In clinical practice, obstetricians use visual interpretation of fetal heart rate (FHR) to diagnose fetal conditions, but inconsistencies among interpretations can hinder accuracy. This study introduces MTU-Net3+, a deep learning model designed for automated, multi-task FHR analysis, aiming to improve diagnostic accuracy and efficiency. The proposed MTU-Net3 + was built upon the UNet3 + architecture, incorporating an encoder, a decoder, full-scale skip connections, and a deep supervision module, and further integrates a self-attention mechanism and bidirectional Long Short-Term Memory layers to enhance its performance. The MTU-Net3 + model accepts the preprocessed 20-minute FHR signals as input, outputting categorical probabilities and baseline values for each time point. The proposed MTU-Net3 + model was trained on a subset of a public database, and was tested on the remaining data of the public database and a private database. In the remaining public datasets, this model achieved F1 scores of 84.21% for deceleration (F1.Dec) and 61.33% for acceleration (F1.Acc), with a Root Mean Square Baseline Difference (RMSD.BL) of 3.46 bpm, 0% of points with an absolute difference exceeding 15 bpm(D15bpm), a Synthetic Inconsistency Coefficient (SI) of 44.82%, and a Morphological Analysis Discordance Index (MADI) of 7.00%. On the private dataset, the model recorded an RMSD.BL of 1.37 bpm, 0% D15bpm, F1.Dec of 100%, F1.Acc of 87.50%, an SI of 12.20% and a MADI of 2.79%. The MTU-Net3 + model proposed in this study performed well in automated FHR analysis, demonstrating its potential as an effective tool in the field of fetal health assessment.

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.

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.

BACKGROUND: Heat exposure during pregnancy can increase the risk of preterm birth (PTB) through a range of potential mechanisms including pregnancy complications, hormone secretion and infections. However, current research mainly focuses on the effect of heat exposure on pathophysiological pathways of pregnant women, but ignore that maternal heat exposure can also cause physiological changes to the fetus, which will affect the risk of PTB.
OBJECTIVE: In this study, we aimed to explore the mediating role of fetal heart rate (FHR) in the relationship between maternal heat exposure and PTB incidence.
METHODS: We assigned heat exposure to a multi-center birth cohort in China during 2015-2018, which included all 162,407 singleton live births with several times FHR measurements during the second and third trimesters. We examined the associations between heat exposure, FHR and PTB in the entire pregnancy, each trimester and the last gestational month. The inverse odds ratio-weighted approach applied to the Cox regression was used to identify the mediation effect of heat exposure on PTB and its clinical subtypes via FHR.
RESULTS: Exposure to heat significantly increased the risk of PTB during the third trimester and the entire pregnancy, hazard ratios and 95 % CIs were 1.266 (1.161, 1.379) and 1.328 (1.218, 1.447). Heat exposure during the third trimester and entire pregnancy increased FHR in the third trimester by 0.24 bpm and 0.14 bpm. The proportion of heat exposure mediated by FHR elevation on PTB and its subtype ranged from 3.68 % to 24.06 %, with the significant mediation effect found for both medically indicated PTB and spontaneous PTB.
CONCLUSIONS: This study suggests that heat exposure during pregnancy has an important impact on fetal health, and FHR, as a surrogate marker of fetal physiology, may mediate the increased risk of PTB caused by extreme heat. Monitoring and managing physiological changes in the fetus would constitute a promising avenue to reduce adverse birth outcomes associated with maternal heat exposure.

