Cardiotocography (CTG) is widely used to assess fetal well-being. CTG is typically obtained using ultrasound and autocorrelation methods, which extract periodicity from the signal to calculate the heart rate. However, during labor, maternal vessel pulsations can be measured, resulting in the output of the maternal heart rate (MHR). Since the autocorrelation output is displayed as fetal heart rate (FHR), there is a risk that obstetricians may mistakenly evaluate the fetal condition based on MHR, potentially overlooking the necessity for medical intervention. This study proposes a method that utilizes Doppler ultrasound (DUS) signals and artificial intelligence (AI) to determine whether the heart rate obtained by autocorrelation is of fetal origin. We developed a system to simultaneously record DUS signals and CTG and obtained data from 425 cases. The midwife annotated the DUS signals by auditory differentiation, providing data for AI, which included 30,160 data points from the fetal heart and 2160 data points from the maternal vessel. Comparing the classification accuracy of the AI model and a simple mathematical method, the AI model achieved the best performance, with an area under the curve (AUC) of 0.98. Integrating this system into fetal monitoring could provide a new indicator for evaluating CTG quality.

BACKGROUND: Fetal umbilical cord hematoma has a low incidence but high mortality, and its cause during delivery is often unclear. We report an autopsy case in which it was concluded that umbilical cord hematoma resulted from fetal movements during childbirth.
UNASSIGNED: A 27-year-old primigravida at 39 + 2 weeks gestation with normal antenatal visits suffered a fetal heart rate decrease during active labor. Bedside ultrasound revealed fetal death in utero 22 min later. Forensic pathologists found that the umbilical vessels were torn and bleeding on almost the same plane, and the hematoma compressed both umbilical arteries, which is the cause of fetal stillness in utero. A total of 32 cases were reported, including 6 umbilical cord ruptures and 26 umbilical cord hematomas. The cause of hematoma was unknown in 77 % of cases, while dysplasia was present in 56.25 % of umbilical cords.
CONCLUSIONS: This case indicates that fetal movements may cause umbilical cord vessel injury, particularly when oxytocin is used to induce labor. When fetal heart sounds decrease for no apparent reason, the possibility of cord injury should be considered, and cesarean delivery should be performed as soon as possible. Therefore, rigorous fetal heart tracing during active delivery is necessary.

UNASSIGNED: Fetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and obstetrician expertise present challenges in interpreting fetal heart rate, including failure to acknowledge findings or misinterpretation. Artificial intelligence has the potential to support obstetricians in diagnosing abnormal fetal heart rates.
UNASSIGNED: Employ preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models.
UNASSIGNED: The results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility.
UNASSIGNED: Compared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women\'s clinical characteristics and disregard the potential impact of varying gestational weeks.

BACKGROUND: Buprenorphine-naloxone treatment may confer substantial benefits for the treatment of opioid use disorder (OUD) during pregnancy including lower risk for overdose/death, less diversion potential and reduced use of other substances. Treatment may also result in less severe Neonatal Abstinence Syndrome (NAS), but little is known about the effects of this medication on fetal neurodevelopment.
METHODS: The purpose of the current study is to evaluate neurobehaviors among fetuses exposed to buprenorphine-naloxone at four time points over the second and third trimesters of gestation in pregnant women with OUD on buprenorphine-naloxone therapy. Sixty minutes of continuous fetal monitoring via fetal actocardiograph with a single wide array abdominal transducer took place at times of peak and trough buprenorphine-naloxone levels in 24 pregnant women. Data collection, which included measures of fetal heart rate and motor activity, was conducted between 24 and 36 weeks gestation, with the majority (84.6%) monitored at two or more gestational ages. Medication dose and other substance use was monitored throughout the study and infant NAS severity was assessed.
RESULTS: Fetal heart rate (FHR), FHR variability, accelerations in FHR, and motor activity were suppressed when buprenorphine-naloxone levels were at pharmacologic peak as compared to trough concentrations at 36 weeks, but not earlier in gestation. Maternal medication dose was unrelated to infant NAS severity.
CONCLUSIONS: Conclusions: There were evident subclinical fetal neurophysiological responses at times of peak maternal buprenorphine/naloxone levels in later gestation, similar to those previously described for buprenorphine only. Further studies evaluating the effects of these changes in fetal neurobehaviors on the longer-term infant development are needed.

