Artificial intelligence based cardiotocogram assessment during labor.

OBJECTIVE: To assess whether artificial intelligence, inspired by clinical decision-making procedures in delivery rooms, can correctly interpret cardiotocographic tracings and distinguish between normal and pathological events. STUDY DESIGN: A method based on artificial intelligence was developed to determine whether a cardiotocogram shows a normal response of the fetal heart rate to uterine activity (UA). For a given fetus and given the UA and previous FHR, the method predicts a fetal heart rate response, under the assumption that the fetus is still in good condition and based on how that specific fetus has responded so far. We hypothesize that this method, when having only learned from fetuses born in good condition, is incapable of predicting the response of a compromised fetus or an episode of transient fetal distress. The (in)capability of the method to predict the fetal heart rate response would then yield a method that can help to assess fetal condition when the obstetrician is in doubt. Cardiotocographic data of 678 deliveries during labor were selected based on a healthy outcome just after birth. The method was trained on the cardiotocographic data of 548 fetuses of this group to learn their heart rate response. Subsequently it was evaluated on 87 fetuses, by assessing whether the method was able to predict their heart rate responses. The remaining 43 cardiotocograms were segment-by-segment annotated by three experienced gynecologists, indicating normal, suspicious, and pathological segments, while having access to the full recording and neonatal outcome. This future knowledge makes the expert annotations of a quality that is unachievable during live interpretation. RESULTS: The comparison between abnormalities detected by the method (only using past and present input) and the annotated CTG segments by gynecologists (also looking at future input) yields an area under the curve of 0.96 for the distinction between normal and pathological events in majority-voted annotations. CONCLUSION: The developed method can distinguish between normal and pathological events in near real-time, with a performance close to the agreement between three gynecologists with access to the entire CTG tracing and fetal outcome. The method has a strong potential to support clinicians in assessing fetal condition in clinical practice.

An intelligent quantification system for fetal heart rhythm assessment: A multicenter prospective study.

BACKGROUND: The motion relationship and time intervals of the pulsed-wave Doppler (PWD) spectrum are essential for diagnosing fetal arrhythmia. However, few technologies currently are available to automatically calculate fetal cardiac time intervals (CTIs). OBJECTIVE: The purpose of this study was to develop a fetal heart rhythm intelligent quantification system (HR-IQS) for the automatic extraction of CTIs and establish the normal reference range for fetal CTIs. METHODS: A total of 6498 PWD spectrums of 2630 fetuses over the junction between the left ventricular inflow and outflow tracts were recorded across 14 centers. E, A, and V waves were manually labeled by 3 experienced fetal cardiologists, with 17 CTIs extracted. Five-fold cross-validation was performed for training and testing of the deep learning model. Agreement between the manual and HR-IQS-based values was evaluated using the intraclass correlation coefficient and Spearman's rank correlation coefficient. The Jarque-Bera test was applied to evaluate the normality of CTIs' distributions, and the normal reference range of 17 CTIs was established with quantile regression. Arrhythmia subset was compared with the non-arrhythmia subset using the Mann-Whitney U test. RESULTS: Significant positive correlation (P <.001) and moderate-to-excellent consistency (P <.001) between the manual and HR-IQS automated measurements of CTIs was found. The distribution of CTIs was non-normal (P <.001). The normal range (2.5th to 97.5th percentiles) was successfully established for the 17 CTIs. CONCLUSIONS: Using our HR-IQS is feasible for the automated calculation of CTIs in practice and thus could provide a promising tool for the assessment of fetal rhythm and function.

Cardiotocography use for fetal assessment during labor in low- and middle-income countries: A scoping review.

