Use of uterine electromyography to diagnose term and preterm labor

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.

The latent phase of labor

The latent phase of labor extends from the initiation of labor to the onset of the active phase. Because neither margin is always precisely identifiable, the duration of the latent phase often can only be estimated. During this phase, the cervix undergoes a process of rapid remodeling, which may have begun gradually weeks before. As a consequence of extensive changes in its collagen and ground substance, the cervix softens, becomes thinner and dramatically more compliant, and may dilate modestly. All of these changes prepare the cervix for the more rapid dilatation that will occur during the active phase to follow. For the clinician, it is important to recognize that the latent phase may normally extend for many hours. The normal limit for the duration of the latent phase should be considered to be approximately 20 hours in a nullipara and 14 hours in a multipara. Factors that have been associated with a prolonged latent phase include deficient prelabor or intrapartum cervical remodeling, excessive maternal analgesia or anesthesia, maternal obesity, and chorioamnionitis. Approximately 10% of women with a prolonged latent phase are actually in false labor, and their contractions eventually abate spontaneously. The management of a prolonged latent phase involves either augmenting uterine activity with oxytocin or providing a sedative-induced period of maternal rest. Both are equally effective in advancing the labor to active phase dilatation. A very long latent phase may be a harbinger of other labor dysfunctions.

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.

Stretchable Sponge Electrodes for Long-Term and Motion-Artifact-Tolerant Recording of High-Quality Electrophysiologic Signals

Soft electronic devices and sensors have shown great potential for wearable and ambulatory electrophysiologic signal monitoring applications due to their light weight, ability to conform to human skin, and improved wearing comfort, and they may replace the conventional rigid electrodes and bulky recording devices widely used nowadays in clinical settings. Herein, we report an elastomeric sponge electrode that offers greatly reduced electrode-skin contact impedance, an improved signal-to-noise ratio (SNR), and is ideally suited for long-term and motion-artifact-tolerant recording of high-quality biopotential signals. The sponge electrode utilizes a porous polydimethylsiloxane sponge made from a sacrificial template of sugar cubes, and it is subsequently coated with a poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) conductive polymer using a simple dip-coating process. The sponge electrode contains numerous micropores that greatly increase the skin-electrode contact area and help lower the contact impedance by a factor of 5.25 or 6.7 compared to planar PEDOT:PSS electrodes or gold-standard Ag/AgCl electrodes, respectively. The lowering of contact impedance resulted in high-quality electrocardiogram (ECG) and electromyogram (EMG) recordings with improved SNR. Furthermore, the porous structure also allows the sponge electrode to hold significantly more conductive gel compared to conventional planar electrodes, thereby allowing them to be used for long recording sessions with minimal signal degradation. The conductive gel absorbed into the micropores also serves as a buffer layer to help mitigate motion artifacts, which is crucial for recording on ambulatory patients. Lastly, to demonstrate its feasibility and potential for clinical usage, we have shown that the sponge electrode can be used to monitor uterine contraction activities from a patient in labor. With its low-cost fabrication, softness, and ability to record high SNR biopotential signals, the sponge electrode is a promising platform for long-term wearable health monitoring applications.

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.

Network theory based EHG signal analysis and its application in preterm prediction

OBJECTIVE

Preterm birth is the leading cause of neonatal morbidity and mortality. Early identification of high-risk patients followed by medical interventions is essential to the prevention of preterm birth. Based on the relationship between uterine contraction and the fundamental electrical activities of muscles, we extracted effective features from EHG signals recorded from pregnant women, and use them to train classifiers with the purpose of providing high precision in classifying term and preterm pregnancies.

METHODS

To characterize changes from irregularity to coherence of the uterine activity during the whole pregnancy, network representations of the original electrohysterogram (EHG) signals are established by applying the Horizontal Visibility Graph (HVG) algorithm, from which we extract network degree density and distribution, clustering coefficient and assortativity coefficient. Concerns on the interferences of different noise sources embedded in the EHG signal, we apply Short-Time Fourier Transform (STFT) to expand the original signal in the time-frequency domain. This allows a network representation and the extraction of related features on each frequency component. Feature selection algorithms are then used to filter out unrelated frequency components. We further apply the proposed feature extraction method to EHG signals available in the Term-Preterm EHG database (TPEHG), and use them to train classifiers. We adopt the Partition-Synthesis scheme which splits the original imbalanced dataset into two sets and synthesizes artificial samples separately within each subset to solve the problem of dataset imbalance.

