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Background Routine assessment in (near) term pregnancy is often inaccurate for the identification of fetuses who are mild to moderately compromised due to placental insufficiency and are at risk of adverse outcomes, especially when fetal size is seemingly within normal range for gestational age. Although biometric measurements and cardiotocography are frequently used, it is known that these techniques have low sensitivity and specificity. In clinical practice this diagnostic uncertainty results in considerable ‘over treatment’ of women with healthy fetuses whilst truly compromised fetuses remain unidentified. The CPR is the ratio of the umbilical artery pulsatility index over the middle cerebral artery pulsatility index. A low CPR reflects fetal redistribution and is thought to be indicative of placental insufficiency independent of actual fetal size, and a marker of adverse outcomes. Its utility as an indicator for delivery in women with reduced fetal movements (RFM) is unknown. The aim of this study is to assess whether expedited delivery of women with RFM identified as high risk on the basis of a low CPR improves neonatal outcomes. Secondary aims include childhood outcomes, maternal obstetric outcomes, and the predictive value of biomarkers for adverse outcomes. Methods International multicentre cluster randomised trial of women with singleton pregnancies with RFM at term, randomised to either an open or concealed arm. Only women with an estimated fetal weight ≥ 10th centile, a fetus in cephalic presentation and normal cardiotocograph are eligible and after informed consent the CPR will be measured. Expedited delivery is recommended in women with a low CPR in the open arm. Women in the concealed arm will not have their CPR results revealed and will receive routine clinical care. The intended sample size based on the primary outcome is 2160 patients. The primary outcome is a composite of: stillbirth, neonatal mortality, Apgar score < 7 at 5 min, cord pH < 7.10, emergency delivery for fetal distress, and severe neonatal morbidity. Discussion The CEPRA trial will identify whether the CPR is a good indicator for delivery in women with perceived reduced fetal movements. Trial registration Dutch trial registry (NTR), trial NL7557 . Registered 25 February 2019.

BackgroundIn 2013, the stillbirth rate in the UK was 4.2 per 1000 live births, ranking 24th out of 49 high-income countries, with an annual rate of reduction of only 1.4% per year. The majority of stillbirths occur in normally formed infants, with (retrospective) evidence of placental insufficiency the most common clinical finding. Maternal perception of reduced fetal movements (RFM) is associated with placental insufficiency and increased risk of subsequent stillbirth.This study will test the hypothesis that the introduction of a package of care to increase women's awareness of the need for prompt reporting of RFM and standardised management to identify fetal compromise with timely delivery in confirmed cases, will reduce the rate of stillbirth. Following the introduction of a similar intervention in Norway the odds of stillbirth fell by 30%, but the efficacy of this intervention (and possible adverse effects and implications for service delivery) has not been tested in a randomised trial.MethodsWe describe a stepped-wedge cluster trial design, in which participating hospitals in the UK and Ireland will be randomised to the timing of introduction of the care package. Outcomes (including the primary outcome of stillbirth) will be derived from detailed routinely collected maternity data, allowing us to robustly test our hypothesis. The degree of implementation of the intervention will be assessed in each site. A nested qualitative study will examine the acceptability of the intervention to women and healthcare providers and identify process issues including barriers to implementation.Ethics and disseminationEthical approval was obtained from the Scotland A Research Ethics Committee (Ref 13/SS/0001) and from Research and Development offices in participating maternity units. The study started in February 2014 and delivery of the intervention completed in December 2016. Results of the study will be submitted for publication in peer-reviewed journals and disseminated to local investigating sites to inform education and care of women presenting with RFM.Trial registration numberwww.clinicaltrials.gov NCT01777022.VersionProtocol Version 4.2, 3 February 2017.

To investigate whether assisted reproductive technology (ART) affects gross fetal movement. A prospective cohort study. 65 women who conceived with ART (ART group) and 211 women (control group) without ART recorded fetal movement with the fetal movement acceleration measurement recorder at night weekly after 28 weeks. The number ratio of 10 s epochs with fetal movement to all epochs was calculated as the fetal movement parameter. When no fetal movement was observed for more than 5 min, it was defined as a no fetal movement period, and the average number per hour, the average duration, and the longest duration of the no fetal movement periods were calculated as the no fetal movement parameters. Gestational weeks were classified into 28–33 and 34–39 weeks, and the fetal movement parameter and the no fetal movement parameters were compared using the Student’s t-test. The fetal movement parameters at 28–33 weeks were 17.43% (ART) and 16.58% (control) (p = 0.219), and those at 34–39 weeks were 11.72% (ART) and 11.96% (control) (p = 0.590). In the same way, for the no fetal movement parameters, the average numbers were 1.58 and 1.63 per hour (p = 0.357), and 2.36 and 2.30 per hour (p = 0.503). The average durations were 8.30 and 8.46 min (p = 0.712), and 9.20 and 9.51 min (p = 0.188). The longest durations were 16.26 and 17.02 min (p = 0.295), and 22.34 and 22.87 min (p = 0.534). ART does not affect gross fetal movement count.