BACKGROUND: The use of cardiotocography (CTG) to improve neonatal outcomes is controversial. The medical settings, subjects, utilizations, and interpretation guidelines of CTG are unclear for low- and middle-income countries (LMICs). OBJECTIVES: To assess and review CTG use for studies identified in LMICs and provide insights on the potential for effective use of CTG to improve maternal and neonatal outcomes. SEARCH STRATEGY: The databases Medline, CINAHL, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) were searched for published and unpublished literature through September 2023. SELECTION CRITERIA: Publications were identified which were conducted in LMICs, based on the World Bank list of economies for 2019; targeting pregnant women in childbirth; and focusing on the utilization of CTG and neonatal outcomes. DATA COLLECTION AND ANALYSIS: Publications were screened, and duplicates were removed. A scoping review was conducted using PRISMA-ScR guidelines. RESULTS: The searches generated 1157 hits, of which 67 studies were included in the review. In the studies there was considerable variation and ambiguity regarding the study settings, target populations, utilizations, timing, frequency, and duration of CTG. While cesarean section rates were extensively investigated as an outcome of studies of CTG itself and the effect of additional techniques on CTG, other clinically significant outcomes, including neonatal mortality, were not well reported. CONCLUSIONS: Variations and ambiguities were found in the use of CTG in LMICs. Due to the limited amount of evidence, studies are needed to examine CTG availability in the context of LMICs.

The significance of meeting Dawes-Redman criteria in computerised antenatal fetal heart rate assessment.

OBJECTIVE: To investigate the significance of not meeting Dawes-Redman criteria on computerised cardiotocography in high-risk pregnancies. DESIGN: Retrospective observational study. SETTING: UK university hospital. POPULATION: High-risk pregnancies undergoing antenatal assessment. METHODS: We interrogated the database for records of computerised fetal heart rate assessment and pregnancy outcomes. MAIN OUTCOME MEASURES: Neonatal outcome and stillbirths. RESULTS: Excluding duplicate assessment in the same pregnancy, 14 025 records with complete information on the criteria of normality having been met and the outcome of the pregnancy were available. Criteria were not met for 907 records (6.46%). The gestational age of assessment was lower in the group not meeting criteria of normality. Overall, 32 stillbirths occurred in normally formed fetuses (2.28/1000). Stillbirths were more frequent in the group not meeting criteria (odds ratio [OR] 8.78, 95% CI 4.28-18.02). This finding persisted even after records with abnormally low short-term variation (STV) were excluded. The confidence intervals around the rate of stillbirth in the two groups overlapped beyond an STV of 8 ms. CONCLUSIONS: Approximately 1:16 pregnancies do not meet the criteria of normality. The criteria are not met more often at preterm gestation than at term. The risk of stillbirth was higher in the group not meeting criteria of normality, even if cases with low STV are excluded. Cases not meeting criteria should be followed up closely, unless the STV is ≥8 ms. Stillbirths still occurred in the group meeting criteria, but the rate was lower than in the general population.

A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal.

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

Fetal states identification in cardiotocographic tracings through discrete emissions multivariate hidden Markov models.

BACKGROUND AND OBJECTIVES: Computerized Cardiotocography (cCTG) allows to analyze the Fetal Heart Rate (FHR) objectively and thoroughly, providing valuable insights on fetal condition. A challenging but crucial task in this context is the automatic identification of fetal activity and quiet periods within the tracings. Different neural mechanisms are involved in the regulation of the fetal heart, depending on the behavioral states. Thereby, their correct identification has the potential to increase the interpretability and diagnostic capabilities of FHR quantitative analysis. Moreover, the most common pathologies in pregnancy have been associated with variations in the alternation between quiet and activity states. METHODS: We address the problem of fetal states clustering by means of an unsupervised approach, resorting to the use of a multivariate Hidden Markov Models (HMM) with discrete emissions. A fixed length sliding window is shifted on the CTG traces and a small set of features is extracted at each slide. After an encoding procedure, these features become the emissions of a multivariate HMM in which quiet and activity are the hidden states. After an unsupervised training procedure, the model is used to automatically segment signals. RESULTS: The achieved results indicate that our developed model exhibits a high degree of reliability in identifying quiet and activity states within FHR signals. A set of 35 CTG signals belonging to different pregnancies were independently annotated by an expert gynecologist and segmented using the proposed HMM. To avoid any bias, the physician was blinded to the results provided by the algorithm. The overall agreement between the HMM's predictions and the clinician's interpretations was 90%. CONCLUSIONS: The proposed method reliably identified fetal behavioral states, the alternance of which is an important factor in the fetal development. One key strength of our approach lies in the ease of interpreting the obtained results. By utilizing a small set of parameters that are already used in cCTG and possess clear intrinsic meanings, our method provides a high level of explainability. Another significant advantage of our approach is its fully unsupervised learning process. The states identified by our model using the Baum-Welch algorithm are associated with the "Active" and "Quiet" states only after the clustering process, removing the reliance on expert annotations. By autonomously identifying the clusters based solely on the intrinsic characteristics of the signal, our method achieves a more objective evaluation that overcomes the limitations of subjective interpretations. Indeed, we believe it could be integrated in cCTG systems to obtain a more complete signal analysis.