RESULTS

The optimally selected network-based features, not only contribute to the identification of the essential frequency components of uterine activities related to preterm birth, but also to improved performance in classifying term/preterm pregnancies, i.e., the SVM (Support Vector Machine) classifier trained with the available samples in the TPEHG gives sensitivity, specificity, overall accuracy, and auc values as high as 0.89, 0.93, 0.91, and 0.97, respectively.

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.

Bursting Rate Variability

In this paper, a new electromyographic phenomenon, referred to as Bursting Rate Variability (BRV), is reported. Not only does it manifest itself visually as a train of short periods of accrued surface electromyographic (sEMG) activity in the traces, but it has a deeper underpinning because the sEMG bursts are synchronous with wavelet packets in the D8 subband of the Daubechies 3 (db3) wavelet decomposition of the raw signal referred to as “D8 doublets”—which are absent during muscle relaxation. Moreover, the db3 wavelet decomposition reconstructs the entire sEMG bursts with two contiguous relatively high detail coefficients at level 8, suggesting a high incidence of two consecutive neuronal discharges. Most importantly, the timing between successive bursts shows some variability, hence the BRV acronym. Contrary to Heart Rate Variability (HRV), where the R-wave is easily identified, here, time-localization of the burst requires a statistical waveform matching between the “D8 doublet” and the burst in the raw sEMG signal. Furthermore, statistical fitting of the empirical distribution of return times shows a striking difference between control and quadriplegic subjects. Finally, the BRV rate appears to be within 60–88 bursts per minute on average among 9 human subjects, suggesting a possible connection between BRV and HRV.

Realistic preterm prediction based on optimized synthetic sampling of EHG signal

Preterm labor is the leading cause of neonatal morbidity and mortality in newborns and has attracted significant research attention from many scientific areas. The relationship between uterine contraction and the underlying electrical activities makes uterine electrohysterogram (EHG) a promising direction for detecting and predicting preterm births. However, due to the scarcity of EHG signals, especially those leading to preterm births, synthetic algorithms have been used to generate artificial samples of preterm birth type in order to eliminate bias in the prediction towards normal delivery, at the expense of reducing the feature effectiveness in automatic preterm detection based on machine learning. To address this problem, we quantify the effect of synthetic samples (balance coefficient) on the effectiveness of features and form a general performance metric by using several feature scores with relevant weights that describe their contributions to class segregation. In combination with the activation/inactivation functions that characterize the effect of the abundance of training samples on the accuracy of the prediction of preterm and normal birth delivery, we obtained an optimal sample balance coefficient that compromises the effect of synthetic samples in removing bias toward the majority group (i.e., normal delivery and the side effect of reducing the importance of features). A more realistic predictive accuracy was achieved through a series of numerical tests on the publicly available TPEHG database, therefore demonstrating the effectiveness of the proposed method.

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.

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.

Uterine electromyography (EMG) measurements to predict preterm caesarean section in patients with complete placenta previa

The objective of the study was to evaluate uterine electrical activity (EA) with EMG methods in pregnant women with complete placenta previa with preterm caesarean section (CS). This prospective study included 78 patients with complete placenta previa who were recorded for uterine EA activity from 32 to 34 weeks of gestation. The clinical and the uterine EMG burst characteristics, that are responsible for contractions, were compared between a preterm CS group (case group, n = 33) and an elective control group (control group, n = 45). The uterine EA burst duration was longer in the case group compared with the control group (28.79 ± 3.75 vs 19.35 ± 2.56 s; p < .001). Also, the number of burst per 30 min was also higher in the case group compared with the control group (3.28 ± 0.18 vs 1.72 ± 0.22; p < .001), Similarly, the RMS was higher in the case group compared with the control group (0.07 ± 0.01 vs 0.04 ± 0.01 mV; p = .041). In addition, the PDS was higher in the case group compared with the control group (0.47 ± 0.03 vs 0.39 ± 0.02 Hz; p = .023). This study demonstrates that women with complete placenta previa have higher uterine EA at 32-34 weeks of gestation and this is associated with a higher risk of preterm CS due to massive vaginal bleeding.IMPACT STATEMENTWhat is already known on this subject? Antepartum massive bleeding in complete placenta previa causes maternal and foetal mortality and morbidity, currently there is no effective method to predict it.What do the results of this study add? This study showed in patients with complete placenta previa who were delivered preterm via emergent caesarean section, the uterine electrical activity measured by uterine electromyography (EMG) at 32-34 weeks of gestation had an active patternWhat are the implications of these findings for clinical practice and/or further research? Uterine EMG is a potential tool to measure uterine electrical activity and can guide clinical management of patients with complete placenta previa, further study are needed to confirm its effectiveness in a large sample size.