Identifying and understanding prenatal developmental disorders at an early stage are crucial as fetal brain development has long-term effects on an individual’s life. The maturation of the fetal autonomic nervous system (ANS) is believed to influence the coordination and direction of maternal-fetal heartbeat synchronization. Fetal behavioral states (FBSes) include quiet sleep (1F), active sleep (2F), quiet awake (3F), and active awake (4F). In this study, the focus is on fetal movements, leading to the grouping of 1F and 3F into a quiet state, while 2F and 4F are combined to form an active state. Thus, the FBSes discussed in this article consist of fetal quiet and active states. Here, we explore the relationship between FBSes and the coupling of maternal and fetal heartbeats. We also seek to understand how maternal breathing patterns influence this coupling while considering FBSes. The study involved 105 healthy fetuses with gestational ages (GA) from 20 to 40 weeks. Non-invasive electrocardiogram (ECG) signals were recorded for 3 to 10 minutes. The ECG samples were separated into three gestational groups (Early: 16 ≤ GA < 25, Mid: 25 ≤ GA < 32, and Late: 32 ≤ GA < 40 weeks). Maternal respiration rate and coupling strength parameters were calculated for various maternal-fetal heartbeat coupling ratios. The findings of the study indicated that FBSes influenced maternal-fetal HR coupling strength during late gestation but not during early and mid-gestation. The changes in maternal-fetal HR synchronization or communication as gestation progresses occur in both FBSes. Furthermore, we noticed a significantly higher level of maternal-fetal heartbeat synchronization during periods of higher respiratory rates when the fetus was in a quiet state. These results emphasize how FBSes impact the synchronization of maternal-fetal HR and contribute to the understanding of fetal growth and health.

Counting fetal movements is essential for assessing fetal health, but manually recording these movements can be challenging and inconvenient for pregnant women. This study presents a wearable device designed to detect fetal movements across various settings, both within and outside medical facilities. The device integrates accelerometer and gyroscope sensors with Internet of Things (IoT) technology to accurately differentiate between fetal and non-fetal movements. Data were collected from 35 pregnant women at Suranaree University of Technology (SUT) Hospital. This study evaluated ten signal extraction methods, six machine learning algorithms, and four feature selection techniques to enhance classification performance. The device utilized Particle Swarm Optimization (PSO) for feature selection and Extreme Gradient Boosting (XGB) with PSO hyper-tuning. It achieved a sensitivity of 90.00%, precision of 87.46%, and an F1-score of 88.56%, reflecting commendable results. The IoT-enabled technology facilitated continuous monitoring with an average latency of 423.6 ms. It ensured complete data integrity and successful transmission, with the capability to operate continuously for up to 48 h on a single charge. The findings substantiate the efficacy of the proposed approach in detecting fetal movements, thereby demonstrating a practical and valuable technology for fetal movement detection applications.

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI. In this study, we evaluate a novel technique for robust computational assessment of motion artifacts, and the quantitative comparison of regression models for artifact removal in fetal FC analysis. It exploits the association between dynamic FC and non‐stationarity of fetal movement, to detect residual noise. To validate our motion artifact detection technique in detail, we used a parametric generative model for neural events and fMRI blood oxygenation level‐dependent (BOLD) signal. We conducted a systematic evaluation of 11 commonly used regression models in a sample of 70 fetuses with gestational age of 19–39 weeks. Results demonstrate that the proposed method has better accuracy in identifying corrupted FC compared to methods designed for adults. The technique, suggests that censoring, global signal regression and anatomical component‐based regression models are the most effective models for compensating motion. The benchmarking technique, and the generative model for realistic fetal fMRI BOLD enables investigators conducting in utero fMRI analysis to effectively quantify the impact of fetal motion and evaluate alternative regression strategies for mitigating this impact. The code is publicly available at: https://github.com/cirmuw/fetalfMRIproc. Illustration of the systematic approach to measure the relationship between functional connectivity (FC) and motion at the subject level. The average time series of blood oxygenation level‐dependent signals from cortical ROIs are extracted. Time‐varying functional connectivity and framewise displacement (FD) are computed over sliding windows and the correlation coefficient is measured between them (a). To evaluate the statistical significance of the time‐varying FC–FD relation, an appropriate null distribution is formed by generating surrogate FD time series and repeating the entire procedure (b). Comparing the true value of the time‐varying FC–FD relation to the null distribution determines the extent to which motion drives the changes in FC.

