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

Performance of a Maternal Abdominal Surface Electrode System for Fetal Heart Rate and Uterine Contraction Monitoring from 34 to 37 Weeks

OBJECTIVE

The objective of this study was to compare performance of a maternal surface electrode patch with ultrasound- and tocodynamometer-based monitoring to detect fetal heart rate and uterine contractility in late preterm labors.

STUDY DESIGN

Thirty women between 340/7 and 366/7 weeks' gestation were monitored simultaneously with a Doppler/tocodynamometer system and a wireless fetal-maternal abdominal surface electrode system. Fetal and maternal heart rate and uterine contraction data from both systems were compared. Reliability was measured by the success rate and percent agreement. Deming regression and Bland-Altman analysis estimated the concordance between the systems. Uterine contractions were assessed by visual interpretation of monitor tracings.

RESULTS

The success rate for the surface electrode system was 89.5% (95% confidence interval [CI], 85.7-93.3), and for ultrasound it was 88.4% (95% CI, 84.9-91.9; p = 0.73), with a percent agreement of 88.1% (95% CI, 84.2-92.8). Results were uninfluenced by the patients' body mass. The mean Deming slope was 1 and the y-intercept was -3.0 beats per minute (bpm). Bland-Altman plots also showed a close relationship between the methods, with limits of agreement less than 10 bpm. The percent agreement for maternal heart rate was 98.2% (95% CI, 97.4-98.8), and for uterine contraction detection it was 89.5% (95% CI, 85.5-93.4).

CONCLUSION

Fetal heart rate and uterine contraction monitoring at 340/7 to 366/7 weeks using abdominal surface electrodes was not inferior to Doppler ultrasound/tocodynamometry for fetal-maternal assessment.

REGISTRATION

clinicaltrials.gov/February 20, 2017/identifier NCT03057275.

KEY POINTS

· Monitoring the preterm fetal heart rate with surface electrodes is feasible.. · Preterm contractions can be monitored with surface electrodes.. · The technique was noninferior to standard external monitors..

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.

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.

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.

Uterine slow wave: directionality and changes with imminent delivery

OBJECTIVE

The slow wave (SW) of the electrohysterogram (EHG) may contain relevant information on the electrophysiological condition of the uterus throughout pregnancy and labor. Our aim was to assess differences in the SW as regards the imminence of labor and the directionality of uterine myoelectrical activity.

APPROACH

The SW of the EHG was extracted from the signals of the Icelandic 16-electrode EHG database in the bandwidth [5, 30] mHz and its power, spectral content, complexity and synchronization between the horizontal (X) and vertical (Y) directions were characterized by the root mean square (RMS), dominant frequency (domF), sample entropy (SampEn) and maximum cross-correlation (CCmax) of the signals, respectively. Significant differences between parameters at time-to-delivery (TTD) ≤7 vs. >7 days and between the horizontal vs. vertical directions were assessed.

MAIN RESULTS

The SW power significantly increased in both directions as labor approached (TTD≤7d vs. >7d (mean±SD): x= 0.12±0.10 vs. 0.08±0.06mV; y= 0.12±0.09 vs. 0.08±0.05mV), as well as the dominant frequency in the horizontal direction (= 9.1±1.3 vs. 8.5±1.2mHz) and the synchronization between both directions (= 0.44±0.16 vs. 0.36±0.14). Furthermore, its complexity decreased in the vertical direction (= 6.13·10-2±8.7·10-3 vs. 6.50·10-2±8.3·10-3), suggesting a higher cell-to-cell electrical coupling. Whereas there were no differences between the SW features in both directions in the general population, statistically significant differences were obtained between them in individuals in many cases.

SIGNIFICANCE

Our results suggest that the SW of the EHG is related to bioelectrical events in the uterus and could provide objective information to clinicians in challenging obstetric scenarios.

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.

Wanting to be 'with woman', not with machine: Midwives' experiences of caring for women being continuously monitored in labour

PROBLEM

Some continuous electronic fetal monitoring (CEFM) devices restrict women's bodily autonomy by limiting their mobility in labour and birth.

BACKGROUND

Little is known about how midwives perceive the impact of CEFM technologies on their practice.

AIM

This paper explores the way different fetal monitoring technologies influence the work of midwives.

METHODS

Wireless and beltless 'non-invasive fetal electrocardiogram' (NIFECG) was trialled on 110 labouring women in an Australian maternity hospital. A focus group pertaining to midwives' experiences of using CTG was conducted prior to the trial. After the trial, midwives were asked about their experiences of using NIFECG. All data were analysed using thematic analysis.

FINDINGS

Midwives felt that wired CTG creates barriers to physiological processes. Whilst wireless CTG enables greater freedom of movement for women, it requires constant 'fiddling' from midwives, drawing their attention away from the woman. Midwives felt the NIFECG better enabled them to be 'with woman'.