Critical analysis of electrohysterographic methods for continuous monitoring of intrauterine pressure

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.

Accuracy of electromyometrial imaging of uterine contractions in clinical environment

Clinically, uterine contractions are monitored with tocodynamometers or intrauterine pressure catheters. In the research setting, electromyography (EMG), which detects electrical activity of the uterus from a few electrodes on the abdomen, is feasible, can provide more accurate data than these other methods, and may be useful for predicting preterm birth. However, EMG lacks sufficient spatial resolution and coverage to reveal where uterine contractions originate, how they propagate, and whether preterm contractions differ between women who do and do not progress to preterm delivery. To address those limitations, electromyometrial imaging (EMMI) was recently developed and validated to non-invasively assess three-dimensional (3D) electrical activation patterns on the entire uterine surface in pregnant sheep. EMMI uses magnetic resonance imaging to obtain subject-specific body-uterus geometry and collects uterine EMG data from up to 256 electrodes on the body surface. EMMI software then solves an ill-posed inverse computation to combine the two datasets and generate maps of electrical activity on the entire 3D uterine surface. Here, we assessed the feasibility to clinically translate EMMI by evaluating EMMI's accuracy under the unavoidable geometrical alterations and electrical noise contamination in a clinical environment. We developed a hybrid experimental-simulation platform to model the effects of fetal kicks, contractions, fetal/maternal movements, and noise contamination caused by maternal respiration and environmental electrical activity. Our data indicate that EMMI can accurately image uterine electrical activity in the presence of geometrical deformations and electrical noise, suggesting that EMMI can be reliably translated to non-invasively image 3D uterine electrical activation in pregnant women.

Multifractal Analysis of Uterine Electromyography Signals for the Assessment of Progression of Pregnancy in Term Conditions

OBJECTIVES

The objectives of this paper are to examine the source of multifractality in uterine electromyography (EMG) signals and to study the progression of pregnancy in the term (gestation period > 37 weeks) conditions using multifractal detrending moving average (MFDMA) algorithm.

METHODS

The signals for the study, considered from an online database, are obtained from the surface of abdomen during the second (T1) and third trimester (T2). The existence of multifractality is tested using Hurst and scaling exponents. With the intention of identifying the origin of multifractality, the preprocessed signals are converted to shuffle and surrogate data. The original and the transformed signals are subjected to MFDMA to extract multifractal spectrum features, namely strength of multifractality, maximum, minimum, and peak singularity exponents.

RESULTS

The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature. Further analysis shows that the source of multifractality is mainly owing to the presence of long-range correlation, which is computed as 79.98% in T1 and 82.43% in T2 groups. Among the extracted features, the peak singularity exponent and strength of multifractality show statistical significance in identifying the progression of pregnancy. The corresponding coefficients of variation are found to be low, which show that these features have low intersubject variability.

CONCLUSION

It appears that the multifractal analysis can help in investigating the progressive changes in uterine muscle contractions during pregnancy.

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.

Feasibility of Transabdominal Electrohysterography for Analysis of Uterine Activity in Nonpregnant Women

PURPOSE

Uterine activity plays a key role in reproduction, and altered patterns of uterine contractility have been associated with important physiopathological conditions, such as subfertility, dysmenorrhea, and endometriosis. However, there is currently no method to objectively quantify uterine contractility outside pregnancy without interfering with the spontaneous contraction pattern. Transabdominal electrohysterography has great potential as a clinical tool to characterize noninvasively uterine activity, but results of this technique in nonpregnant women are poorly documented. The purpose of this study is to investigate the feasibility of transabdominal electrohysterography in nonpregnant women.