Hidden Markov model in nonnegative matrix factorization for fetal heart rate estimation using physiological priors.

Objective.Fetal heart rate (fHR) analysis remains the most common technique for detecting fetal distress when monitoring the fetal well-being during labor. If cardiotocography (CTG) is nowadays the non-invasive clinical reference technique for fHR measurement, it suffers from several drawbacks, hence an increasing interest towards alternative technologies, especially around abdominal ECG (aECG).Approach.An original solution, using a single abdominal lead, was recently proposed to address both the feasibility in clinical routine and the challenging detection of temporal events when facing interfered signals from real life conditions. Based on a specification of the non-negative matrix factorization (NMF) algorithm, it exploits the semi-periodicity of fetal electrocardiogram (fECG) for fHR estimation. However, this method assumes temporal independence and therefore does not consider the continuity property of fHR values. It is thus proposed to add to the NMF framework a hidden Markov model (HMM) to include physiological information about fHR temporal evolution. Under a statistical setting, constraints have been added by accommodating regularization terms through Bayesian priors.Main results.The proposed method is evaluated on 23 real aECG signals from a new clinical database, according to CTG reference, and compared with the original NMF-only algorithm. The new proposed method improves performance, with an agreement with CTG increasing from 71% to 80%.Significance.This highlights the interest of a better modelization of the fHR characteristics for a more robust estimation.

Multi-Chain Semi-Markov Analysis of Intrapartum Cardiotocography.

Visual assessment of the evolution of fetal heart rate (FHR) and uterine pressure (UP) patterns is the standard of care in the intrapartum period. Unfortunately, this assessment has high levels of intra- and inter-observer variability. This study processed and analyzed FHR and UP patterns using computerized pattern recognition tools. The goal was to evaluate differences in FHR and UP patterns between fetuses with normal outcomes and those who developed hypoxic-ischemic encephalopathy (HIE). For this purpose, we modeled the sequence of FHR patterns and uterine contractions using Multi-Chain Semi-Markov models (MCSMMs). These models estimate the probability of transitioning between FHR or UP patterns and the dwell time of each pattern. Our results showed that in comparison to the control group, the HIE group had: (1) more frequent uterine contractions during the last 12 hours before birth; (2) more frequent FHR decelerations during the last 12 hours before birth; (3) longer decelerations during the last eight hours before birth; and (4) shorter baseline durations during the last five hours before birth. These results demonstrate that the fetuses in the HIE group were subject to a more stressful environment than those in the normal group. Clinical Relevance- Our results revealed statistically significant differences in FHR/UP patterns between the normal and HIE groups in the hours before birth. This indicates that features derived using MCSMMs may be useful in a machine learning framework to detect infants at increased risk of developing HIE allowing preventive interventions.

Optimal duration of cardiotocography assessment using the iPREFACE score to predict fetal acidemia.

