Feasibility of antenatal ambulatory fetal electrocardiography: a systematic review

BACKGROUND

Antenatal fetal heart rate (FHR) monitoring is currently limited by hospital-based accessibility as well as the availability of relevant equipment and expertise required to position device electrodes. Ambulatory FHR monitoring in the form of noninvasive fetal electrocardiography (NIFECG) is currently an area of research interest, particularly during the era of the COVID-19 pandemic, and the potential to improve maternity care and reduce hospital attendances need to be evaluated.

OBJECTIVES

To assess the feasibility, acceptability, and signal success of ambulatory NIFECG monitoring and identify research areas required to facilitate clinical utilization of this method of monitoring.

METHODS

Medline, EMBASE, and PubMed databases were searched from January 2005 to April 2021 using terms relevant to antenatal ambulatory or home NIFECG. The search was compliant with PRISMA guidelines, and was registered with the PROSPERO database (CRD42020195809). All studies reporting the clinical utilization of NIFECG inclusive of its use in the ambulatory setting performed in the antenatal period, human studies, and those in the English language were included. Those reporting novel technological methods and electrophysiological algorithms, satisfaction surveys, intrapartum studies, case reports and reviews, and animal studies were excluded. Study screening and data extraction were conducted in duplicate. Risk of bias was appraised using the Modified Downs and Black tool. Due to the heterogeneity of the reported findings, a meta-analysis was not feasible.

RESULTS

The search identified 193 citations, where 11 studies were deemed eligible for inclusion. All studies used a single NIFECG system with a duration of monitoring ranging from 5.6 to 21.4 h. Predefined signal acceptance threshold ranged from 34.0-80.0%. Signal success in the study populations was 48.6-95.0% and was not affected by maternal BMI. Good signals were achieved in the 2nd trimester, but less so in the early 3rd trimester. NIFECG was a well-accepted method of FHR monitoring, with up to 90.0% of women's satisfaction levels when worn during outpatient induction of labor. Placement of the acquisition device needed input from healthcare staff in every report.

CONCLUSIONS

Although there is evidence for the clinical feasibility of ambulatory NIFECG, the disparity in the literature limits the ability to draw firm conclusions. Further studies to establish repeatability and device validity, whilst developing standardized FHR parameters and set evidence-based standards for signal success for NIFECG are required to ascertain the clinical benefit and potential limitations of ambulatory outpatient FHR monitoring.

Prediction of fetal RR intervals from maternal factors using machine learning models

Previous literature has highlighted the importance of maternal behavior during the prenatal period for the upbringing of healthy adults. During pregnancy, fetal health assessments are mainly carried out non-invasively by monitoring fetal growth and heart rate (HR) or RR interval (RRI). Despite this, research entailing prediction of fHRs from mHRs is scarce mainly due to the difficulty in non-invasive measurements of fetal electrocardiogram (fECG). Also, so far, it is unknown how mHRs are associated with fHR over the short term. In this study, we used two machine learning models, support vector regression (SVR) and random forest (RF), for predicting average fetal RRI (fRRI). The predicted fRRI values were compared with actual fRRI values calculated from non-invasive fECG. fRRI was predicted from 13 maternal features that consisted of age, weight, and non-invasive ECG-derived parameters that included HR variability (HRV) and R wave amplitude variability. 156 records were used for training the models and the results showed that the SVR model outperformed the RF model with a root mean square error (RMSE) of 29 ms and an average error percentage (< 5%). Correlation analysis between predicted and actual fRRI values showed that the Spearman coefficient for the SVR and RF models were 0.31 (P < 0.001) and 0.19 (P < 0.05), respectively. The SVR model was further used to predict fRRI of 14 subjects who were not included in the training. The latter prediction results showed that individual error percentages were (≤ 5%) except in 3 subjects. The results of this study show that maternal factors can be potentially used for the assessment of fetal well-being based on fetal HR or RRI.