METHODS

Longitudinal measurements were performed on 22 healthy women in 4 representative phases of the menstrual cycle. Twelve electrohysterogram-based indicators previously validated in pregnancy have been estimated and compared in the 4 phases of the cycle. Using the Tukey honest significance test, significant differences were defined for P values below .05.

RESULTS

Half of the selected electrohysterogram-based indicators showed significant differences between menses and at least 1 of the other 3 phases, that is the luteal phase.

CONCLUSION

Our results suggest transabdominal electrohysterography to be feasible for analysis of uterine activity in nonpregnant women. Due to the lack of a golden standard, this feasibility study is indirectly validated based on physiological observations. However, these promising results motivate further research aiming at evaluating electrohysterography as a method to improve understanding and management of dysfunctions (possibly) related to altered uterine contractility, such as infertility, endometriosis, and dysmenorrhea.

Comparison of labour induction with misoprostol and dinoprostone and characterization of uterine response based on electrohysterogram

OBJECTIVE

The objective of this study is to compare the uterine activity response between women administered dinoprostone (prostaglandin E2) and misoprostol (prostaglandin E1) for induction of labour (IOL) by analysing not only the traditional obstetric data but also the parameters extracted from uterine electrohysterogram (EHG).

METHODS

Two cohorts were defined: misoprostol (25-µg vaginal tablets; 251 women) and dinoprostone cohort (10 mg vaginal inserts; 249 women). All the mothers were induced by a medical indication of a Bishop Score < = 6.

RESULTS

The misoprostol cohort was associated with a shorter time to achieve active labour (p = .017) and vaginal delivery (p = .009) and with a higher percentage of vaginal delivery in less than 24 h in mothers with a very unfavourable cervix score (risk ratio (RR): 1.41, IC95% 1.17-1.69, p = .002). Successful inductions with misoprostol showed EHG parameter values significantly higher than basal state for amplitude and pseudo Montevideo units (PMU) 60' after drug administration, while spectral parameters significantly increased after 150'. This response was not observed in failed inductions. In the successful dinoprostone group, the duration and number of contractions increased significantly after 120', PMU did so after 180', and no significant differences were found for spectral parameters, possibly due to the slower pharmacokinetics of this drug.

CONCLUSION

Successful inductions of labour by misoprostol are associated with earlier effective contractions than in labours induced by dinoprostone.

Tocolysis: Present and future treatment options

In the United States, the generally accepted indication for tocolytic therapy centers on suppression of preterm labor. This may be in the form of preventative therapy with progesterone in women with prior spontaneous preterm birth or as an acute intervention to suppress established uterine contractions associated with cervical change occurring at less than 37 weeks gestation. This article seeks to apply this perspective to tocolytic therapy. Here, we provide a review of current tocolytic options and what the last decade of discovery has revealed about the regulation of myometrial excitability and quiescence. Moving forward, we must incorporate the emerging molecular data that is amassing in order to develop novel and effective tocolytic therapeutic options to prevent preterm labor and spontaneous preterm birth (sPTB).

ESTIMATION OF INTRAUTERINE PRESSURE FROM ELECTROHYSTEROGRAPHY USING HILBERT PHASE SLIPS AND STATISTICS METHOD

Prognostic information during pregnancy can be obtained by monitoring maternal uterine activity. Tocodynamometry (TOCO) is widely used to assess the uterine activity today but it has been found that it has very low sensitivity. Another method to assess the uterine activity is intrauterine pressure catheter (IUPC) which is accurate but highly invasive. Electrohysterogram (EHG) measured from abdominal surface is a noninvasive method to detect uterine contractions. To reduce motion artifacts of intrauterine pressure (IUP) estimated from EHG signal and further improve the accuracy of contractions detected by IUP estimation, we propose a method to divide the EHG signal into segments by using Hilbert phase slips. Standard deviation (STD) was used to estimate IUP from each EHG signal segment and median filter was used to remove the motion artifacts. The method we proposed was compared with other four methods from literatures. The proposed method results in a higher contractions detection accuracy of EHG-based IUP estimation and a higher correlation coefficient with the IUPC signals compared to other methods which demonstrated the capabilities of the proposed method in reducing motion artifacts of IUP estimation based on abdominal EHG.