Cardiotocography (CTG) applicability to improve fetal outcomes remains controversial. This study aimed to determine the clinically optimal CTG assessment duration using the integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring (iPREFACE score). This single-center, retrospective observational study included 325 normal full-term singleton vaginal deliveries at the Toho University Omori Medical Center, from September 2018 to March 2019. The iPREFACE(10), iPREFACE(30), and iPREFACE(60) scores were calculated at 10, 30, and 60 min immediately before delivery. The primary outcome was fetal acidemia (umbilical artery blood pH < 7.2). The secondary outcome was the correlation between all iPREFACE scores and the umbilical artery blood pH, base excess (BE), and lactate values. Patients without accurate CTG findings or with failure of umbilical artery blood sampling immediately after birth were excluded, leaving 145 patients in the final analysis. Of these, 16, three, and two had umbilical artery blood pH of < 7.2, < 7.1, and < 7.0, respectively. All iPREFACE scores significantly correlated with umbilical artery blood pH, BE, and lactate values. iPREFACE(30) had the highest predictive capacity for fetal acidemia, suggesting that 30 min immediately before delivery may be a useful scoring time in clinical practice.

On designing a biosignal-based fetal state assessment system: A systematic mapping study.

BACKGROUND AND OBJECTIVE: The patterns present in biosignals, such as fetal heart rate (FHR), are valuable indicators of fetal well-being. In designing biosignal analysis systems, the variety of approaches and technology usage impairs the decision-making for the fundamental units of the systems. There is a need for an updated overview of studies encompassing the biosignal-based fetal state assessment systems. Therefore, we propose a systematic mapping study to identify and synthesize recent research regarding the building blocks that compose these systems. METHODS: We followed well-established guidelines to perform a systematic mapping of studies regarding the building-blocks that compose the fetal state assessment systems and published between January 2016 and January 2021. A search string was determined based on the mapping questions and the PI (population and intervention) divisions. The search string was applied in digital libraries covering the fields of computer science, engineering, and medical informatics. Then, we applied the forward snowballing technique to complement the resulting set. This process resulted in 75 primary studies selected from a total of 871 papers. RESULTS: Selected studies were classified according to the publication types, systems design stages, datasets, and predictive capabilities. The results revealed that (i) The majority of the selected studies refer to the method as a type of publication and there is a lack of validation studies; (ii) The CTU-UHB was the most frequent biosignal-based dataset and UCI-CTG was the most frequent feature-based data; (iii) The selected studies made use of the system design stages alone or in a mixed-mode. CONCLUSION: The results indicated that the well-established classification models achieved competitive results compared with the state-of-the-art methods in data-constrained system designs. Moreover, we identified the need for validation studies in the clinical environment.

Mobile Self-Operated Home Ultrasound System for Remote Fetal Assessment During Pregnancy.

Background: Mobile medical devices for self-patient use are a rapidly evolving section of telehealth. We examined the INSTINCT® ultrasound system, a portable, self-operated ultrasound device attached to a commercial smartphone for remote fetal assessment. We aimed to evaluate whether it is feasible to use remote fetal assessment during pregnancy. Materials and Methods: This is an observational noninterventional trial. We included women with a singleton fetus at 14 + 0 to 39 + 6 gestational weeks. Each participant received the device for a self-use period of 7-14 days and was instructed to perform one to three scans a day. Participants completed a self-assessment questionnaire to evaluate safety and usability (i.e., user experience and satisfaction). Each scan was evaluated for fetal heart activity, amniotic fluid volume, fetal tone, fetal body, and breathing movements. Results: One hundred women, completing 1,360 self scans, used the device for 8.1 ± 1.5 days, performing an average of 13.6 ± 6.2 scans each. There were no device-related serious adverse events. Success in detection was 95.3% for fetal heart activity, 88.3% for body movements, 69.4% for tone, 92.2% for normal amniotic fluid volume, and 23.8% for breathing movements. Interobserver agreement was 94.4% for fetal heart rate activity, 85.9% for body movements, 69.5% for fetal tone, 86.9% for amniotic fluid volume, and 94.0% for breathing movements. Self-assessed user experience was rated at 4.4/5, whereas device satisfaction was rated at 3.9/5. Conclusion: The INSTINCT ultrasound system is a feasible solution for remote sonographic fetal assessment. Further studies are needed to assess its role and impact in telehealth antenatal care and fetal surveillance.

Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records.

BACKGROUND: Cardiotocography (CTG) interpretation plays a critical role in prenatal fetal monitoring. However, the interpretation of fetal status assessment using CTG is mainly confined to clinical research. To the best of our knowledge, there is no study on data analysis of CTG records to explore the causal relationships between the important CTG features and fetal status evaluation. METHODS: For analyses, 2126 cardiotocograms were automatically processed and the respective diagnostic features measured by the Sisporto program. In this paper, we aim to explore the causal relationships between the important CTG features and fetal status evaluation. First, we utilized data visualization and Spearman correlation analysis to explore the relationship among CTG features and their importance on fetal status assessment. Second, we proposed a forward-stepwise-selection association rule analysis (ARA) to supplement the fetal status assessment rules based on sparse pathological cases. Third, we established structural equation models (SEMs) to investigate the latent causal factors and their causal coefficients to fetal status assessment. RESULTS: Data visualization and the Spearman correlation analysis found that thirteen CTG features were relevant to the fetal state evaluation. The forward-stepwise-selection ARA further validated and complemented the CTG interpretation rules in the fetal monitoring guidelines. The measurement models validated the five latent variables, which were baseline category (BCat), variability category (VCat), acceleration category (ACat), deceleration category (DCat) and uterine contraction category (UCat) based on fetal monitoring knowledge and the above analyses. Furthermore, the interpretable models discovered the cause factors of fetal status assessment and their causal coefficients to fetal status assessment. For instance, VCat could predict BCat, and UCat could predict DCat as well. ACat, BCat and DCat directly affected fetal status assessment, where ACat was the important causal factor. CONCLUSIONS: The analyses revealed the interpretation rules and discovered the causal factors and their causal coefficients for fetal status assessment. Moreover, the results are consistent with the computerized fetal monitoring and clinical knowledge. Our approaches are conducive to evidence-based medical research and realizing intelligent fetal monitoring.

Comprehensive pregnancy monitoring with a network of wireless, soft, and flexible sensors in high- and low-resource health settings.

Vital signs monitoring is a fundamental component of ensuring the health and safety of women and newborns during pregnancy, labor, and childbirth. This monitoring is often the first step in early detection of pregnancy abnormalities, providing an opportunity for prompt, effective intervention to prevent maternal and neonatal morbidity and mortality. Contemporary pregnancy monitoring systems require numerous devices wired to large base units; at least five separate devices with distinct user interfaces are commonly used to detect uterine contractility, maternal blood oxygenation, temperature, heart rate, blood pressure, and fetal heart rate. Current monitoring technologies are expensive and complex with implementation challenges in low-resource settings where maternal morbidity and mortality is the greatest. We present an integrated monitoring platform leveraging advanced flexible electronics, wireless connectivity, and compatibility with a wide range of low-cost mobile devices. Three flexible, soft, and low-profile sensors offer comprehensive vital signs monitoring for both women and fetuses with time-synchronized operation, including advanced parameters such as continuous cuffless blood pressure, electrohysterography-derived uterine monitoring, and automated body position classification. Successful field trials of pregnant women between 25 and 41 wk of gestation in both high-resource settings (n = 91) and low-resource settings (n = 485) demonstrate the system's performance, usability, and safety.

Assessment of human fetal cardiac autonomic nervous system development using color tissue Doppler imaging.