Uterine activity modifies the response of the fetal autonomic nervous system at preterm active labor

Background

The autonomic nervous system of preterm fetuses has a different level of maturity than term fetuses. Thus, their autonomic response to transient hypoxemia caused by uterine contractions in labor may differ. This study aims to compare the behavior of the fetal autonomic response to uterine contractions between preterm and term active labor using a novel time-frequency analysis of fetal heart rate variability (FHRV).

Methods

We performed a case-control study using fetal R-R and uterine activity time series obtained by abdominal electrical recordings from 18 women in active preterm labor (32-36 weeks of gestation) and 19 in active term labor (39-40 weeks of gestation). We analyzed 20 minutes of the fetal R-R time series by applying a Continuous Wavelet Transform (CWT) to obtain frequency (HF, 0.2-1 Hz; LF, 0.05-0.2 Hz) and time-frequency (Flux0, Flux90, and Flux45) domain features. Time domain FHRV features (SDNN, RMSSD, meanNN) were also calculated. In addition, ultra-short FHRV analysis was performed by segmenting the fetal R-R time series according to episodes of the uterine contraction and quiescent periods.

Results

No significant differences between preterm and term labor were found for FHRV features when calculated over 20 minutes. However, we found significant differences when segmenting between uterine contraction and quiescent periods. In the preterm group, the LF, Flux0, and Flux45 were higher during the average contraction episode compared with the average quiescent period (p<0.01), while in term fetuses, vagally mediated FHRV features (HF and RMSSD) were higher during the average contraction episode (p<0.05). The meanNN was lower during the strongest contraction in preterm fetuses compared to their consecutive quiescent period (p=0.008).

Conclusion

The average autonomic response to contractions in preterm fetuses shows sympathetic predominance, while term fetuses respond through parasympathetic activity. Comparison between groups during the strongest contraction showed a diminished fetal autonomic response in the preterm group. Thus, separating contraction and quiescent periods during labor allows for identifying differences in the autonomic nervous system cardiac regulation between preterm and term fetuses.

Cardiotocography analysis by empirical dynamic modeling and Gaussian processes

Introduction: During labor, fetal heart rate (FHR) and uterine activity (UA) can be continuously monitored using Cardiotocography (CTG). This is the most widely adopted approach for electronic fetal monitoring in hospitals. Both FHR and UA recordings are evaluated by obstetricians for assessing fetal well-being. Due to the complex and noisy nature of these recordings, the evaluation by obstetricians suffers from high interobserver and intraobserver variability. Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. Methods: Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. In this paper, we propose to model intrapartum CTG recordings from a dynamical system perspective using empirical dynamic modeling with Gaussian processes, which is a Bayesian nonparametric approach for estimation of functions. Results and Discussion: In the context of our paper, Gaussian processes are capable for simultaneous estimation of the dimensionality of attractor manifolds and reconstructing of attractor manifolds from time series data. This capacity of Gaussian processes allows for revealing causal relationships between the studied time series. Experimental results on real CTG recordings show that FHR and UA signals are causally related. More importantly, this causal relationship and estimated attractor manifolds can be exploited for several important applications in computerized analysis of CTG recordings including estimating missing FHR samples, recovering burst errors in FHR tracings and characterizing the interactions between FHR and UA signals.

Change in T2* measurements of placenta and fetal organs during Braxton Hicks contractions

Maternal-placental perfusion can be temporarily compromised by Braxton Hicks (BH) uterine contractions. Although prior studies have employed T2* changes to investigate the effect of BH contractions on placental oxygen, the effect of these contractions on the fetus has not been fully characterized. We investigated the effect of BH contractions on quantitative fetal organ T2* across gestation together with the birth information. We observed a slight but significant decrease in fetal brain and liver T2* during contractions.

Can 17 hydroxyprogesterone caproate (17P) decrease preterm deliveries in patients with a history of PMC or pPROM?

BACKGROUND

A history of spontaneous preterm birth (sPTB) is a significant risk factor for recurrence. Intra-muscular-7α-hydroxyprogesterone caproate (17P) has been the preventive treatment of choice until the recent "Prolong study" that reported no benefit.

