code is made publicly available.

Monitoring uterine contractions during labor: current challenges and future directions

Organ-level models are used to describe how cellular and tissue-level contractions coalesce into clinically observable uterine contractions. More importantly, these models provide a framework for evaluating the many different contraction patterns observed in laboring patients, ideally offering insight into the pitfalls of currently available recording modalities and suggesting new directions for improving recording and interpretation of uterine contractions. Early models proposed wave-like propagation of bioelectrical activity as the sole mechanism for recruiting the myometrium to participate in the contraction and increase contraction strength. However, as these models were tested, the results consistently revealed that sequentially propagating waves do not travel long distances and do not encompass the gravid uterus. To resolve this discrepancy, a model using 2 mechanisms, or a "dual model," for organ-level signaling has been proposed. In the dual model, the myometrium is recruited by action potentials that propagate wave-like as far as 10 cm. At longer distances, the myometrium is recruited by a mechanotransduction mechanism that is triggered by rising intrauterine pressure. In this review, we present the influential models of uterine function, highlighting their main features and inconsistencies, and detail the role of intrauterine pressure in signaling and cervical dilation. Clinical correlations demonstrate the application of organ-level models. The potential to improve the recording and clinical interpretation of uterine contractions when evaluating labor is discussed, with emphasis on uterine electromyography. Finally, 7 questions are posed to help guide future investigations on organ-level signaling mechanisms.

Quantitative analysis for grading uterine electromyography activities during labor

BACKGROUND

The strength of uterine contraction is one of the decisive factors for labor progression and parturition. Clinicians usually encounter difficulties in early identification of inadequate contractions and in oxytocin treatment. Electromyography-an emerging technology for uterine contraction monitoring-can quantify the intensity of myoelectric activity of uterine contraction. Therefore, grading patients with different uterine contraction intensities by electromyography is of great significance to the clinical intensive management of uterine contraction and labor process.

OBJECTIVE

This study aimed to quantify and grade electromyography activity during the latent phase of the first stage of labor and explore its relationship with oxytocin treatment and length of labor.

STUDY DESIGN

We performed a retrospective cohort study to identify electromyography parameters as a predictor for oxytocin treatment and length of labor among a cohort of term singleton primipara (n=508) during the latent phase who delivered in Guangzhou between August 2018 and December 2021. The electromyography parameters were graded according to the quartile method, and the significance of grading and delivery outcome was explored. Univariate and multivariate logistic regression were used to determine the predictors of oxytocin treatment.

RESULTS

Maternal gestational age (adjusted risk ratio, 1.2; 95% confidence interval, 1.0-1.5), root mean square (adjusted risk ratio, 0.01; 95% confidence interval, 0.004-0.03), and power (adjusted risk ratio, 0.02; 95% confidence interval, 0.01-0.05) were significant predictors of oxytocin argumentation. The low electromyography activity group had a longer first stage labor and total labor time and were more likely to use oxytocin.

CONCLUSION

Electromyography parameters root mean square and power had high predictive values for later oxytocin treatment among patients with spontaneous labor. Patients with low-grade electromyography were more likely need oxytocin treatment. Electromyography grading is very important for its clinical promotion and use, and it could lead to more reliable analyses of oxytocin treatments and eventually to more effective interventions to prevent prolonged labor.

Characterization and separation of preterm and term spontaneous, induced, and cesarean EHG records

To improve the understanding of the underlying physiological processes that lead to preterm birth, and different term delivery modes, we quantitatively characterized and assessed the separability of the sets of early (23rd week) and later (31st week) recorded, preterm and term spontaneous, induced, cesarean, and induced-cesarean electrohysterogram (EHG) records using several of the most widely used non-linear features extracted from the EHG signals. Linearly modeled temporal trends of the means of the median frequencies (MFs), and of the means of the peak amplitudes (PAs) of the normalized power spectra of the EHG signals, along pregnancy (from early to later recorded records), derived from a variety of frequency bands, revealed that for the preterm group of records, in comparison to all other term delivery groups, the frequency spectrum of the frequency band B0L (0.08-0.3 Hz) shifts toward higher frequencies, and that the spectrum of the newly identified frequency band B0L' (0.125-0.575 Hz), which approximately matches the Fast Wave Low band, becomes stronger. The most promising features to separate between the later preterm group and all other later term delivery groups appear to be MF (p=1.1⋅10^-5) in the band B0L of the horizontal signal S3, and PA (p=2.4⋅10^-8) in the band B0L' (S3). Moreover, the PA in the band B0L' (S3) showed the highest power to individually separate between the later preterm group and any other later term delivery group. Furthermore, the results suggest that in preterm pregnancies the resting maternal heart rate decreases between the 23rd and 31st week of gestation.

Comparison of Oxytocin vs. Carbetocin Uterotonic Activity after Caesarean Delivery Assessed by Electrohysterography: A Randomised Trial

Electrohysterography has been used for monitoring uterine contractility in pregnancy and labour. Effective uterine contractility is crucial for preventing postpartum haemorrhage. The objective of our study was to compare postpartum electrohysterograms in women receiving oxytocin vs. carbetocin for postpartum haemorrhage prevention after caesarean delivery. The trial is registered at ClinicalTrials.gov with the identifier NCT04201665. We included 64 healthy women with uncomplicated singleton pregnancies at term scheduled for caesarean section after one previous caesarean section. After surgery, a 15 min electrohysterogram was obtained after which women were randomised to receive either five IU of oxytocin intravenously or 100 μg of carbetocin intramuscularly. A 30 min electrohysterogram was performed two hours after drug application. Changes in power density spectrum peak frequency of electrohysterogram pseudo-bursts were analysed. A significant reduction in power density spectrum peak frequency in the first two hours was observed after carbetocin but not after oxytocin (median = 0.07 (interquartile range (IQR): 0.87 Hz) compared to median = -0.63 (IQR: 0.20) Hz; p = 0.004). Electrohysterography can be used for objective comparison of uterotonic effects. We found significantly higher power density spectrum peak frequency two hours after oxytocin compared to carbetocin.