OBJECTIVES: Functional development of the fetal cardiac autonomic nervous system (cANS) plays a key role in fetal maturation and can be assessed through fetal heart rate variability (fHRV)-analysis, with each HRV parameter representing different aspects of cANS activity. Current available techniques, however, are unable to assess the fHRV parameters accurately throughout the whole pregnancy. This study aims to test the feasibility of color tissue Doppler imaging (cTDI) as a new ultrasound technique for HRV analysis. Secondly, we explored time trends of fHRV parameters using this technique. METHODS: 18 healthy singleton fetuses were examined sequentially every 8 weeks from 10 weeks GA onwards. From each examination, 3 cTDI recordings of the four-chamber view of 10 seconds were retrieved to determine accurate beat-to-beat intervals. The fHRV parameters SDNN, RMSSD, SDNN/RMSSD, and pNN10, each representing different functional aspects of the cANS, were measured, and time trends during pregnancy were explored using spline functions within a linear mixed-effects model. RESULTS: In total, 77% (95% Cl 66-87%) of examinations were feasible for fHRV analysis from the first trimester onwards, which is a great improvement compared to other techniques. The technique is able to determine different maturation rates of the fHRV parameters, showing that cANS function, presumably parasympathetic activity, establishes around 20 weeks GA and matures rapidly until 30 weeks GA. CONCLUSIONS: This is the first study able to assess cANS function through fHRV analysis from the first trimester onwards. The use of cTDI to determine beat-to-beat intervals seems feasible in just 3 clips of 10 seconds, which holds promise for future clinical use in assessing fetal well-being.

Economic evaluation of computerised interpretation of fetal heart rate during labour: a cost-consequence analysis alongside the INFANT study.

OBJECTIVE: Economic evaluation of computerised decision-support software intended to assist in the interpretation of a cardiotocography (CTG) during birth. DESIGN: Individual patient level data from the INFANT study (an unmasked randomised controlled trial). SETTING: Maternity units in the UK and Ireland. POPULATION: Singleton or twin pregnancy women of 35 weeks' gestation or more and receiving continuous electronic fetal monitoring during labour. INTERVENTION: Computerised decision-support software. METHODS: Cost-consequence analysis presenting costs and outcomes with a time horizon of 2 years from a government healthcare perspective. Unit cost data collected from a combination of primary and secondary sources. MAIN OUTCOME MEASURES: Primary clinical outcomes were (i) composite 'poor neonatal outcome' and (ii) developmental assessment at age 2 years in a subset of surviving children. Mean cost per mother and infant dyad from birth to hospital discharge, and from hospital discharge to 24 months follow-up. Maternal health-related quality of life was assessed at 12 and 24 months follow-up using the EuroQol three-level health-related quality of life instrument (EQ-5D-3L). RESULTS: Data were analysed for 46 042 women and 46 614 infants. No statistically significant differences were detected between trial arms in any of the primary clinical outcomes or maternal quality of life. No statistically significant differences in costs were detected in maternal or infant costs from trial entry to hospital discharge or overall from hospital discharge to 2-year follow-up. CONCLUSIONS: Decision-support software during labour is not associated with additional maternal or infant benefits and over a 2-year period the software did not lead to additional costs or savings to the National Health Service. TRIAL REGISTRATION NUMBER: ISRCTN98680152.

Reducing neonatal morbidity by discontinuing oxytocin during the active phase of first stage of labor: a multicenter randomized controlled trial STOPOXY.

BACKGROUND: Oxytocin is effective in reducing labor duration, but can be associated with fetal and maternal complications such as neonatal acidosis and post-partum hemorrhage. When comparing discontinuing oxytocin in the active phase with continuing oxytocin infusion, previous studies were underpowered to show a reduction in neonatal morbidity. Thus, we aim at evaluating the impact of discontinuing oxytocin during the active phase of the first stage of labor on the neonatal morbidity rate. METHODS: STOPOXY is a multicenter, randomized, open-label, controlled trial conducted in 20 maternity units in France. The first participant was recruited January 17th 2020. The trial includes women with a live term (≥37 weeks) singleton, in cephalic presentation, receiving oxytocin before 4 cm, after an induced or spontaneous labor. Women aged < 18 years, with a lack of social security coverage, a scarred uterus, a multiple pregnancy, a fetal congenital malformation, a growth retardation <3rd percentile or an abnormal fetal heart rate at randomization are excluded. Women are randomized before 6 cm when oxytocin is either continued or discontinued. Randomization is stratified by center and parity. The primary outcome, neonatal morbidity is assessed using a composite variable defined by an umbilical arterial pH at birth < 7.10 and/or a base excess > 10 mmol/L and/or umbilical arterial lactates> 7 mmol/L and/or a 5 min Apgar score < 7 and/or admission in neonatal intensive care unit. The primary outcome will be compared between the two groups using a chi-square test with a p-value of 0.05. Secondary outcomes include neonatal complications, duration of active phase, mode of delivery, fetal and maternal complications during labor and delivery, including cesarean delivery rate and postpartum hemorrhage, and birth experience. We aim at including 2475 women based on a reduction in neonatal morbidity from 8% in the control group to 5% in the experimental group, with a power of 80% and an alpha risk of 5%. DISCUSSION: Discontinuing oxytocin during the active phase of labor could improve both child health, by reducing moderate to severe neonatal morbidity, and maternal health by reducing cesarean delivery and postpartum hemorrhage rates. TRIAL REGISTRATION: Clinical trials NCT03991091 , registered June 19th, 2019.