OBJECTIVE

To determine the benefit of (17P) treatment in preventing reoccurrence of sPTB, by evaluating two presenting symptoms of the first sPTB: premature contractions (PMC) and preterm premature rupture of membranes (pPROM).

STUDY DESIGN

This retrospective study included 342 women with a previous singleton sPTB followed by a subsequent pregnancy. sPTB were either due to PMC (n = 145) or pPROM (n = 197). During the subsequent pregnancy, 90 (26.3%) patients received 250 mg 17P IM. Each presenting symptom-PMC or pPROM-was evaluated within itself comparing treated vs. untreated groups. Data were analyzed using t-test, Chi-square and Fisher's exact test. Logistic regression analysis was also performed.

RESULTS

Patients treated with 17P in the subsequent pregnancy had delivered earlier in the previous pregnancy (33.4w vs. 35.3w in the PMC group, and 34.1w vs. 35.7w in the pPROM group, p<0.001). In the following pregnancy, they had higher admission rates due to suspected preterm labor (31.7% vs. 10.9% in the treated vs. untreated PMC group (p = 0.003) and 26.1% vs. 5.4% in the treated vs. untreated pPROM group (p<0.001). In both groups, but more prominently in the previous PMC group, treatment compared to non-treatment in the subsequent pregnancy significantly prolonged it (4.3w vs. 2.6w in the PMC group (p = 0.007), and 3.7w vs. 2.7w in the pPROM group (p = 0.018)). The presenting symptom of sPTB in the following pregnancy tended to recur in cases of another sPTB, with a significantly greater likelihood of repeating the sPTB mechanism in cases with PMC, regardless of receiving 17P (69% in the PMC cohort and 60% in the pPROM cohort, p<0.001).

CONCLUSIONS

17P might delay preterm delivery in patients with a previous sPTB on an individual level (prolongation of the pregnancy for each patient compared to her previous delivery). Therefore, our results imply that 17P can decrease potential premature delivery complications for patients with a previous sPTB due to PMC or pPROM.

IDENTIFICATION OF UTERINE CONTRACTIONS BY AN ENSEMBLE OF GAUSSIAN PROCESSES

Identifying uterine contractions with the aid of machine learning methods is necessary vis-á-vis their use in combination with fetal heart rates and other clinical data for the assessment of a fetus wellbeing. In this paper, we study contraction identification by processing noisy signals due to uterine activities. We propose a complete four-step method where we address the imbalanced classification problem with an ensemble Gaussian process classifier, where the Gaussian process latent variable model is used as a decision-maker. The results of both simulation and real data show promising performance compared to existing methods.

Fetal heart rate during maternal sleep

Despite prolonged and cumulative exposure during gestation, little is known about the fetal response to maternal sleep. Eighty-four pregnant women with obesity (based on pre-pregnancy BMI) participated in laboratory-based polysomnography (PSG) with continuous fetal electrocardiogram monitoring at 36 weeks gestation. Multilevel modeling revealed both correspondence and lack of it in maternal and fetal heart rate patterns. Fetal heart rate (fHR) and variability (fHRV), and maternal heart rate (mHR) and variability (mHRV), all declined during the night, with steeper rates of decline prior to 01:00. fHR declined upon maternal sleep onset but was not otherwise associated with maternal sleep stage; fHRV differed during maternal REM and NREM. There was frequent maternal waking after sleep onset (WASO) and fHRV and mHRV were elevated during these episodes. Cross-correlation analyses revealed little temporal coupling between maternal and fetal heart rate, except during WASO, suggesting that any observed associations in maternal and fetal heart rates during sleep are the result of other physiological processes. Implications of the maternal sleep context for the developing fetus are discussed, including the potential consequences of the typical sleep fragmentation that accompanies pregnancy.