Dedicated Entropy Measures for Early Assessment of Pregnancy Progression From Single-Channel Electrohysterography

OBJECTIVE

Preterm birth is a large-scale clinical problem involving over 10% of infants. Diagnostic means for timely risk assessment are lacking and the underlying physiological mechanisms unclear. To improve the evaluation of pregnancy before term, we introduce dedicated entropy measures derived from a single-channel electrohysterogram (EHG).

METHODS

The estimation of approximate entropy (ApEn) and sample entropy (SampEn) is adjusted to monitor variations in the regularity of single-channel EHG recordings, reflecting myoelectrical changes due to pregnancy progression. In particular, modifications in the tolerance metrics are introduced for improving robustness to EHG amplitude fluctuations. An extensive database of 58 EHG recordings with 4 monopolar channels in women presenting with preterm contractions was manually annotated and used for validation. The methods were tested for their ability to recognize the onset of labor and the risk of preterm birth. Comparison with the best single-channel methods according to the literature was performed.

RESULTS

The reference methods were outperformed. SampEn and ApEn produced the best prediction of delivery, although only one channel showed a significant difference () between labor and nonlabor. The modified ApEn produced the best prediction of preterm delivery, showing statistical significance () in three channels. These results were also confirmed by the area under the receiver operating characteristic curve and fivefold cross validation.

CONCLUSION

The use of dedicated entropy estimators improves the diagnostic value of EHG analysis earlier in pregnancy.

SIGNIFICANCE

Our results suggest that changes in the EHG might manifest early in pregnancy, providing relevant prognostic opportunities for pregnancy monitoring by a practical single-channel solution.

Simultaneous Recording and Analysis of Uterine and Abdominal Muscle Electromyographic Activity in Nulliparous Women During Labor

OBJECTIVE

To record and characterize electromyography (EMG) from the uterus and abdominal muscles during the nonlabor to first and second stages of labor and to define relationships to contractions.

METHODS

Nulliparous patients without any treatments were used (n = 12 nonlabor stage, 48 during first stage and 33 during second stage). Electromyography of both uterine and abdominal muscles was simultaneously recorded from electrodes placed on patients' abdominal surface using filters to separate uterine and abdominal EMG. Contractions of muscles were also recorded using tocodynamometry. Electromyography was characterized by analysis of various parameters.

RESULTS

During the first stage of labor, when abdominal EMG is absent, uterine EMG bursts temporally correspond to contractions. In the second stage, uterine EMG bursts usually occur at same frequency as groups of abdominal bursts and precede abdominal bursts, whereas abdominal EMG bursts correspond to contractions and are accompanied by feelings of "urge to push." Uterine EMG increases progressively from nonlabor to second stage of labor.

CONCLUSIONS

(1) Uterine EMG activity can be separated from abdominal EMG events by filtering. (2) Uterine EMG gradually evolves from the antepartum stage to the first and second stages of labor. (3) Uterine and abdominal EMG reflect electrical activity of the muscles during labor and are valuable to assess uterine and abdominal muscle events that control labor. (4) During the first stage of labor uterine, EMG is responsible for contractions, and during the second stage, both uterine and abdominal muscle participate in labor.

Effect of filtering on the classification rate of nonlinear analysis methods applied to uterine EMG signals

Nonlinear time series analysis can provide useful information regarding nonlinear features of biological signals. The effect of filtering on the performance of nonlinear methods is not well-understood. In this work, we investigate the effects of signal filtering on the sensitivity of four nonlinear methods: Time reversibility, Sample Entropy, Lyapunov Exponents and Delay Vector Variance. These methods were applied to uterine EMG signals with the aim of using them to discriminate between pregnancy and labor contractions. The signals were filtered using three different band-pass filters before the application of the methods. Results showed that the sensitivity of some methods such as sample entropy was significantly improved with filtering. On the other hand, filtering had little effect on some other methods such as time reversibility. This study concludes that while filtering increases computation time, it may be necessary for some nonlinear methods particularly those with low sensitivity.

Vaginal electrohysterography: the design and preliminary evaluation of a novel device for uterine contraction monitoring in an ovine model (.)