OBJECTIVE: Spinal anesthesia often causes hypotension, with consequent risk to the fetus. The use of vasopressor agents has been highly recommended for the prevention of spinal anesthesia-induced hypotension during caesarean delivery. Many studies have shown that norepinephrine can provide more stable maternal hemodynamics than phenylephrine. We therefore tested the hypothesis that norepinephrine preserves fetal circulation better than phenylephrine when used to treat maternal hypotension consequent to spinal anesthesia.
METHODS: Prospective, randomized, double-blinded study.
METHODS: Operating room.
METHODS: We recruited 223 parturients with uncomplicated singleton pregnancies who were scheduled for elective caesarean section under combined spinal-epidural anesthesia.
METHODS: The patients received prophylactic intravenous infusion of either 0.08 μg/kg/min norepinephrine or 0.5 μg/kg/min phenylephrine for prevention of spinal anesthesia-induced hypotension.
METHODS: Changes in fetal heart rate and fetal cardiac output before and after spinal anesthesia were measured using noninvasive Doppler ultrasound.
RESULTS: 90 subjects who received norepinephrine infusion and 93 subjects who received phenylephrine infusion were ultimately analyzed in the present study. The effects of norepinephrine and phenylephrine on the change of fetal heart rate and fetal cardiac output at 3 and 6 min after spinal block were similar. Although there was a statistically significant decrease in fetal cardiac output at 6 min after subarachnoid block initiation in both the norepinephrine group (mean difference 0.02 L/min; 95% CI, 0-0.04 L/min; P = 0.03) and the phenylephrine group (mean difference 0.02 L/min; 95% CI, 0-0.04 L/min; P = 0.02), it remained within the normal range.
CONCLUSIONS: Prophylactic infusion of comparable doses of phenylephrine or norepinephrine has similar effects on fetal heart rate and cardiac output changes after spinal anesthesia. Neither phenylephrine nor norepinephrine has meaningful detrimental effects on fetal circulation or neonatal outcomes.

OBJECTIVE: The use of telemonitoring in healthcare is generally increasing. Women with complicated pregnancies are using telemonitoring as an alternative to conventional management, encompassing hospitalization or frequent outpatient clinic visits. However, there is sparse evidence on how pregnant women experience monitoring of their unborn babies at home. Women might feel uncomfortable with this responsibility, and moreover they might miss face-to-face contact with healthcare personnel.
METHODS: The study setting was a Danish hospital with a tertiary obstetric unit attending approximately 3400 births annually. A qualitative study design with interview as method included 11 pregnant women with type 1 diabetes or Gestational Diabetes Mellitus. This design was used to investigate how pregnant women with complicated pregnancies experienced telemonitoring of the fetus. Reflexive thematic analysis was used to analyze the pregnant women\'s experiences of telemonitoring.
RESULTS: Women with type 1 diabetes or Gestational Diabetes Mellitus found the advantages of telemonitoring to outweigh the disadvantages. They experienced telemonitoring as time-saving and that telemonitoring decreased the level of stress. Moreover, telemonitoring supports positive collaboration with healthcare professionals. The women also experienced a lack of coordination of consultations between different departments at the hospital and challenges with timing, feedback, and technical issues. Moreover, the women requested an opportunity to discuss family formation and emotions.
CONCLUSIONS: Pregnant women with type 1 diabetes or Gestational Diabetes Mellitus benefit from the use of telemonitoring. To further improve the implementation and use of telemonitoring clinical implications, consider how timing and coordination of care, technical equipment, and feedback mechanisms could be improved.

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Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.

Monitoring fetal heart rate (FHR) through cardiotocography is crucial for the early diagnosis of fetal distress situations, necessitating prompt obstetrical intervention. However, FHR signals are often marred by various contaminants, making preprocessing techniques essential for accurate analysis. This scoping review, following PRISMA-ScR guidelines, describes the preprocessing methods in original research articles on human FHR (or beat-to-beat intervals) signal preprocessing from PubMed and Web of Science, published from their inception up to May 2021. From the 322 unique articles identified, 54 were included, from which prevalent preprocessing approaches were identified, primarily focusing on the detection and correction of poor signal quality events. Detection usually entailed analyzing deviations from neighboring samples, whereas correction often relied on interpolation techniques. It was also noted that there is a lack of consensus regarding the definition of missing samples, outliers, and artifacts. Trends indicate a surge in research interest in the decade 2011-2021. This review underscores the need for standardizing FHR signal preprocessing techniques to enhance diagnostic accuracy. Future work should focus on applying and evaluating these methods across FHR databases aiming to assess their effectiveness and propose improvements.