A systematic scoping review to identify the design and assess the performance of devices for antenatal continuous fetal monitoring

BACKGROUND

Antepartum fetal monitoring aims to assess fetal development and wellbeing throughout pregnancy. Current methods utilised in clinical practice are intermittent and only provide a 'snapshot' of fetal wellbeing, thus key signs of fetal demise could be missed. Continuous fetal monitoring (CFM) offers the potential to alleviate these issues by providing an objective and longitudinal overview of fetal status. Various CFM devices exist within literature; this review planned to provide a systematic overview of these devices, and specifically aimed to map the devices' design, performance and factors which affect this, whilst determining any gaps in development.

METHODS

A systematic search was conducted using MEDLINE, EMBASE, CINAHL, EMCARE, BNI, Cochrane Library, Web of Science and Pubmed databases. Following the deletion of duplicates, the articles' titles and abstracts were screened and suitable papers underwent a full-text assessment prior to inclusion in the review by two independent assessors.

RESULTS

The literature searches generated 4,885 hits from which 43 studies were included in the review. Twenty-four different devices were identified utilising four suitable CFM technologies: fetal electrocardiography, fetal phonocardiography, accelerometry and fetal vectorcardiography. The devices adopted various designs and signal processing methods. There was no common means of device performance assessment between different devices, which limited comparison. The device performance of fetal electrocardiography was reduced between 28 to 36 weeks' gestation and during high levels of maternal movement, and increased during night-time rest. Other factors, including maternal body mass index, fetal position, recording location, uterine activity, amniotic fluid index, number of fetuses and smoking status, as well as factors which affected alternative technologies had equivocal effects and require further investigation.

CONCLUSIONS

A variety of CFM devices have been developed, however no specific approach or design appears to be advantageous due to high levels of inter-device and intra-device variability.

Characterization of Uterine Motion in Early Gestation Using MRI-Based Motion Tracking

Magnetic resonance imaging (MRI) is a promising non-invasive imaging technique that can be safely used to study placental development and function. However, studies of the human placenta performed by MRI are limited by uterine motion and motion in the uterus during MRI remains one of the major limiting factors. Here, we aimed to investigate the characterization of uterine activity during MRI in the second trimester of pregnancy using MRI-based motion tracking. In total, 46 pregnant women were scanned twice (first scan between 14 and 18 weeks and second scan between 19 and 24 weeks), and 20 pregnant subjects underwent a single MRI between 14 and 18 weeks GA, resulting in 112 MRI scans. An MRI-based algorithm was used to track uterine motion in the superior-inferior and left-right directions. Uterine contraction and maternal motion cases were separated by the experts, and unpaired Wilcoxon tests were performed within the groups of gestational age (GA), fetal sex, and placental location in terms of the overall intensity measures of the uterine activity. In total, 22.3% of cases had uterine contraction during MRI, which increased from 18.6% at 14–18 weeks to 26.4% at 19–24 weeks GA. The dominant direction of the uterine contraction and maternal motion was the superior to the inferior direction during early gestation.

Proportion and Associated Factors of Nonreassuring Fetal Heart Rate Patterns in Finote Selam Primary Hospital, North West Ethiopia

Introduction

Nonreassuring fetal heart rate patterns (NRFHRP) suggest fetal conciliation or a deteriorating ability to handle the stress of labor. Nearly half of stillbirths occurring worldwide are due to hypoxia which is primarily manifested by NRFHRP. Hence, this study assessed the proportion and associated factors of NRFHRP in the Finote Selam primary hospital, North West Ethiopia.

Methods

An institution-based retrospective cross-sectional study was conducted from March 1 to April 1, 2019, on 364 charts of mothers who gave birth from January 2017 to January 2018 at the Finote Selam primary hospital. A computer-based simple random sampling technique was used to select charts. A secondary data was collected using a structured questionnaire adapted from different literatures. The data was entered and analyzed using Epi Info version 7 and Statistical Package for the Social Sciences (SPSS) version 23.0. Binary logistic regression was executed, and all explanatory variables with p value < 0.2 were entered into multivariable logistic regressions. Multivariable logistic regression was used to control the effect of confounding variables and to identify factors affecting NRFHRP. Odds ratios with 95% confidence intervals were computed, and statistical significance was declared if p < 0.05.