OBJECTIVE

Tocodynamometry is the most common method of labor evaluation but most clinicians would agree it has limited utility before 26 weeks of gestation. The obesity epidemic has further reduced our ability to accurately detect uterine contractions using the tocodynamometer at any gestational age. We sought to design and test a novel contraction monitor that bypasses the maternal abdomen.

METHODS

An optimized version of an intravaginal electrohysterographic ring device was tested in an ovine model. The device and its methodology as well as the tocodynamometer were validated against the current gold standard uterine activity monitor, the intrauterine pressure catheter in six sheep at varying gestational ages.

RESULTS

Both the intravaginal ring device and the tocodynamometer correlated well with IUPC, r = 0.69 and 0.73, respectively (p < 0.001). The number of contractions detected by each monitor remained similar even after accounting for confounders.

CONCLUSIONS

These results suggest that uterine activity can be monitored from the vaginal interface in an ovine model and offers an alternative clinical tool for the detection of contractions in situations, in which tocodynamometry would be ineffective or intrauterine monitoring inappropriate.

The Icelandic 16-electrode electrohysterogram database

External recordings of the electrohysterogram (EHG) can provide new knowledge on uterine electrical activity associated with contractions. Better understanding of the mechanisms underlying labor can contribute to preventing preterm birth which is the main cause of mortality and morbidity in newborns. Promising results using the EHG for labor prediction and other uses in obstetric care are the drivers of this work. This paper presents a database of 122 4-by-4 electrode EHG recordings performed on 45 pregnant women using a standardized recording protocol and a placement guide system. The recordings were performed in Iceland between 2008 and 2010. Of the 45 participants, 32 were measured repeatedly during the same pregnancy and participated in two to seven recordings. Recordings were performed in the third trimester (112 recordings) and during labor (10 recordings). The database includes simultaneously recorded tocographs, annotations of events and obstetric information on participants. The publication of this database enables independent and novel analysis of multi-electrode EHG by the researchers in the field and hopefully development towards new life-saving technology.

Effects of Patient-Controlled Epidural Analgesia on Uterine Electromyography During Spontaneous Onset of Labor in Term Nulliparous Women

OBJECTIVE

To investigate the effect of patient-controlled epidural analgesia (PCEA) on uterine electromyography (EMG) activity in term pregnant women during labor.

METHODS

Nulliparous pregnant women in spontaneous term labor (N = 30) were enrolled (PCEA group, n = 20 and control group, n = 10). Five time periods (30 minutes each) were defined for noninvasive abdominal recordings and analysis of uterine EMG activity, that is, period I: before PCEA treatment with 2-cm cervical dilation; periods II to IV: each period successively at 30, 60, and 120 minutes after PCEA; and period V: second stage of labor with cervix at 10 cm dilation. Control patients without PCEA were monitored during the same times. The number of bursts/30 min, power density spectrum peak frequency, mean amplitude, and duration of uterine EMG bursts were measured to assess uterine EMG activity. Maternal, fetal, and labor characteristics were also recorded. Data were analyzed by analysis of variance followed by other tests.

RESULTS

Electromyography parameters are significantly lower (P < .001) after PCEA (periods II to IV) compared to controls but similar between groups by period V (P > .05). Also, patients with PCEA have a slower rate of cervical dilation (P < .003, period IV only) and longer labor in both stage 1 and stage 2 (P < .05). All patients have similar (P > .05) positive labor outcomes.

CONCLUSIONS

Patient-controlled epidural analgesia initially suppresses uterine EMG and slows cervical dilation thereby prolonging labor. However, the EMG activity recovers with labor progress with no effects on delivery outcomes.