BACKGROUND: In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques.
METHODS: In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer\'s convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy.
RESULTS: Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively.
CONCLUSIONS: Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

UNASSIGNED: This study aims to compare the fetal heart rate (FHR) baseline predicted by the cardiotocograph network (CTGNet) with that estimated by clinicians.
UNASSIGNED: A total of 1,267 FHR recordings acquired with different electrical fetal monitors (EFM) were collected from five datasets: 84 FHR recordings acquired with F15 EFM (Edan, Shenzhen, China) from the Guangzhou Women and Children\'s Medical Center, 331 FHR recordings acquired with SRF618B5 EFM (Sanrui, Guangzhou, China), 234 FHR recordings acquired with F3 EFM (Lian-Med, Guangzhou, China) from the NanFang Hospital of Southen Medical University, 552 cardiotocographys (CTG) recorded using STAN S21 and S31 (Neoventa Medical, Mölndal, Sweden) and Avalon FM40 and FM50 (Philips Healthcare, Amsterdam, The Netherlands) from the University Hospital in Brno, Czech Republic, and 66 FHR recordings acquired using Avalon FM50 fetal monitor (Philips Healthcare, Amsterdam, The Netherlands) at St Vincent de Paul Hospital (Lille, France). Each FHR baseline was estimated by clinicians and CTGNet, respectively. And agreement between CTGNet and clinicians was evaluated using the kappa statistics, intra-class correlation coefficient, and the limits of agreement.
UNASSIGNED: The number of differences <3 beats per minute (bpm), 3-5 bpm, 5-10 bpm and ≥10 bpm, is 64.88%, 15.94%, 14.44% and 4.74%, respectively. Kappa statistics and intra-class correlation coefficient are 0.873 and 0.969, respectively. Limits of agreement are -6.81 and 7.48 (mean difference: 0.36 and standard deviation: 3.64).
UNASSIGNED: An excellent agreement was found between CTGNet and clinicians in the baseline estimation from FHR recordings with different signal loss rates.

To extract weak fetal ECG signals from the mixed ECG signal on the mother\'s abdominal wall, providing a basis for accurately estimating fetal heart rate and analyzing fetal ECG morphology. First, based on the relationship between the maternal chest ECG signal and the maternal ECG component in the abdominal signal, the temporal convolutional encoder-decoder network (TCED-Net) model is trained to fit the nonlinear transmission of the maternal ECG signal from the chest to the abdominal wall. Then, the maternal chest ECG signal is nonlinearly transformed to estimate the maternal ECG component in the abdominal mixed signal. Finally, the estimated maternal ECG component is subtracted from the abdominal mixed signal to obtain the fetal ECG component. The simulation results on the FECGSYN dataset show that the proposed approach achieves the best performance in F1 score, mean square error (MSE), and quality signal-to-noise ratio (qSNR) (98.94%, 0.18, and 8.30, respectively). On the NI-FECG dataset, although the fetal ECG component is small in energy in the mixed signal, this method can effectively suppress the maternal ECG component and thus extract a clearer and more accurate fetal ECG signal. Compared with existing algorithms, the proposed method can extract clearer fetal ECG signals, which has significant application value for effective fetal health monitoring during pregnancy.

CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fetal heart rate). The rapid development of Artificial Intelligence has led realization of computer-aided CTG technology, Intelligent CTG classification based on FHR is a fundamental component of these technologies. Its implementation can provide doctors with auxiliary decisions. Most of existing FHR classification methods are based on combing different deep learning models, such as CNN (Convolutional Neural Network), LSTM (Long short-term memory) and Transformer. However, these studies ignore the balance of positive and negative samples in dataset and the matching degree between model and FHR classification task, which reduces the classification accuracy. In this paper, we mainly discuss two major problems in previous FHR classification studies: reduce class imbalance and select appropriate convolution kernel. To address above two problems, we propose a data augmentation method based on ECMN (Edge Clipping and Multiscale Noise) to resolve class imbalance. Subsequently, we introduce a one-dimensional long convolutional layer, which use trend area to calculate the appropriate convolution kernel. Based on appropriate convolution kernel, an improved residual structure with attention mechanism named TGLCN (Trend-Guided Long Convolution Network) is proposed to improve FHR classification accuracy. Finally, horizontal and longitudinal experiments show that the TGLCN obtains high classification accuracy and speed of parameter adjustment.

Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model\'s ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.