Result

Out of 364 total deliveries, NRFHRP was detected on 55 (15.1%) fetuses, and the commonest NRFHRP detected was bradycardia 44 (80%). Most NRFHRP (38.18%) occurred on the deceleration phase of labor. There was no identified possible cause for NRFHRP on 34.5% of cases. Referral from nearby health institutions [AOR = 2.832 (95% CI 1.457, 5.503)], primigravida [AOR = 2.722 (95% CI 1.377, 5.381)], augmentation of labor [AOR = 3.664 (95% CI 1.782, 7.534)], and meconium-stained amniotic fluid [AOR = 6.491 (95% CI 3.198, 13.173)] were significantly associated with NRFHRP.

Conclusion

The proportion of NRFHRP is high. Referral from nearby health institutions, primigravida mothers, augmentation of labor, and meconium-stained amniotic fluid were significantly associated with NRFHRP. Implementing a better referral link and close monitoring during follow-up could minimize NHFHRP.

Analysis of the fetal cardio-electrohysterographic coupling at the third trimester of gestation in healthy women by Bivariate Phase-Rectified Signal Averaging

INTRODUCTION

The fetal cardio-electrohysterographic coupling (FCEC) is defined as the influence of the uterine electrical activity on fetal heart rate. FCEC has been mainly evaluated by visual analysis of cardiotocographic data during labor; however, this physiological phenomenon is poorly explored during the antenatal period. Here we propose an approach known as Bivariate Phase-Rectified Signal Averaging analysis (BPRSA) to assess such FCEC in the late third trimester of low-risk pregnancies. We hypothesized that BPRSA is a more reliable measure of FCEC than visual analysis and conventional measures such as cross-correlation, coherence, and cross-sample entropy. Additionally, by using BPRSA it is possible to detect FCEC even from the third trimester of pregnancy.

MATERIAL AND METHODS

Healthy pregnant women in the last third trimester of pregnancy (36.6 ± 1.8 gestational weeks) without any clinical manifestation of labor were enrolled in the Maternal and Childhood Research Center (CIMIGen), Mexico City (n = 37). Ten minutes of maternal electrohysterogram (EHG) and fetal heart rate (FHR) data were collected by a transabdominal non-invasive device. The FCEC was quantified by the coefficient of coherence, the maximum normalized cross-correlation, and the cross-sample entropy obtained either from the EHG and FHR raw signals or from the corresponding BPRSA graphs.

RESULTS

We found that by using BPRSA, the FCEC was detected in 92% cases (34/37) compared to 48% cases (18/37) using the coefficient of coherence between the EHG and FHR raw signals. Also, BPRSA indicated FCEC in 82% cases (30/37) compared to 30% cases (11/37) using the maximum normalized cross-correlation. By comparing the analyses, the BPRSA evidenced higher FCEC in comparison to the coupling estimated from the raw EHG and FHR signals.

CONCLUSIONS

Our results support the consideration that in the third trimester of pregnancy, the fetal heart rate is also influenced by uterine activity despite the emerging manifestation of this activity before labor. To quantify FCEC, the BPRSA can be applied to FHR and EHG transabdominal signals acquired in the third trimester of pregnancy.

DISCOVERING CAUSALITIES FROM CARDIOTOCOGRAPHY SIGNALS USING IMPROVED CONVERGENT CROSS MAPPING WITH GAUSSIAN PROCESSES

Convergent cross mapping (CCM) is designed for causal discovery in coupled time series, where Granger causality may not be applicable because of a separability assumption. However, CCM is not robust to observation noise which limits its applicability on signals that are known to be noisy. Moreover, the parameters for state space reconstruction need to be selected using grid search methods. In this paper, we propose a novel improved version of CCM using Gaussian processes for discovery of causality from noisy time series. Specifically, we adopt the concept of CCM and carry out the key steps using Gaussian processes within a non-parametric Bayesian probabilistic framework in a principled manner. The proposed approach is first validated on simulated data, and then used for understanding the interaction between fetal heart rate and uterine activity in the last two hours before delivery and of interest in obstetrics. Our results indicate that uterine activity affects the fetal heart rate, which agrees with recent clinical studies.