Regional identification of information flow termination of electrohysterographic signals: Towards understanding human uterine electrical propagation

BACKGROUND AND OBJECTIVE

The uterine electrohysterogram (EHG) contains important information about electrical signal propagation which may be useful to monitor and predict the progress of pregnancy towards parturition. Directed information processing has the potential to be of use in studying EHG recordings. However, so far, there is no directed information-based estimation scheme that has been applied to investigating the propagation of human EHG recordings. To realize this, the approach of directed information and its reliability and adaptability should be scientifically studied.

METHODS

We demonstrated an estimation scheme of directed information to identify the spatiotemporal relationship between the recording channels of EHG signal and assess the algorithm reliability initially using simulated data. Further, a regional identification of information flow termination (RIIFT) approach was developed and applied for the first time to extant multichannel EHG signals to reveal the terminal zone of propagation of the electrical activity associated with uterine contraction. RIIFT operates by estimating the pairwise directed information between neighboring EHG channels and identifying the location where there is the strongest inward flow of information. The method was then applied to publicly-available experimental data obtained from pregnant women with the use of electrodes arranged in a 4-by-4 grid.

RESULTS

Our results are consistent with the suggestions from the previous studies with the added identification of preferential sites of excitation termination - within the estimated area, the direction of surface action potential propagation towards the medial axis of uterus during contraction was discovered for 72.15% of the total cases, demonstrating that our RIIFT method is a potential tool to investigate EHG propagation for advancing our understanding human uterine excitability.

CONCLUSIONS

We developed a new approach and applied it to multichannel human EHG recordings to investigate the electrical signal propagation involved in uterine contraction. This provides an important platform for future studies to fill knowledge gaps in the spatiotemporal patterns of electrical excitation of the human uterus.

Mid-trimester uterine electromyography in patients with a short cervix

BACKGROUND

Preterm birth is the largest single cause of infant death in the United States. A cervical length of <2.5 cm, measured in the mid-trimester, has been shown to identify individuals at increased risk. Uterine electromyography is an emerging technology for noninvasively assessing uterine bioelectrical activity. With its ability to characterize nuanced differences in myometrial signals, uterine electromyography assessments during the mid-trimester may provide insight into the mechanisms of cervical shortening.

OBJECTIVE

This study aimed to characterize uterine bioelectrical activity in pregnant individuals with short cervices in the mid-trimester compared with that of pregnant individuals of the same gestational age with normal cervical lengths.

STUDY DESIGN

This is a prospective cohort study of subjects with singleton, nonanomalous pregnancies between 16 weeks and 0 days and 22 weeks and 6 days of gestational age. Subjects with normal cervical length (≥3.0 cm) were compared with subjects with short cervical length (<2.5 cm). The short-cervical-length cohort was further stratified by history of preterm birth. Multichannel uterine electromyography recordings were obtained for ∼60 minutes using proprietary, directional electromyography sensors on the abdomen. Uterine electromyography signals were observed and classified in groups as spikes, short bursts, and bursts. Primary outcomes were relative expression of spike, short-burst, and burst uterine electromyography signals. Subgroup analyses assessed each signal percentage by cervical length, history of preterm birth, and gestational age at delivery. Differences in percentage of uterine electromyography signals according to cervical length were analyzed using nonparametric tests of significance.

RESULTS

Of the 28 included subjects, 10 had normal and 18 had short cervical length. There were 9 subjects with short cervical length and a history of preterm birth. Spikes were the most commonly recorded signals and were higher in the normal-cervical-length cohort (96.3% [interquartile range, 93.1%-100.0%]) than the short-cervical-length cohort (75.2% [interquartile range, 66.7%-92.0%], P=.001). In contrast, median percentages of short-bursts and bursts were significantly higher in subjects with a short cervical length (17.3% [interquartile range, 13.6%-23.9%] vs 2.5% for normal cervical length [interquartile range, 0%-5.5%], P=.001 and 6.6% [interquartile range, 0%-13.4%] vs 0% for normal cervical length [interquartile range, 0%-2.8%], P=.014, respectively). Within subgroup analyses, cervical length was inversely proportional to percentage of observed short-bursts (P=.013) and bursts (P=.014). Subjects with short cervical length and history of preterm birth had higher burst percentages (12.8% [interquartile range, 9.0%-15.7%]) than those with short cervical length and no history of preterm birth (3.3% [interquartile range, 0%-5.0%], P=.003).

CONCLUSION

Short-burst and burst uterine electromyography signals are observed more frequently in mid-trimester patients with short cervical lengths. This relationship provides insight into abnormal myometrial activation in the mid-trimester and offers a plausible biophysiological link to cervical shortening.

Association between Increased Uterine Activity, as Recorded Noninvasively from the Anterior Abdominal Wall at 34 Weeks' Gestation, and Preterm Birth

Background

There is a need to accurately identify pregnant women at risk for preterm birth as early as possible. Recent developments in technology enable the recording of uterine electrical activity (electrohysterogram) from the anterior abdominal wall in a non-invasive way.

