Fetal Extraction Research Articles

Fetal sex impacts birth to placental weight ratio and umbilical cord oxygen values with implications for regulatory mechanisms

BackgroundWe determined the effect of fetal sex on birth/placental weight and umbilical vein and artery oxygen values with implications for placental efficiency and regulatory mechanisms underlying fetal–placental growth differences.MethodsA hospital database was used to obtain birth/placental weight, cord PO2 and other information on patients delivering between Jan 1, 1990 and Jun 15, 2011 with GA > 34 weeks (N = 69,836). Oxygen saturation was calculated from the cord PO2 and pH data, while fractional O2 extraction was calculated from the oxygen saturation data. The effect of fetal sex on birth/placental weight, cord PO2, O2 saturation, and fractional O2 extraction was examined in all patients adjusting for pregnancy and labor/delivery covariates, and in a subset of low-risk patients.ResultsBirth/placental weights were lower in females indicating decreased placental efficiency. Umbilical vein oxygen values were higher in females attributed to increased uterine blood flow, while artery oxygen values were lower in females attributed to decreased hemoglobin and umbilical blood flow, and increased oxygen consumption. Fetal O2 extraction was increased in females confirming increased O2 consumption relative to delivery.ConclusionsSex-related differences in uterine/umbilical blood flows, placental development, and fetal O2 consumption can be linked to the differences observed in cord oxygen. The lower umbilical artery oxygen in females as a measure of systemic oxygenation signaling growth could account for their decreased birth weights, while slower development in female placentae could account for their lower placental weights, which could be differentially effected contributing to their lower birth/placental weights.

A novel fetal ecg signal extraction from maternal ecg signal using conditional generative adversarial networks (CGAN)

Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system.

Effects of Early Nursing Monitoring on Pregnancy Outcomes of Pregnant Women with Gestational Diabetes Mellitus under Internet of Things.

To analyze the effect of early nursing intervention based on fetal heart signal extraction algorithm and Internet of Things (IoT) wireless communication technology on the adverse pregnancy outcomes of pregnant women with gestational diabetes mellitus (GDM) and newborns, 88 pregnant women diagnosed with GDM who underwent the 75 g glucose tolerance test at 24-28 gestational weeks in the hospital were selected as the research objects. According to the different intervention methods, the patients were divided into 44 cases of the experimental group (nursing intervention based on maternal and infant monitoring system) and 44 cases of the control group (outpatient follow-up intervention). The results showed that the compliance score and diet compliance rate of patients in the experimental group were signally higher than those in the control group at 1 and 3 months after intervention (P < 0.05). The levels of fasting blood glucose (FBG), blood glucose 2 hours after the meal, and hemoglobin A1c (HbA1c) in the experimental group were lower than those in the control group at 1 and 3 months after intervention (P < 0.05). The number of giant babies, hypoglycemia, hyperbilirubinemia, fetal distress, premature delivery, and birth weight in the experimental group was all lower than those in the control group, while the Apgar scores were higher than that in the control group (P < 0.05). To sum up, the intervention based on the intelligent maternal and infant monitoring system could timely help pregnant women adjust their diet structure and optimize the management of blood glucose and blood lipids, thus effectively improving the adverse pregnancy outcome and maintaining the health of pregnant women and newborns.

BEAN: Brain Extraction and Alignment Network for 3D Fetal Neurosonography

Brain extraction (masking of extra-cerebral tissues) and alignment are fundamental first steps of most neuroimage analysis pipelines. The lack of automated solutions for 3D ultrasound (US) has therefore limited its potential as a neuroimaging modality for studying fetal brain development using routinely acquired scans. In this work, we propose a convolutional neural network (CNN) that accurately and consistently aligns and extracts the fetal brain from minimally pre-processed 3D US scans. Our multi-task CNN, Brain Extraction and Alignment Network (BEAN), consists of two independent branches: 1) a fully-convolutional encoder-decoder branch for brain extraction of unaligned scans, and 2) a two-step regression-based branch for similarity alignment of the brain to a common coordinate space. BEAN was tested on 356 fetal head 3D scans spanning the gestational range of 14 to 30 weeks, significantly outperforming all current alternatives for fetal brain extraction and alignment. BEAN achieved state-of-the-art performance for both tasks, with a mean Dice Similarity Coefficient (DSC) of 0.94 for the brain extraction masks, and a mean DSC of 0.93 for the alignment of the target brain masks. The presented experimental results show that brain structures such as the thalamus, choroid plexus, cavum septum pellucidum, and Sylvian fissure, are consistently aligned throughout the dataset and remain clearly visible when the scans are averaged together. The BEAN implementation and related code can be found under www.github.com/felipemoser/kelluwen.