Propagation of electrical activity in uterine muscle during pregnancy: a review

The uterine muscle (the myometrium) plays its most evident role during pregnancy, when quiescence is required for adequate nourishment and development of the foetus, and during labour, when forceful contractions are needed to expel the foetus and the other products of conception. The myometrium is composed of smooth muscle cells. Contraction is initiated by the spontaneous generation of electrical activity at the cell level in the form of action potentials. The mechanisms underlying uterine quiescence during pregnancy and electrical activation during labour remain largely unknown; as a consequence, the clinical management of preterm contractions during pregnancy and inefficient uterine contractility during labour remains suboptimal. In an effort to improve clinical management of uterine contractions, research has focused on understanding the propagation properties of the electrical activity of the uterus. Different perspectives have been undertaken, from animal and in vitro experiments up to clinical studies and dedicated methods for non-invasive parameter estimation. A comparison of the results is not straightforward due to the wide range of different approaches reported in the literature. However, previous studies unanimously reveal a unique complexity as compared to other organs in the pattern of uterine electrical activity propagation, which necessarily needs to be taken into consideration for future studies to be conclusive. The aim of this review is to structure current variegated knowledge on the properties of the uterus in terms of pacemaker position, pattern, direction and speed of the electrical activity during pregnancy and labour.

Progress in understanding electro-mechanical signalling in the myometrium

In this review, we give a state-of-the-art account of uterine contractility, focussing on excitation-contraction (electro-mechanical) coupling (ECC). This will show how electrophysiological data and intracellular calcium measurements can be related to more modern techniques such as confocal microscopy and molecular biology, to advance our understanding of mechanical output and its modulation in the smooth muscle of the uterus, the myometrium. This new knowledge and understanding, for example concerning the role of the sarcoplasmic reticulum (SR), or stretch-activated K channels, when linked to biochemical and molecular pathways, provides a clearer and better informed basis for the development of new drugs and targets. These are urgently needed to combat dysfunctions in excitation-contraction coupling that are clinically challenging, such as preterm labour, slow to progress labours and post-partum haemorrhage. It remains the case that scientific progress still needs to be made in areas such as pacemaking and understanding interactions between the uterine environment and ion channel activity.

Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals

Several measures have been proposed to detect nonlinear characteristics in time series. Results on time series, multiple surrogates and their z-score are used to statistically test for the presence or absence of non-linearity. The z-score itself has sometimes been used as a measure of nonlinearity. The sensitivity of nonlinear methods to the nonlinearity level and their robustness to noise have rarely been evaluated in the past. While surrogates are important tools to rigorously detect nonlinearity, their usefulness for evaluating the level of nonlinearity is not clear. In this paper we investigate the performance of four methods arising from three families that are widely used in non-linearity detection: statistics (time reversibility), predictability (sample entropy, delay vector variance) and chaos theory (Lyapunov exponents). We used sensitivity to increasing complexity and the mean square error (MSE) of Monte Carlo instances for quantitative comparison of their performances. These methods were applied to a Henon nonlinear synthetic model in which we can vary the complexity degree (CD). This was done first by applying the methods directly to the signal and then using the z-score (surrogates) with and without added noise. The methods were then applied to real uterine EMG signals and used to distinguish between pregnancy and labor contraction bursts. The discrimination performances were compared to linear frequency based methods classically used for the same purpose such as mean power frequency (MPF), peak frequency (PF) and median frequency (MF). The results show noticeable difference between different methods, with a clear superiority of some of the nonlinear methods (time reversibility, Lyapunov exponents) over the linear methods. Applying the methods directly to the signals gave better results than using the z-score, except for sample entropy.

Velocity and directionality of the electrohysterographic signal propagation

OBJECTIVE

The initiation of treatment for women with threatening preterm labor requires effective distinction between true and false labor. The electrohysterogram (EHG) has shown great promise in estimating and classifying uterine activity. However, key issues remain unresolved and no clinically usable method has yet been presented using EHG. Recent studies have focused on the propagation velocity of the EHG signals as a potential discriminator between true and false labor. These studies have estimated the propagation velocity of individual spikes of the EHG signals. We therefore focus on estimating the propagation velocity of the entire EHG burst recorded during a contraction in two dimensions.

STUDY DESIGN

EHG measurements were performed on six women in active labor at term, and a total of 35 contractions were used for the estimation of propagation velocity. The measurements were performed using a 16-channel two-dimensional electrode grid. The estimates were calculated with a maximum-likelihood approach.

RESULTS

The estimated average propagation velocity was 2.18 (±0.68) cm/s. No single preferred direction of propagation was found.

CONCLUSION

The propagation velocities estimated in this study are similar to those reported in other studies but with a smaller intra- and inter-patient variation. Thus a potential tool has been established for further studies on true and false labor contractions.

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

Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique.

Prediction of preterm deliveries from EHG signals using machine learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.