DISCUSSION

Midwives play a pivotal role in mediating the influence of CEFM on women's experiences in labour. Exploring the way in which different forms of CEFM impact on midwives' practice may assist us to better understand how to prioritise the woman in order to facilitate safe and satisfying birth experiences.

CONCLUSION

The presence of CEFM technology in the birth space impacts midwives' ways of working and their capacity to be woman-centred. Current CTG technology may impede midwives' capacity to be 'with woman'. Compared to the CTG, the NIFECG has the potential to enable midwives to provide more woman-centred care for those experiencing complex pregnancies.

Clinical evaluation of electrohysterography as method of monitoring uterine contractions during labor: A propensity score matched study

OBJECTIVE

Electrohysterography is a non-invasive technique to monitor uterine activity and has a significantly higher sensitivity compared to conventional external tocodynamometry. Whether this technique could lead to improved obstetrical outcomes is still unknown. In this propensity score matched study, clinical results of the first pilot implementing electrohysterography during labor were evaluated. The hypothesis tested is that electrohysterography will help to optimize uterine activity and thereby lead to fewer obstetric interventions. Secondary outcomes were Apgar score, arterial umbilical pH values, first stage labor duration, episiotomy rate and postpartum vaginal blood loss.

STUDY DESIGN

From November 2017 until October 2018, electrohysterography was introduced as a standard alternative for monitoring uterine activity in high-risk deliveries. It could be applied in case of induced labor, previous cesarean delivery, body mass index ≥30 kg/m² or an inadequate external tocodynamometry monitoring. Outcomes were compared to a matched group of women in which external tocodynamometry was applied for uterine activity monitoring during labor. These women were identified using propensity scores.

RESULTS

A total of 348 women received electrohysterography as standard method of uterine monitoring during labor. A match (1:1 ratio) was found for 317 women, resulting in a total population of 634 women. No significant differences were seen in obstetric interventions (i.e. cesarean deliveries and assisted vaginal deliveries) between the electrohysterography and tocodynamometry group (P = 0.80). No statistically significant differences were seen regarding the secondary outcomes.

CONCLUSIONS

This first pilot study implementing electrohysterography as monitoring method during labor in a high-risk population did not result in statistically significant differences regarding obstetric interventions, low Apgar scores or low umbilical artery pH values. Therefore, we suggest that electrohysterography causes no harm and we recommend further implementation and evaluation in clinical practice.

Differentiation of fluctuations in uterine contractions associated with Term pregnancies using adaptive fractal features of electromyography signals

Analysis of uterine contractions using electromyography signals is gaining importance due to its capability to measure the dynamics of uterus. Uterine electromyography (uEMG) provides information on the nature of uterine contractions non-invasively. In this study, the fluctuations in uEMG signals associated with Term pregnancies are analyzed. For this, Term uEMG signals corresponding to second (T1) and third (T2) trimesters are considered. The signals are subjected to Adaptive Fractal Analysis (AFA), wherein a global trend is obtained by using overlapping windows of three orders namely, 25%, 50% and 75%. The signals are detrended and the fluctuation function is estimated. Two Hurst exponent features computed at short range (Hs) and long range (Hl) are extracted and statistically analyzed. Results show that AFA is able to characterize variations in the fluctuations of Term delivery signals. The feature values are observed to vary significantly during different weeks of gestation. It is found that features of T2 signals are higher than that of T1 signals for all the considered overlaps, indicating that T2 signals possess smoother characteristics than T1 signals. Further, coefficient of variation is observed to be low, indicating that these features are able to handle the inter-subject variations in Term signals. Therefore, it appears that the proposed approach could aid in investigation of progressive changes in uterine contractions during Term pregnancies.

Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate

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

M-mode Ultrasound Scan as a Potential Alternative Technique for Monitoring Uterine Contractions in Obese Patients

We investigate motion mode (M-mode) ultrasound scan as a potential non-invasive uterine monitoring technique and compare its contraction characteristics with external tocodynamometry (TOCO). This prospective diagnostic accuracy study included 39 term pregnant woman in active spontaneous labor. M-mode and TOCO were simultaneously performed and uterine contraction characteristics and consistency were compared quantitatively and visually. The results identified a 71.5% ± 35.3% uterine wall thickening during uterine contractions on M-mode. Uterine monitoring with M-mode had a consistency rate of 88.7% ± 6.9% with conventional TOCO method. During 20-min monitoring, the number of detected contractions was significantly higher (p < 0.001) in M-mode (8.2 ± 1.2) than TOCO (7.4 ± 1.5). As for the mean value of the duration of a contraction (seconds), it was significantly shorter (p < 0.001) in M-mode (38.5 ± 3.5) than TOCO (49.2 ± 4.1). For M-mode, the number of detected contractions had a negative but insignificant correlation with the body mass index (BMI) (r = - 0.25 [- 0.52, 0.07], p = 0.127) and the subcutaneous tissue thickness (STT) (r = - 0.21 [- 0.49, 0.11], p = 0.200). As for TOCO, the contractions had a negative and significant correlation with BMI (r = - 0.41 [- 0.64, - 0.11], p = 0.009) and negative and insignificant correlation with STT (r = - 0.26 [- 0.54, 0.06], p = 0.104). The evidence suggests that contraction detection with M-mode is a promising non-invasive technique for uterine monitoring. The preliminary analysis finds that contraction detection is not affected by BMI or STT. With future sensitivity studies, and improvements in image-processing and software technologies, the proposed technique promises to be a viable alternative to existing techniques, especially for obese patients.