Variational mode decomposition based automated fetal PCG denoising and extraction

Congenital cardiac anomalies of fetus are often characterized by the unprecedented changes in the auditory properties of cardiac sounds occurs during the gestation period of pregnant women. These abnormalities are often seen in inconsistent patterns of heart sounds that are driven by asynchronous variations in heart rates of mother and fetus. This hostile situation becomes severe if it is untreated and might threaten to life risk in pregnant women. In this work, we proposed a novel and automated signal processing paradigm using Variational Mode Decomposition (VMD) to detect and extract the mother and fetus heart sounds from the raw PCG signals recorded from outer surface of the maternal abdomen. The proposed framework constitutes a couple of cascaded VMD blocks: The first VMD block alleviates the raspatory noises and other artifacts from the raw PCG signal; while the Next one, decomposes the mother and fetus heart sounds obtained from its preceding block. A publicly available Shiraz University Fetal Heart Sounds Database is used to test the efficacy of the proposed model. In addition, noisy PCG corpus characterized by the additive white gaussian noise is used to test the efficacy of the proposed network. F

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

Pharmacokinetics and Tolerability of Intraperitoneal Chloroprocaine After Fetal Extraction in Women Undergoing Cesarean Delivery.

Intraperitoneal chloroprocaine has been used during cesarean delivery to supplement suboptimal neuraxial anesthesia for decades. The short in vitro half-life of chloroprocaine (11-21 seconds) has been cited to support the safety of this approach. However, there are no data regarding the rate of absorption, representing patient drug exposure, through this route of administration. Accordingly, we designed a study to determine the in vivo half-life of intraperitoneal chloroprocaine and assess clinical tolerability. We designed a single-center, prospective, cohort, multiple-dose escalation study of women 18 to 50 years of age undergoing cesarean delivery with spinal anesthesia. Chloroprocaine (40 mL) was administered after delivery of the newborn and before uterine closure. The first cohort (n = 5) received 1%, the second cohort (n = 5) received 2%, and the third cohort (n = 5) received 3% chloroprocaine solution. Maternal blood samples were obtained before administration and 1, 5, 10, 20, and 30 minutes after dosing. The primary objective was to define the pharmacokinetic profile of intraperitoneal chloroprocaine, including in vivo half-life. The secondary objective was to evaluate tolerability through determination of peak plasma concentration and prospective assessment for local anesthetic systemic toxicity. The peak plasma concentration occurred 5 minutes after intraperitoneal administration in all 3 cohorts: 64.8 ng/mL (6.5 µg/kg), 28.7 ng/mL (2.9 µg/kg), and 799.2 ng/mL (79.9 µg/kg) for 1%, 2%, and 3% chloroprocaine, respectively. The in vivo half-life of chloroprocaine after intraperitoneal administration was estimated to be 5.3 minutes (95% confidence interval, 4.0-6.6). We did not detect clinical signs of local anesthetic systemic toxicity in any of the 3 cohorts. The in vivo half-life of intraperitoneal chloroprocaine (5.3 minutes) is more than an order of magnitude greater than the in vitro half-life (11-21 seconds). However, maximum plasma concentrations of chloroprocaine (C max range, 0.05-79.9 µg/kg) were not associated with local anesthetic systemic toxicity and remain well below our predefined safe level of exposure (970 µg/kg) and levels associated with clinical symptoms (2.6-2.9 mg/kg). Therefore, our study suggests that intraperitoneal chloroprocaine, in a dosage ≤1200 mg, administered after fetal extraction, is well tolerated during cesarean delivery.

Rudimentary Corneal Rupture of A Unicornuate Uterus in A 23-Week Pregnancy: About A Case

The rudimentary uterine horn belongs to the group of major uterine malformations according to the classification of Musset's classification. This rudimentary horn may have a cavity with endometrial lining of the uterus, and thus can serve as a site of implantation during pregnancy. The presence of a pregnancy in a rudimentary horn is a rare event, which can be revealed by a uterine rupture. It leads to an emergency fetal extraction and it is then recommended to perform an exeresis of this rudimentary horn [6, 7]. If it is not performed, it theoretically exposes the risk of recurrence of uterine rupture in case of a new pregnancy in this rudimentary horn. There is a need to increase awareness of this disease, especially in developing countries where the possibility of detection of the disease is limited. We report a case of ruptured rudimentary horn pregnancy in shock at 23 weeks of gestation misdiagnosed as intrauterine pregnancy. The pregnancy was located in a rudimentary left uterine horn ruptured on its posterior surface and communicating with the right horn. A hemi hysterectomy was then performed with the removal of the rudimentary horn and the homolateral adnexa. The fetus was deadborn. The postoperative course was simple and the patient was discharged on the fifth postoperative day.