Objective

To investigate whether uterine activity recorded under resting conditions at a gestational age of 34 weeks could identify a risk of preterm birth.

Study design

A commercial antenatal holter device with its dedicated software was used to record and store raw data of the maternal and fetal electrocardiograms and uterine activity for the Safe Passage Study. Uterine activity was recorded under resting conditions from 34 weeks' gestation in epochs of 250 ms (millisecond) for at least 30 min. From this database the raw data, recorded at a mean gestational age of 34 weeks, of 50 women who had preterm deliveries were selected for comparison with data of women who had term deliveries. Mean uterine activity, expressed in microvolt (μV)/epoch, was used for the comparison.

Results

After exclusion of 25 participants where labour was induced or augmented and another three for other reasons, 36 remained in each group. The participants in each group were comparable in respect of maternal age, gravidity, parity, gestational age at recruitment and duration of recording. Uterine activity in the preterm group (60.3 μV/epoch) differed significantly (p<0.01) from that of the comparison group (52.4 μV/epoch). Using a cut-off point of 52.3 μV/epoch as obtained from receiver operator characteristic curves (area under the curve 0.72), the sensitivity and specificity of identifying risks of preterm labour were 81% and 50% respectively.

Conclusion

Results of this small study are promising but need to be confirmed in larger studies and preferably at earlier gestational age.

Enhancing classification of preterm-term birth using continuous wavelet transform and entropy-based methods of electrohysterogram signals

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 (channel-filter) 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.

Non-invasive fetal electrocardiography, electrohysterography and speckle-tracking echocardiography in the second trimester: study protocol of a longitudinal prospective cohort study (BEATS-study)

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.

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

Noninvasive monitoring of uterine electrical activity among patients with obesity: a new external monitoring device

BACKGROUND

Tocodynamometry is a common, noninvasive tool used to measure contraction frequency; however, its utility is often limited in patients with obesity. An intrauterine pressure catheter provides a more accurate measurement of uterine contractions but requires ruptured membranes, limiting its utility during early latent labor. Electrical uterine myography has shown promise as a noninvasive contraction monitor with efficacy similar to that of the intrauterine pressure catheter; however, its efficacy has not been widely studied in the obese population.

OBJECTIVE

This study aimed to validate the accuracy of electrical uterine myography by comparing it with tocodynamometry and intrauterine pressure catheters among laboring patients with obesity.

STUDY DESIGN

This was a prospective observational study from February 2017 to April 2018 of patients with obesity, aged 18 years or older, who were admitted to the labor unit with viable singleton pregnancies and no contraindications for electromyography. Patients were monitored simultaneously with electrical myography and tocodynamometry or intrauterine catheter for more than 30 minutes. Two blinded obstetricians reviewed the tracings. The outcomes of interest were continuous and interpretable tracing, number of contractions, and timing and duration of contractions, interpreted as point estimates and associated 95% confidence intervals.

RESULTS

A total of 110 patients were enrolled (65 tocodynamometry, 55 intrauterine catheter). Electrical myography was significantly more interpretable during a 30-minute tracing (P=.001) and detected 39% more contractions than tocodynamometry (P<.0001; 95% confidence interval, 23%-57%), whereas there was no difference in the interpretability of tracings or number of contractions between electrical myography and an intrauterine catheter (P=.16; 95% confidence interval, -0.19 to 1.19). Patients who underwent simultaneous monitoring preferred the electrical myography device over tocodynamometry.

CONCLUSION

Electrical uterine myography is superior to tocodynamometry in the detection of intrapartum uterine contraction monitoring and comparable with internal contraction monitoring.

Performance comparison of coupling-evaluation methods in discriminating between pregnancy and labor EHG signals

BACKGROUND

Recent years have seen an increased interest in electrohysterogram (EHG) signals as a means to evaluate the synchronization of uterine contractions. Several studies have pointed out that the quality of signal processing - and hence the interpretation of measurement results - is affected significantly by the choice of measurement technique and the presence of non-stationary frequency content in EHG signals. To our knowledge, the effect of time variance on the quality of EHG signal processing has never been fully investigated. How best to process EHG signals with the goal of distinguishing labor-induced contractions from their harmless, pre-labor cousins, remains an open question.

METHOD

Our methodology is based on three pillars. The first consists of a new method for EHG preprocessing in which we apply a second-order Butterworth filter to retain only the EHG fast-wave, low-frequency band (FWL), then use a bivariate piecewise stationary pre-segmentation (bPSP) algorithm to segment the EHG signal into stationary parts. The second pillar addresses the estimation of connectivity and directionality using three methods: nonlinear correlation coefficient (h²), general synchronization (H), and Granger causality (GC). The third pillar is related to signal classification and discrimination between pregnancy and labor using receiver operating curves (ROC) and connectivity and direction maps. For this purpose, we analyze the impact of four factors on data processing efficiency: i) method of connectivity detection, ii) effect of piecewise stationary segmentation preprocessing, iii) retained frequency content and iv) electrode configuration used for EHG recording (bipolar vs. unipolar).

RESULTS

Our results show that piecewise signal segmentation and filtering considerably improves classification performance and statistical significance for some connectivity methods, in particular the h². To this end we propose a new approach (detailed below) for h² called Filtered-Windowed (FW) h² that better highlights the differences between pregnancy and labor in the connectivity matrix and directionality maps.