Unsupervised hidden semi-Markov model for automatic beat onset detection in 1D Doppler ultrasound.

OBJECTIVE: One dimensional (1D) Doppler ultrasound (DUS) is commonly used for fetal health assessment, during both regular prenatal visits and labor. It is used in preference to ECG and other modalities because of its simplicity and cost. To date, all analysis of such data has been confined to a smoothed, windowed heart rate estimation derived from the 1D DUS signal, reducing the potential of short-term variability information. A first step in improving the assessment of short-term variability of the fetal heart rate (FHR) is through implementing an accurate beat detector for 1D DUS signals. APPROACH: This work presents an unsupervised probabilistic segmentation method enabled by a hidden semi-Markov model (HSMM). The proposed method employs envelope and spectral features for an online segmentation of fetal 1D DUS signal. The beat onsets and fetal cardiac beat-to-beat intervals are then estimated from the segmentations. For this work, two data sets were used, including 1D DUS recordings from five fetuses recorded in Germany, comprising 6521 beats and 45.06 minutes of data (dataset 1). Simultaneous fetal ECG (fECG) was used as the reference for beat timing. Dataset 2, comprising 4044 beats captured from 17 subjects in the UK was hand scored for beat location and was used as an independent held-out test set. Leave-one-out subject cross-validation was used for parameter tuning on dataset 1. No retraining was performed for dataset 2. To assess the performance of the beat onset detection, the root mean square error (RMSE), F1 score, sensitivity, positive predictivity (PPV) and the error in several standard common heart rate variability metrics were used. These metrics were evaluated on three fiducial points: (1) beat onset, (2) beat offset, and (3) middle of beat interval. MAIN RESULTS: In dataset 1, the proposed method provided an RMSE of 20 ms, F1 score of 97.5 %, a Se of 97.6%, and a PPV of 97.3%. In dataset 2, the proposed method achieved an RMSE of 26 ms, an F1 score of 98.5 %, a Se of 98.0 % and a PPV of 98.9 %. It was also determined that the best beat-to-beat interval was derived from the onset of each beat. For the dataset 2, significant correlations were found in all short term heart rate variability metrics tested, both in the time and frequency domain. Only the proportion of successive normal-to-normal interval differences greater than 20 ms (pNN20) exhibited a significant absolute difference. SIGNIFICANCE: This work presents the first-ever description of an algorithm to identify cardiac beats with 1D DUS, closely matching the fetal ECG-derived beats, to enable short-term heart rate variability analysis. The novel algorithm proposed requires no human labeling of data, and could have applicability beyond 1D DUS to other similar highly variable time series.

Scale-Up of Doppler to Improve Intrapartum Fetal Heart Rate Monitoring in Tanzania: A Qualitative Assessment of National and Regional/District Level Implementation Factors.