Clinical management of the pregnant patient undergoing non-obstetric surgery: Review of guidelines

The management principles of non-obstetric surgery during pregnancy are important concepts for all health care providers to be cognizant of. The goals of non-obstetric surgery are to ensure maternal safety, maintain the pregnancy, and ensure fetal well-being. In this regard, organogenesis occurs roughly between days 7-57 and thus, certain medications have a higher incidence of fetal teratogenicity in this first trimester. Some examples of common surgeries performed urgently or emergently include appendectomies, ovarian detorsions, bowel obstruction, trauma, and cholecystectomies. The choice of anesthetic technique and the selection of appropriate anesthetic drugs should be guided by indication for surgery, the nature of the surgery, and the site of the surgical procedure. Many of the concerns for any patients undergoing urgent or emergent surgery must be considered by anesthesia providers along with steps to ensure the fetus has the best outcome.

Detecting Causality using Deep Gaussian Processes

Convergent cross mapping (CCM) is a state space reconstruction (SSR)-based method designed for causal discovery in coupled time series, where Granger causality may not be applicable due to a separability assumption. However, CCM requires a large number of observations and is not robust to observation noise which limits its applicability. Moreover, in CCM and its variants, the SSR step is mostly implemented with delay embedding where the parameters for reconstruction usually need to be selected using grid search-based methods. In this paper, we propose a Bayesian version of CCM using deep Gaussian processes (DGPs), which are naturally connected with deep neural networks. In particular, we adopt the framework of SSR-based causal discovery and carry out the key steps using DGPs within a non-parametric Bayesian probabilistic framework in a principled manner. The proposed approach is first validated on simulated data and then tested on data used in obstetrics for monitoring the well-being of fetuses, i.e., fetal heart rate (FHR) and uterine activity (UA) signals in the last two hours before delivery. Our results indicate that UA affects the FHR, which agrees with recent clinical studies.

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.

INFERENCE ABOUT CAUSALITY FROM CARDIOTOCOGRAPHY SIGNALS USING GAUSSIAN PROCESSES

In this paper, we propose a novel and simple method for discovery of Granger causality from noisy time series using Gaussian processes. More specifically, we adopt the concept of Granger causality, but instead of using autoregressive models for establishing it, we work with Gaussian processes. We show that information about the Granger causality is encoded in the hyper-parameters of the used Gaussian processes. The proposed approach is first validated on simulated data, and then used for understanding the interaction between fetal heart rate and uterine activity in the last two hours before delivery and of interest in obstetrics. Our results indicate that uterine activity affects fetal heart rate, which agrees with recent clinical studies.

The fetal circadian rhythm in pregnancies complicated by pregestational diabetes is altered by maternal glycemic control and the morning cortisol concentration

Circadian rhythmicity is fundamental to human physiology, and is present even during fetal life in normal pregnancies. The impact of maternal endocrine disease on the fetal circadian rhythm is not well understood. The present study aimed to determine the fetal circadian rhythm in pregnancies complicated by pregestational diabetes mellitus (PGDM), compare it with a low-risk reference population, and identify the effects of maternal glycemic control and morning cortisol concentrations. Long-term fetal electrocardiogram recordings were made in 40 women with PGDM at 28 and 36 weeks of gestation. Two recordings were made in 18 of the women (45.0%) and one recording was made in 22 (55.0%). The mean fetal heart rate (fHR) and the fHR variation (root mean square of squared differences) were extracted in 1-min epochs, and circadian rhythmicity was detected by cosinor analysis. The study cohort was divided based on HbA1c levels and morning cortisol concentrations. Statistically, significant circadian rhythms in the fHR and the fHR variation were found in 45 (100%) and 44 (95.7%) of the 45 acceptable PGDM recordings, respectively. The rhythms were similar to those of the reference population. However, there was no statistically significant population-mean rhythm in the fHR among PGDM pregnancies at 36 weeks, indicating an increased interindividual variation. The group with higher HbA1c levels (>6.0%) had no significant population-mean fHR rhythm at 28 or 36 weeks, and no significant fHR-variation rhythm at 36 weeks. Similarly, the group with a lower morning cortisol concentration (≤8.8 µg/dl) had no significant population-mean fHR-variation rhythm at 28 and 36 weeks. These findings indicate that individual fetal rhythmicity is present in pregnancies complicated by PGDM. However, suboptimal maternal glycemic control and a lower maternal morning cortisol concentration are associated with a less-well-synchronized circadian system of the fetus.