CONCLUSIONS

This is the first comparative study of the effects of multiple processing factors on connectivity measurement efficiency. Our results indicate that appropriate preprocessing can improve the differentiation of pregnancy and labor-induced contraction signals and may lead to innovative applications in the prevention of preterm labor.

Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records

The aim of the present study was to assess the capability of conduction velocity amplitudes and directions of propagation of electrohysterogram (EHG) waves to better distinguish between preterm and term EHG surface records. Using short-time cross-correlation between pairs of bipolar EHG signals (upper and lower, left and right), the conduction velocities and their directions were estimated using preterm and term EHG records of the publicly available Term–Preterm EHG DataSet with Tocogram (TPEHGT DS) and for different frequency bands below and above 1.0 Hz, where contractions and the influence of the maternal heart rate on the uterus, respectively, are expected. No significant or preferred continuous direction of propagation was found in any of the non-contraction (dummy) or contraction intervals; however, on average, a significantly lower percentage of velocity vectors was found in the vertical direction, and significantly higher in the horizontal direction, for preterm dummy intervals above 1.0 Hz. The newly defined features—the percentages of velocities in the vertical and horizontal directions, in combination with the sample entropy of the EHG signal recorded in the vertical direction, obtained from dummy intervals above 1.0 Hz—showed the highest classification accuracy of 86.8% (AUC=90.3%) in distinguishing between preterm and term EHG records of the TPEHGT DS.

Review and Study of Uterine Bioelectrical Waveforms and Vector Analysis to Identify Electrical and Mechanosensitive Transduction Control Mechanisms During Labor in Pregnant Patients

The bioelectrical signals that produce uterine contractions during parturition are not completely understood. The objectives are as follows: (1) to review the literature and information concerning uterine biopotential waveforms generated by the uterus, known to produce contractions, and evaluate mechanotransduction in pregnant patients using electromyographic (EMG) recording methods and (2) to study a new approach, uterine vector analysis, commonly used for the heart: vectorcardiography analysis. The patients used in this study were as follows: (1) patients at term not in labor (n = 3); (2) patients during the 1st stage of labor at cervical dilations from 2 to 10 cm (n = 30); and (3) patients in the 2nd stage of labor and during delivery (n = 3). We used DC-coupled electrodes and PowerLab hardware (model no. PL2604, ADInstruments, Castle Hill, Australia), with software (LabChart, ADInstruments) for storage and analysis of biopotentials. Uterine and abdominal EMG recordings were made from the surface of each patient using 3 electrode pairs with 1 pair (+ and -, with a 31-cm spacing distance) placed in the right/left position (X position) and with 1 pair placed in an up/down position (Y position, also 31 cm apart) and with the third pair at the front/back (Z position). Using signals from the three X, Y, and Z electrodes, slow (0.03 to 0.1 Hz, high amplitude) and fast wave (0.3 to 1 Hz, low amplitude) biopotentials were recorded. The amplitudes of the slow waves and fast waves were significantly higher during the 2nd stage of labor compared to the 1st stage (respectively, p = 9.54 × e^-3 and p = 3.94 × e^-7). When 2 channels were used, for example, the X vs. Y, for 2-D vector analysis or 3 channels, X vs. Y vs. Z, for 3-D analysis, are plotted against each other on their axes, this produces a vector electromyometriogram (EMMG) that shows no directionality for fast waves and a downward direction for slow waves. Similarly, during the 2nd stage of labor during abdominal contractions ("pushing"), the slow and fast waves were enlarged. Manual applied pressure was used to evoke bioelectrical activity to examine the mechanosensitivity of the uterus. Conclusions: (1) Phasic contractility of the uterus is a product of slow waves and groups of fast waves (bursts of spikes) to produce myometrial contractile responses. (2) 2-D and 3-D uterine vector analyses (uterine vector electromyometriogram) demonstrate no directionality of small fast waves while the larger slow waves represent the downward direction of biopotentials towards the cervical opening. (3) Myometrial cell action event excitability and subsequent contractility likely amplify slow wave activity input and uterine muscle contractility via mechanotransduction systems. (4) Models illustrate the possible relationships of slow to fast waves and the association of a mechanotransduction system and pacemaker activity as observed for slow waves and pacemakers in gastrointestinal muscle. (5) The interaction of these systems is thought to regulate uterine contractility. (6) This study suggests a potential indicator of delivery time. Such vector approaches might help us predict the progress of gestation and better estimate the timing of delivery, gestational pathologies reflected in bioelectric events, and perhaps the potential for premature delivery drug and mechanical interventions.

Analysis of Electrohysterographic Signal Propagation Direction during Uterine Contraction: the Application of Directed Information

The potential of using the information of uterine contractions (UCs) derived from electrohysterogram (EHG) has been recognized in early detection of preterm delivery. A better understanding of the conduction property of EHG is clinically useful for developing advanced methods to achieve a reliable prediction of preterm delivery. In this paper, a method to analyze the destination of EHG propagation has been proposed via the estimation of directed information (DI) between each pair of neighboring channels with a novel propagation terminal zone (PTZ) identification algorithm. The proposed method was applied to experimental data from the Icelandic 16-electrode EHG database. The results demonstrated that for more than 81.8% participants, the PTZ was identified along the medial axis of uterus, among which more than half have their PTZ determined in the center between the uterine fundus and public symphysis, which indicated a great probability of propagation of EHG signals towards the center of uterus plane.Clinical relevance- This study makes a fundamental contribution for predicting preterm delivery, which can provide improvement in obstetric care towards pregnancy monitoring.