Intrauterine versus external tocodynamometry in monitoring labour: a randomised controlled clinical trial

OBJECTIVE

To investigate whether the use of intrauterine tocodynamometry versus external tocodynamometry (IT versus ET) during labour reduces operative deliveries and improves newborn outcome. As IT provides more accurate information on labour contractions, the hypothesis was that it may more appropriately guide oxytocin use than ET.

DESIGN

Randomised controlled trial.

SETTING

Two labour wards, in a university tertiary hospital and a central hospital.

POPULATION

A total of 1504 parturients with singleton pregnancies, gestational age ≥37 weeks and fetus in cephalic position: 269 women with uterine scars, 889 nulliparas and 346 parous women with oxytocin augmentation.

METHODS

Participants underwent IT (n = 736) or ET (n = 768) during the active first stage of labour.

MAIN OUTCOME MEASURES

Primary outcome: rate of operative deliveries.

SECONDARY OUTCOMES

duration of labour, amount of oxytocin given, adverse neonatal outcomes.

RESULTS

Operative delivery rates were 26.9% (IT) and 25.9% (ET) (odds ratio 1.05, 95% CI 0.84-1.32, P = 0.663). The ET to IT conversion rate was 31%. We found no differences in secondary outcomes (IT versus ET). IT reduced oxytocin use during labours with signs of fetal distress, and trial of labour after caesarean section.

CONCLUSIONS

IT did not reduce the rate of operative deliveries, use of oxytocin, or adverse neonatal outcomes, and it did not shorten labour duration.

TWEETABLE ABSTRACT

IT (versus ET) reduced oxytocin use in high-risk labours but did not influence operative delivery rate or adverse neonatal outcomes.

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Background

Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN).

Materials and Methods

In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s.

Results

The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak.

Conclusion

The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN.

Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram

Uterine contraction (UC) activity is commonly used to monitor the approach of labour and delivery. Electrohysterograms (EHGs) have recently been used to monitor UC and distinguish between efficient and inefficient contractions. In this study, we aimed to identify UC in EHG signals using a convolutional neural network (CNN). An open-access database (Icelandic 16-electrode EHG database from 45 pregnant women with 122 recordings, DB1) was used to develop a CNN model, and 14000 segments with a length of 45 s (7000 from UCs and 7000 from non-UCs, which were determined with reference to the simultaneously recorded tocography signals) were manually extracted from the 122 EHG recordings. Five-fold cross-validation was applied to evaluate the ability of the CNN to identify UC based on its sensitivity (SE), specificity (SP), accuracy (ACC), and area under the receiver operating characteristic curve (AUC). The CNN model developed using DB1 was then applied to an independent clinical database (DB2) to further test its generalisation for recognizing UCs. The EHG signals in DB2 were recorded from 20 pregnant women using our multi-channel system, and 308 segments (154 from UCs and 154 from non-UCs) were extracted. The CNN model from five-fold cross-validation achieved average SE, SP, ACC, and AUC of 0.87, 0.98, 0.93, and 0.92 for DB1, and 0.88, 0.97, 0.93, and 0.87 for DB2, respectively. In summary, we demonstrated that CNN could effectively identify UCs using EHG signals and could be used as a tool for monitoring maternal and foetal health.

Potential use of electrohysterography in obstetrics: a review article

Monitoring the uterine contraction during pregnancy is necessary to monitor labor progress, fetal and maternal well-being, and uterine activity. The aim of this review was to evaluate the performance of electrohysterography and to analyze the nature of uterine contraction. A search was undertaken using PubMed, Embase, and ClinicalTrials.gov database from 1 January 1950 to 1 November 2018. Search terms include: "Uterine" or "Uterus" or "Labor" or "Labour" and "electrical activity" or "electrohysterogram" or "electrohysterograph". Reviewing the literature, electrohysterography showed a higher sensitivity for uterine contraction detection and was independent of body mass index, abdominal wall thickness, or maternal position enabling monitoring obese patients as well. Electrohysterography can enhance uterine monitoring throughout labor because of its noninvasiveness, adhesive properties, and reduced obesity sensitiveness. Electrohysterography should be available to safely improve intrapartum monitoring instead of the invasive intrauterine pressure catheter.