High-quality intrapartum care, including intermittent monitoring of fetal heart rates (FHR) to detect and manage abnormalities, is recommended by WHO and the Government of Tanzania (GoT) and creates potential to save newborn lives in Tanzania. Handheld Doppler devices have been investigated in several low-resource countries as an alternative to Pinard stethoscope and are more sensitive to detecting accelerations and decelerations of the fetal heart as compared to Pinard. This study assessed perspectives of high-level Tanzanian policymakers on facilitators and barriers to scaling up use of the hand-held Doppler for assessing FHR during labor and delivery. From November 2018-August 2019, nine high-level policymakers and subject matter experts were interviewed using a semi-structured questionnaire, with theoretical domains drawn from Proctor's implementation outcomes framework. Interviewees largely saw use of Doppler to improve intrapartum FHR monitoring as aligning with national priorities, though they noted competing demands for resources. They felt that GoT should fund Doppler, but prioritization and budgeting should be driven from district level. Recommended ways forward included learning from scale up of Helping Babies Breathe rollout, making training approaches effective, using clinical mentoring, and establishing systematic monitoring of outcomes. To be most effective, introduction of Doppler must be concurrent with improving case management practices for abnormal intrapartum FHR. WHO's guidance on scale-up, as well as implementation science frameworks, should be considered to guide implementation and evaluation.

Fetal heart rate monitoring practices at a public hospital in Northern Uganda - what health workers document, do and say.

Background: In Uganda, perinatal mortality is 38 per 1000 pregnancies. One-third of these deaths are due to birth asphyxia. Adequate fetal heart rate (FHR) monitoring during labor may detect birth asphyxia but little is known about monitoring practices in low resource settings.Objective: To explore FHR monitoring practices among health workers at a public hospital in Northern Uganda.Methods: A sequential explanatory mixed methods study was conducted by reviewing 251 maternal records and conducting 11 interviews and two focus group discussions with health workers complemented by observations of 42 women in labor until delivery. Quantitative data were summarized using frequencies and percentages. Content analysis was used for qualitative data.Results: FHR was assessed in 235/251 (93.6%) of records at admission. Health workers documented the FHR at least once in 175/228 (76.8%) of cases during the first stage of labor compared to observed 17/25 (68.0%) cases. Median intervals between FHR monitoring were 30 (IQR 30-120) minutes in patients' records versus 139 (IQR 87-662) minutes according to observations. Observations suggested no monitoring of FHR during the second stage of labor but records indicated monitoring in 3.2% of cases. Reported barriers to adequate FHR monitoring were inadequate number of staff and monitoring devices, institutional challenges such as few beds, documentation problems and perceived non-compliant women not reporting for repeated checks during the first stage of labor. Health workers demonstrated knowledge of national FHR monitoring guidelines and acknowledged that practice was different.Conclusions: When compared to national and international guidelines, FHR monitoring is sub-optimal in the studied setting. Approximately one in four women was not monitored during the first stage of labor. Barriers to appropriate FHR monitoring included shortage of staff and devices, institutional challenges and mother's negative attitudes. These barriers need to be addressed in order to reduce neonatal mortality.

Assessment of autonomic function in the late term fetus: The effects of sex and state.

Researchers have reported associations between fetal sex and heart rate (FHR) and heart rate variability (FHRV) but rarely in the context of fetal behavioral sleep state. We examined differences in measures of fetal autonomic function by sex and sleep state. Fetal abdominal ECG monitoring technology was used to measure FHR and two measures of FHRV-standard deviation of FHR (SD) and beat-to-beat variability (RMSSD). FHR and movement patterns were also recorded with standard Doppler ultrasound monitor technology employed to code sleep states. Data were collected from 82 healthy fetuses ranging from 36 to 39 weeks gestation. A one-way MANOVA showed that FHR was significantly lower and SD was significantly higher for males than females. Independent samples t tests found that these sex differences were only in the active sleep state. There were no significant differences in RMSSD by sex. Repeated measures MANOVA for a subset that exhibited more than one state (N = 22) showed that SD was significantly different by state. RMSSD showed a marginally significant sleep state difference. In conclusion, fetal sex differences in HR and HRV may indicate more mature autonomic functioning in near-term males than females and fetal sleep state can influence abdominal fECG derived measures of FHR and FHRV.