Propagation of spontaneous electrical activity in the ex vivo human uterus

Contractions of the non-pregnant uterus play a key role in fertility. Yet, the electrophysiology underlying these contractions is poorly understood. In this paper, we investigate the presence of uterine electrical activity and characterize its propagation in unstimulated ex vivo human uteri. Multichannel electrohysterographic measurements were performed in five freshly resected human uteri starting immediately after hysterectomy. Using an electrode grid externally and an electrode array internally, measurements were performed up to 24 h after hysterectomy and compared with control. Up to 2 h after hysterectomy, we measured biopotentials in all included uteri. The median root mean squared (RMS) values of the external measurements ranged between 3.95 μV (interquartile range (IQR) 2.41–14.18 μV) and 39.4 μV (interquartile range (IQR) 10.84–105.64 μV) and were all significantly higher than control (median RMS of 1.69 μV, IQR 1.13–3.11 μV), consisting of chicken breast meat. The RMS values decreased significantly over time. After 24 h, the median RMS (1.27 μV, IQR 0.86–3.04 μV) was comparable with the control (1.69 μV, IQR 1.13–3.11 μV, p = 0.125). The internal measurements showed a comparable pattern over time, but overall lower amplitude. The measured biopotentials propagated over the uterine surface, following both a plane-wave as well as an erratic pattern. No clear pacemaker location nor a preferred propagation direction could be identified. These results show that ex vivo uteri can spontaneously generate propagating biopotentials and provide novel insight contributing to improving our understanding of the electrophysiology of the human non-pregnant uterus.

Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios

Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel–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.

Deep neural network for semi-automatic classification of term and preterm uterine recordings

Pregnancy is a complex process, and the prediction of premature birth is uncertain. Many researchers are exploring non-invasive approaches to enhance its predictability. Currently, the ElectroHysteroGram (EHG) and Tocography (TOCO) signal are a real-time and non-invasive technology which can be employed to predict preterm birth. For this purpose, sparse autoencoder (SAE) based deep neural network (SAE-based DNN) is developed. The deep neural network has three layers including a stacked sparse autoencoder (SSAE) network with two hidden layers and one final softmax layer. To this end, the bursts of all 26 recordings of the publicly available TPEHGT DS database corresponding to uterine contraction intervals and non-contraction intervals (dummy intervals) were manually segmented. 20 features were extracted by two feature extraction algorithms including sample entropy and wavelet entropy. Afterwards, the SSAE network is adopted to learn high-level features from raw features by unsupervised learning. The softmax layer is added at the top of the SSAE network for classification. In order to verify the effectiveness of the proposed method, this study used 10-fold cross-validation and four indicators to evaluate classification performance. Experimental research results display that the performance of deep neural network can achieve Sensitivity of 98.2%, Specificity of 97.74%, and Accuracy of 97.9% in the publicly TPEHGT DS database. The performance of deep neural network outperforms the comparison models including deep belief networks (DBN) and hierarchical extreme learning machine (H-ELM). Finally, experimental research results reveal that the proposed method could be valid applied to semi-automatic identification of term and preterm uterine recordings.

Spatial-dependent regularization to solve the inverse problem in electromyometrial imaging

Recently, electromyometrial imaging (EMMI) was developed to non-invasively image uterine contractions in three dimensions. EMMI collects body surface electromyography (EMG) measurements and uses patient-specific body-uterus geometry generated from magnetic resonance images to reconstruct uterine electrical activity. Currently, EMMI uses the zero-order Tikhonov method with mean composite residual and smoothing operator (CRESO) to stabilize the underlying ill-posed inverse computation. However, this method is empirical and implements a global regularization parameter over all uterine sites, which is sub-optimal for EMMI given the severe eccentricity of body-uterus geometry. To address this limitation, we developed a spatial-dependent (SP) regularization method that considers both body-uterus eccentricity and EMG noise. We used electrical signals simulated with spherical and realistic geometry models to compare the reconstruction accuracy of the SP method to those of the CRESO and the L-Curve methods. The SP method reconstructed electrograms and potential maps more accurately than the other methods, especially in cases of high eccentricity and noise contamination. Thus, the SP method should facilitate clinical use of EMMI and can be used to improve the accuracy of other electrical imaging modalities, such as Electrocardiographic Imaging. Graphical abstract The spatial-dependent regularization (SP) technique was designed to improve the accuracy of Electromyometrial Imaging (EMMI). The top panel shows the eccentricity of body-uterus geometry and four representative body surface electrograms. The bottom panel shows boxplots of correlation coefficients and relative errors for the electrograms reconstructed with SP and two conventional methods, the L-Curve and mean CRESO methods.

A New Efficient Algorithm for Prediction of Preterm Labor

Electrohysterogram (EHG) signal represents electrical activity of uterine collected from abdominal surface of pregnant women. It has been proven that EHG analysis could be a suitable way to predict preterm labor and consequently to prevent it. The aim of this paper is to present an efficient low computational complexity algorithm to detect preterm labor using EHG signals. To this purpose, Empirical Mode Decomposition (EMD) has been applied for features extraction. Root mean square (RMS) of first two intrinsic mode function (IMF) of decomposed EHG signals were used as features and classification was performed by Support Vector Machine (SVM). In this experiment, the database consisted of 262 term delivery subjects (duration of pregnancy ≥37 weeks) and 38 preterm delivery subjects (duration of pregnancy <; 37 weeks). Each record contained 3 channels, therefore, various configurations of features and channels were applied. Among those configurations, classification based on RMS of the second IMF from channel one achieved best results with accuracy= 99.56%, sensitivity= 98.95% and specificity= 99.30%.