Maternal exercise, season and sex modify the daily fetal heart rate rhythm

AIM

The knowledge on biological rhythms is rapidly expanding. We aimed to define the longitudinal development of the daily (24-hour) fetal heart rate rhythm in an unrestricted, out-of-hospital setting and to examine the effects of maternal physical activity, season and fetal sex.

METHODS

We recruited 48 women with low-risk singleton pregnancies. Using a portable monitor for continuous fetal electrocardiography, fetal heart rate recordings were obtained around gestational weeks 24, 28, 32 and 36. Daily rhythms in fetal heart rate and fetal heart rate variation were detected by cosinor analysis; developmental trends were calculated by population-mean cosinor and multilevel analysis.

RESULTS

For the fetal heart rate and fetal heart rate variation, a significant daily rhythm was present in 122/123 (99.2%) and 116/121 (95.9%) of the individual recordings respectively. The rhythms were best described by combining cosine waves with periods of 24 and 8 hours. With increasing gestational age, the magnitude of the fetal heart rate rhythm increased, and the peak of the fetal heart rate variation rhythm shifted from a mean of 14:25 (24 weeks) to 20:52 (36 weeks). With advancing gestation, the rhythm-adjusted mean value of the fetal heart rate decreased linearly in females (P < .001) and nonlinearly in males (quadratic function, P = .001). At 32 and 36 weeks, interindividual rhythm diversity was found in male fetuses during higher maternal physical activity and during the summer season.

CONCLUSION

The dynamic development of the daily fetal heart rate rhythm during the second half of pregnancy is modified by fetal sex, maternal physical activity and season.

A mixed-methods evaluation of continuous electronic fetal monitoring for an extended period

INTRODUCTION

Continuous fetal monitoring is used to objectively record the fetal heart rate and fetal activity over an extended period of time; however, its feasibility and acceptability to women is currently unknown. The study addressed the hypothesis that continuous fetal monitoring is feasible and acceptable to pregnant women.

MATERIAL AND METHODS

Pregnant participants (n = 22) were monitored using a continuous fetal electrocardiography device, the Monica AN24. Signal quality, duration of recording and cardiotocography findings were correlated with maternal and fetal factors. Participants' change in anxiety before and after monitoring was assessed using validated questionnaires. Participants' experiences were explored through a questionnaire (n = 20) and semi-structured interview (n = 13).

RESULTS

Recordings were successfully obtained in 19 of the 22 participants (86.3%). The mean recording quality of fetal heart rate was 69.0% (range 17.4%-99.4%) and maternal heart rate was 99.0% (90.9%-100.0%). Recording quality was positively correlated with gestational age (P = 0.05) and negatively correlated with uterine activity and maternal movement (P < 0.001). Overall, participants were satisfied with their experience of continuous fetal monitoring; 30% considered it preferable to intermittent monitoring. Continuous fetal monitoring did not significantly increase maternal anxiety, with a trend towards a reduction in Pregnancy Specific Anxiety score (P = 0.07). Qualitative analysis grouped women's responses into three themes: (a) reassurance and anxiety, (b) the physical device and (c) future developments in continuous fetal monitoring.

CONCLUSIONS

Continuous fetal monitoring is a feasible and acceptable form of monitoring to pregnant women although further practical improvements could be incorporated. Further research is required to assess the ability of continuous fetal monitoring to detect fetal compromise.