Fetal movement count monitoring is one of the most commonly used methods of assessing fetal well-being. While few methods are available to monitor fetal movements, they consist of several adverse qualities such as unreliability as well as the inability to be conducted in a non-clinical setting. Therefore, this research was conducted to design a complete system that will enable pregnant mothers to monitor fetal movement at home. This system consists of a non-invasive, non-transmitting sensor unit that can be fabricated at a low cost. An accelerometer was utilized as the primary sensor and a micro-controller based circuit was implemented. Clinical testing was conducted utilizing this sensor unit. Two phases of clinical testing procedures were done and during the first phase readings from 120 mothers were taken while during the second phase readings from 15 mothers were taken. Validation was done by conducting an abdominal ultrasound scan which was utilized as the ground truth during the second phase of the clinical testing procedure. A clinical survey was also conducted in parallel with clinical testings in order to improve the sensor unit as well as to improve the final system. Four different signal processing algorithms were implemented on the data set and the performance of each was compared with each other. Out of the four algorithms three algorithms were able to obtain a true positive rate around 85%. However, the best algorithm was selected on the basis of minimizing the false positive rate. Consequently, the most feasible as well as the best performing algorithm was determined and it was utilized in the final system. This algorithm have a true positive rate of 86% and a false positive rate of 7% Furthermore, a mobile application was also developed to be used with the sensor unit by pregnant mothers. Finally, a complete end to end method to monitor fetal movement in a non-clinical setting was presented by the proposed system.

PurposePrenatal stress (PS) during pregnancy affects in utero- and postnatal child brain-development. Key systems affected are the hypothalamic–pituitary–adrenal axis and the autonomic nervous system (ANS). Maternal- and fetal ANS activity can be gauged non-invasively from transabdominal electrocardiogram (taECG). We propose a novel approach to assess couplings between maternal (mHR) and fetal heart rate (fHR) as a new biomarker for PS based on bivariate phase-rectified signal averaging (BPRSA). We hypothesized that PS exerts lasting impact on fHR.MethodsProspective case–control study matched for maternal age, parity, and gestational age during the third trimester using the Cohen Perceived Stress Scale (PSS-10) questionnaire with PSS-10 over or equal 19 classified as stress group (SG). Women with PSS-10 < 19 served as control group (CG). Fetal electrocardiograms were recorded by a taECG. Coupling between mHR and fHR was analyzed by BPRSA resulting in fetal stress index (FSI). Maternal hair cortisol, a memory of chronic stress exposure for 2–3 months, was measured at birth.Results538/1500 pregnant women returned the questionnaire, 55/538 (10.2%) mother–child pairs formed SG and were matched with 55/449 (12.2%) consecutive patients as CG. Maternal hair cortisol was 86.6 (48.0–169.2) versus 53.0 (34.4–105.9) pg/mg (p = 0.029). At 36 + 5 weeks, FSI was significantly higher in fetuses of stressed mothers when compared to controls [0.43 (0.18–0.85) versus 0.00 (− 0.49–0.18), p < 0.001].ConclusionPrenatal maternal stress affects the coupling between maternal and fetal heart rate detectable non-invasively a month prior to birth. Lasting effects on neurodevelopment of affected offspring should be studied.Trial registrationClinical trial registration: NCT03389178.

Reduced fetal movement (RFM) can indicate that a fetus is at risk, but current monitoring methods provide only a “snapshot in time” of fetal health and require trained clinicians in clinical settings. To improve antenatal care, there is a need for continuous, objective fetal movement monitoring systems. Wearable sensors, like inertial measurement units (IMUs), offer a promising data-driven solution, but distinguishing fetal movements from maternal movements remains challenging. The potential benefits of using linear acceleration and angular rate data for fetal movement detection have not been fully explored. In this study, machine learning models were developed using linear acceleration and angular rate data from twenty-three participants who wore four abdominal IMUs and one chest reference while indicating perceived fetal movements with a handheld button. Random forest (RF), bi-directional long short-term memory (BiLSTM), and convolutional neural network (CNN) models were trained using hand-engineered features, time series data, and time–frequency spectrograms, respectively. The results showed that combining accelerometer and gyroscope data improved detection performance across all models compared to either one alone. CNN consistently outperformed other models but required larger datasets. RF and BiLSTM, while more sensitive to signal noise, offered reasonable performance with smaller datasets and greater interpretability.

Fetal movement is an important biological index of fetal well-being. Since 2008, we have been developing an original capacitive acceleration sensor and device that a pregnant woman can easily use to record fetal movement by herself at home during sleep. In this study, we report a newly developed automated software system for analyzing recorded fetal movement. This study will introduce the system and compare its results to those of a manual analysis of the same fetal movement signals (Experiment I). We will also demonstrate an appropriate way to use the system (Experiment II). In Experiment I, fetal movement data reported previously for six pregnant women at 28-38 gestational weeks were used. We evaluated the agreement of the manual and automated analyses for the same 10-sec epochs using prevalence-adjusted bias-adjusted kappa (PABAK) including quantitative indicators for prevalence and bias. The mean PABAK value was 0.83, which can be considered almost perfect. In Experiment II, twelve pregnant women at 24-36 gestational weeks recorded fetal movement at night once every four weeks. Overall, mean fetal movement counts per hour during maternal sleep significantly decreased along with gestational weeks, though individual differences in fetal development were noted. This newly developed automated analysis system can provide important data throughout late pregnancy.