Noninvasive high-resolution electromyometrial imaging of uterine contractions in a translational sheep model

In current clinical practice, uterine contractions are monitored via a tocodynamometer or an intrauterine pressure catheter, both of which provide crude information about contractions. Although electrohysterography/electromyography can measure uterine electrical activity, this method lacks spatial specificity and thus cannot accurately measure the exact location of electrical initiation and location-specific propagation patterns of uterine contractions. To comprehensively evaluate three-dimensional uterine electrical activation patterns, we describe here the development of electromyometrial imaging (EMMI) to display the three-dimensional uterine contractions at high spatial and temporal resolution. EMMI combines detailed body surface electrical recording with body-uterus geometry derived from magnetic resonance images. We used a sheep model to show that EMMI can reconstruct uterine electrical activation patterns from electrodes placed on the abdomen. These patterns closely match those measured with electrodes placed directly on the uterine surface. In addition, modeling experiments showed that EMMI reconstructions are minimally affected by noise and geometrical deformation. Last, we show that EMMI can be used to noninvasively measure uterine contractions in sheep in the same setup as would be used in humans. Our results indicate that EMMI can noninvasively, safely, accurately, robustly, and feasibly image three-dimensional uterine electrical activation during contractions in sheep and suggest that similar results might be obtained in clinical setting.

Estimation of 8-Electrode Configuration for Recognition of Uterine Contraction with Electrohysterogram

As the representative of electrical activity from uterine muscle, electrohysterogram (EHG) is recorded non-invasively by multiple electrodes positioned on the abdominal surface. The purpose of our paper is to estimate different electrode configurations for recognizing uterine contractions (UCs) with EHG signals. 8-electrode configuration was taken as an example to show our novel method with convolutional neural network (CNN) classification and score. The open accessed Icelandic 16-electrode EHG database was adopted in our study. With 8-electrode configuration, EHG signals corresponding to UCs and non-UCs were segmented and saved as image patches. The CNN was established and trained by thousands of EHG segments. The performance of CNN was evaluated by the area under curve (AUC) and accuracy of recognizing UCs and non-UCs. Seven different 8-electrode configurations were scored and ranked. It was found the 8-electrode configuration with 4 on the uterine fundus, 2 on the body and 2 on the cervix achieved the AUC of 0.766 and the highest score of 2.197. Among the configurations we have tried, it is concluded that the 8 electrodes in 4-2-2 configuration placed along the uterus as an upside-down pear could provide the most important information for recognition of UC based on our experiments.

Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest

Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26^th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26^th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26^th week of gestation.

New electrohysterogram-based estimators of intrauterine pressure signal, tonus and contraction peak for non-invasive labor monitoring

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  =  14.61 mm Hg, MPe  =  29.17 mm Hg and Toe  =  7.8 mm Hg. The adaptive models significantly reduced errors to CPe  =  11.88, MPe  =  16.02 and Toe  =  5.61 mm 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 well-being.

Development of Electrohysterogram Recording System for Monitoring Uterine Contraction

Uterine contraction (UC) is an important clinical indictor for monitoring uterine activity. The purpose of this study is to develop a portable electrohysterogram (EHG) recording system (called PregCare) for monitoring UCs with EHG signals. The PregCare consisted of sensors, a signal acquisition device, and a computer with application software. Eight-channel EHG signals, the tocodynamometry (TOCO) signal, and maternal perception were recorded simultaneously by the signal acquisition device controlled by the computer via Bluetooth. PregCare was firstly evaluated by a signal simulator. Its relative error (RE) and coefficient of variation (CV) were calculated, and its agreement with the commercial instrument PowerLab was assessed by Bland-Altman plots. After that, PregCare was applied to 20 pregnant women in a hospital to record their EHG signals. These EHG signals were preprocessed and segmented into UCs and non-UCs. Then, the EHG features corresponding to UCs and non-UCs were extracted, respectively, including power spectral density (PSD), root mean square (RMS), peak frequency (PF), median frequency (MDF), and sample entropy (SamEn). One-way ANOVA was employed to assess the difference between UCs and non-UCs. The results show that RE and CV were less than 8% and 0.03%, respectively, which indicated the high accuracy and repeatability of PregCare. The small differences of mean and standard deviation indicated the high agreement between PregCare and PowerLab. Besides, the PSD of UCs was much larger than non-UCs between 0 and 0.7 Hz. RMS of UCs was significantly larger than non-UCs (p < 0.05). PF and SamEn of UCs were significantly smaller than non-UCs (p < 0.05). In conclusion, the developed EHG recording system was able to record EHG signals reliably. It has the advantages of portability, low power consumption, and wireless transmission, which can be used for long-term monitoring of UCs and prediction of the preterm delivery.

Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions

The aims of this study were to apply decision tree to classify uterine activities (contractions and non-contractions) using the waveform characteristics derived from different channels of electrohysterogram (EHG) signals and then rank the importance of these characteristics. Both the tocodynamometer (TOCO) and 8-channel EHG signals were simultaneously recorded from 34 healthy pregnant women within 24 h before delivery. After preprocessing of EHG signals, EHG segments corresponding to the uterine contractions and non-contractions were manually extracted from both original and normalized EHG signals according to the TOCO signals and the human marks. 24 waveform characteristics of the EHG segments were derived separately from each channel to train the decision tree and classify the uterine activities. The results showed the Power and sample entropy (SamEn) extracted from the un-normalized EHG segments played the most important roles in recognizing uterine activities. In addition, the EHG signal characteristics from channel 1 produced better classification results (AUC = 0.75, Sensitivity = 0.84, Specificity = 0.78, Accuracy = 0.81) than the others. In conclusion, decision tree could be used to classify the uterine activities, and the Power and SamEn of un-normalized EHG segments were the most important characteristics in uterine contraction classification.

Clinical Use of Electrohysterography During Term Labor: A Systematic Review on Diagnostic Value, Advantages, and Limitations

Importance

Real-time electrohysterography (EHG)-based technologies have recently become available for uterine monitoring during term labor. Therefore, obstetricians need to be familiar with the diagnostic value, advantages, and limitations of using EHG.

Objective

The aims of this study were to determine the diagnostic value of EHG in comparison to (1) the intrauterine pressure catheter (IUPC), (2) the external tocodynamometer (TOCO), and (3) in case of maternal obesity; (4) to evaluate EHG from users' and patients' perspectives; and (5) to assess whether EHG can predict labor outcome.

Evidence Acquisition

A systematic review was performed in the MEDLINE, EMBASE, and Cochrane library in October 2017 resulting in 209 eligible records, of which 20 were included.

Results

A high sensitivity for contraction detection was achieved by EHG (range, 86.0%-98.0%), which was significantly better than TOCO (range, 46.0%-73.6%). Electrohysterography also enhanced external monitoring in case of maternal obesity. The contraction frequency detected by EHG was on average 0.3 to 0.9 per 10 minutes higher compared with IUPC, which resulted in a positive predictive value of 78.7% to 92.0%. When comparing EHG tocograms with IUPC traces, an underestimation of the amplitude existed despite that patient-specific EHG amplitudes have been mitigated by amplitude normalization. Obstetricians evaluated EHG tocograms as better interpretable and more adequate than TOCO. Finally, potential EHG parameters that could predict a vaginal delivery were a predominant fundal direction and a lower peak frequency.

Conclusions and Relevance

Electrohysterography enhances external uterine monitoring of both nonobese and obese women. Obstetricians consider EHG as better interpretable; however, they need to be aware of the higher contraction frequency detected by EHG and of the amplitude mismatch with intrauterine pressure measurements.

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

As one of the main aims of obstetrics is to be able to detect imminent delivery in patients with threatened preterm labor, the techniques currently used in clinical practice have serious limitations in this respect. The electrohysterogram (EHG) has now emerged as an alternative technique, providing relevant information about labor onset when recorded in controlled checkups without administration of tocolytic drugs. The studies published to date mainly focus on EHG-burst analysis and, to a lesser extent, on whole EHG window analysis. The study described here assessed the ability of EHG signals to discriminate imminent labor (< 7 days) in women with threatened preterm labor undergoing tocolytic therapy, using both EHG-burst and whole EHG window analyses, by calculating temporal, spectral, and non-linear parameters. Only two non-linear EHG-burst parameters and four whole EHG window analysis parameters were able to distinguish the women who delivered < 7 days from the others, showing that EHG can provide relevant information on the approach of labor, even in women with threatened preterm labor under the effects of tocolytic therapy. The whole EHG window outperformed the EHG-burst analysis and is seen as a step forward in the development of real-time EHG systems able to predict imminent labor in clinical praxis. Graphical abstract The ability of EHG recordings to predict imminent labor (< 7 days) was analyzed in preterm threatened patients undergoing tocolytic therapies by means of EHG-burst and whole EHG window analysis. The non-linear features were found to have better performance than the temporal and spectral parameters in separating women who delivered in less than 7 days from those who did not.

Characterization and automatic classification of preterm and term uterine records

Predicting preterm birth is uncertain, and numerous scientists are searching for non-invasive methods to improve its predictability. Current researches are based on the analysis of ElectroHysteroGram (EHG) records, which contain information about the electrophysiological properties of the uterine muscle and uterine contractions. Since pregnancy is a long process, we decided to also characterize, for the first time, non-contraction intervals (dummy intervals) of the uterine records, i.e., EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal). For this purpose, we developed a new set of uterine records, TPEHGT DS, containing preterm and term uterine records of pregnant women, and uterine records of non-pregnant women. We quantitatively characterized contraction intervals (contractions) and dummy intervals of the uterine records of the TPEHGT DS in terms of the normalized power spectra of the EHG and TOCO signals, and developed a new method for predicting preterm birth. The results on the characterization revealed that the peak amplitudes of the normalized power spectra of the EHG and TOCO signals of the contraction and dummy intervals in the frequency band 1.0-2.2 Hz, describing the electrical and mechanical activity of the uterus due to the maternal heart (maternal heart rate), are high only during term pregnancies, when the delivery is still far away; and they are low when the delivery is close. However, these peak amplitudes are also low during preterm pregnancies, when the delivery is still supposed to be far away (thus suggesting the danger of preterm birth); and they are also low or barely present for non-pregnant women. We propose the values of the peak amplitudes of the normalized power spectra due to the influence of the maternal heart, in an electro-mechanical sense, in the frequency band 1.0-2.2 Hz as a new biophysical marker for the preliminary, or early, assessment of the danger of preterm birth. The classification of preterm and term, contraction and dummy intervals of the TPEHGT DS, for the task of the automatic prediction of preterm birth, using sample entropy, the median frequency of the power spectra, and the peak amplitude of the normalized power spectra, revealed that the dummy intervals provide quite comparable and slightly higher classification performances than these features obtained from the contraction intervals. This result suggests a novel and simple clinical technique, not necessarily to seek contraction intervals but using the dummy intervals, for the early assessment of the danger of preterm birth. Using the publicly available TPEHG DB database to predict preterm birth in terms of classifying between preterm and term EHG records, the proposed method outperformed all currently existing methods. The achieved classification accuracy was 100% for early records, recorded around the 23rd week of pregnancy; and 96.33%, the area under the curve of 99.44%, for all records of the database. Since the proposed method is capable of using the dummy intervals with high classification accuracy, it is also suitable for clinical use very early during pregnancy, around the 23rd week of pregnancy, when contractions may or may not be present.

Automated electrohysterographic detection of uterine contractions for monitoring of pregnancy: feasibility and prospects

Background

Preterm birth is a major public health problem in developed countries. In this context, we have conducted research into outpatient monitoring of uterine electrical activity in women at risk of preterm delivery. The objective of this preliminary study was to perform automated detection of uterine contractions (without human intervention or tocographic signal, TOCO) by processing the EHG recorded on the abdomen of pregnant women. The feasibility and accuracy of uterine contraction detection based on EHG processing were tested and compared to expert decision using external tocodynamometry (TOCO) .

Methods

The study protocol was approved by local Ethics Committees under numbers ID-RCB 2016-A00663-48 for France and VSN 02-0006-V2 for Iceland.

Two populations of women were included (threatened preterm birth and labour) in order to test our system of recognition of the various types of uterine contractions.

EHG signal acquisition was performed according to a standardized protocol to ensure optimal reproducibility of EHG recordings. A system of 18 Ag/AgCl surface electrodes was used by placing 16 recording electrodes between the woman’s pubis and umbilicus according to a 4 × 4 matrix. TOCO was recorded simultaneously with EHG recording.

EHG signals were analysed in real-time by calculation of the nonlinear correlation coefficient H². A curve representing the number of correlated pairs of signals according to the value of H² calculated between bipolar signals was then plotted. High values of H² indicated the presence of an event that may correspond to a contraction.

Two tests were performed after detection of an event (fusion and elimination of certain events) in order to increase the contraction detection rate.

Results

The EHG database contained 51 recordings from pregnant women, with a total of 501 contractions previously labelled by analysis of the corresponding tocographic recording. The percentage recognitions obtained by application of the method based on coefficient H² was 100% with 782% of false alarms. Addition of fusion and elimination tests to the previously obtained detections allowed the false alarm rate to be divided by 8.5, while maintaining an excellent detection rate (96%).

Conclusion

These preliminary results appear to be encouraging for monitoring of uterine contractions by algorithm-based automated detection to process the electrohysterographic signal (EHG). This compact recording system, based on the use of surface electrodes attached to the skin, appears to be particularly suitable for outpatient monitoring of uterine contractions, possibly at home, allowing telemonitoring of pregnancies. One of the advantages of EHG processing is that useful information concerning contraction efficiency can be extracted from this signal, which is not possible with the TOCO signal.

The uterine pacemaker of labor

The laboring uterus is generally thought to initiate contractions much similar to the heart, with a single, dedicated pacemaker. Research on human and animal models over decades has failed to identify such pacemaker. On the contrary, data indicate that instead of being fixed at a site similar to the sinoatrial node of the heart, the initiation site for each uterine contraction changes during time, often with each contraction. The enigmatic uterine "pacemaker" does not seem to fit the standard definition of what a pacemaker should be. The uterine pacemaker must also mesh with the primary physiological function of the uterus - to generate intrauterine pressure. This requires that most areas of the uterine wall contract in a coordinated, or synchronized, manner for each contraction of labor. It is not clear whether the primary mechanism of the uterine pacemaker is a slow-wave generator or an impulse generator. Slow waves in the gut initiate localized smooth muscle contractions. Because the uterus and the gut have somewhat similar cellular and tissue structure, it is reasonable to consider if uterine contractions are paced by a similar mechanism. Unfortunately, there is no convincing experimental verification of uterine slow waves. Similarly, there is no convincing evidence of a cellular mechanism for impulse generation. The uterus appears to have multiple widely dispersed mechanically sensitive functional pacemakers. It is possible that the coordination of organ-level function occurs through intrauterine pressure, thus creating wall stress followed by activation of many mechanosensitive electrogenic pacemakers.

Classification of pregnancy and labor contractions using a graph theory based analysis

In this paper, we propose a new framework to characterize the electrohysterographic (EHG) signals recorded during pregnancy and labor. The approach is based on the analysis of the propagation of the uterine electrical activity. The processing pipeline includes i) the estimation of the statistical dependencies between the different recorded EHG signals, ii) the characterization of the obtained connectivity matrices using network measures and iii) the use of these measures in clinical application: the classification between pregnancy and labor. Due to its robustness to volume conductor, we used the imaginary part of coherence in order to produce the connectivity matrix which is then transformed into a graph. We evaluate the performance of several graph measures. We also compare the results with the parameter mostly used in the literature: the peak frequency combined with the propagation velocity (PV +PF). Our results show that the use of the network measures is a promising tool to classify labor and pregnancy contractions with a small superiority of the graph strength over PV+PF.