Frequency-Based Maternal Electrocardiogram Attenuation for Fetal Electrocardiogram Analysis

Fetal electrocardiogram (ECG) waveform analysis along with cardiac time intervals (CTIs) measurements are critical for the management of high-risk pregnancies. Currently, there is no system that can consistently and accurately measure fetal ECG. In this work, we present a new automatic approach to attenuate the maternal ECG in the frequency domain and enhance it with measurable CTIs. First, the coherent components between the maternal ECG and abdominal ECG were identified and subtracted from the latter in the frequency domain. The residual was then converted into the time domain using the inverse Fourier transform to yield the fetal ECG. This process was improved by averaging multiple beats. Two fetal cardiologists, blinded to the method, assessed the quality of fetal ECG based on a grading system and measured the CTIs. We evaluated the fetal ECG quality of our method and time-based methods using one synthetic dataset, one human dataset available in the public domain, and 37 clinical datasets. Among the 37 datasets analyzed, the mean average (± standard deviation) grade was 3.49 ± 1.22 for our method vs. 2.64 ± 1.26 for adaptive interference cancellation (p-value < 0.001), thus showing the frequency-based fetal ECG extraction was the superior method, as assessed from our clinicians’ perspectives. This method has the potential for use in clinical settings.

Impact of “natural” cesarean delivery on peripartum blood loss: a randomized controlled trial

Early skin-to-skin contact after vaginal delivery increases milk production and may increase oxytocin release, leading to a reduction in the rate of postpartum hemorrhage. This study aimed to examine the effect of "natural" cesarean deliveries on perioperative maternal blood loss. This was a randomized controlled trial conducted at a single university-affiliated medical center, between August 2016 and February 2018. Term singleton pregnancies scheduled for a planned cesarean delivery under spinal anesthesia were included. Women were randomized at a ratio of 1:1 to natural cesarean delivery (study group) or traditional cesarean delivery (control group) during the routine preoperative assessment. Women in the study group watched fetal extraction, had early skin-to-skin contact, and breastfed until the end of surgery. Neonates in the control group were presented to the mother for a few minutes. Blood samples were drawn from all women, during fascia closure, to determine oxytocin levels using an enzyme-linked immunosorbent assay kit. The laboratory component was performed after recruitment completion and was accomplished in February 2019. The primary outcome was postpartum hemoglobin levels. To detect a difference of 0.5 g/dL between the groups with α=0.05 and β=80%, 214 women were needed. Of 214 randomized women, 23 were excluded. There was no difference in demographic and obstetrical variables between the groups. Postpartum hemoglobin levels were 10.1±1.1 and 10.3±1.3 g/dL in the study and control groups, respectively (P=.19). There was no difference in the rates of postpartum hemorrhage and blood transfusion. Maternal pain scores, satisfaction, and exclusive breastfeeding were comparable. Maternal oxytocin blood levels were 389.5±183.7 and 408.5±233.6 pg/mL in the study and control groups, respectively (P=.96). The incidence of neonatal hypothermia was comparable between the groups (P=.13). Natural cesarean delivery did not affect perioperative hemoglobin level or maternal oxytocin blood concentration.

Fetal Electrocardiogram Extraction from the Mother’s Abdominal Signal Using the Ensemble Kalman Filter

Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm.

654 RIsk factors for difficult foetal extraction in emergency caesarean section: a retrospective registry-based cohort study

Introduction and aims of the study: This study assessed risk factors for difficult foetal extraction in emergency caesarean sections, focusing on top-up epidural anaesthesia compared to spinal anaesthesia. Furthermore, the study addressed consequences of difficult foetal extraction on neonatal and maternal morbidity. Population: 2,332 of 2,892 emergency caesarean sections during 2010-2017. Methods: In this retrospective registry-based cohort study, we analysed main outcomes by crude and multiple adjusted logistic regression providing odds ratios (aORs).

Foetus Heartbeat Extraction

Fetal Electrocardiogram Extraction from Mother's Womb is the subject of this report, which includes a thorough examination and implementation. The Fetal ECG is made up of various thoracic and abdominal ECG signals from a pregnant lady. For estimating corrupted signals due to additive noise or interference, the Adaptive Noise Cancellation Technique is used. These algorithms are used to update the weights of an adaptive filter in the Adaptive Noise Cancellation system: The algorithms Least Mean Square, Normalized Least Mean Square, and Leaky Least Mean Square are utilized. This method can be used in both single-input, single-output (SISO), and multiple-input, single-output (MISO) systems (MISO). When compared to the LMS and LLMS algorithms, the NLMS algorithm performs better in both systems.

Deep learning fetal ultrasound video model match human observers in biometric measurements

Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos. Approach. We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated. Main results. We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model. Significance. We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries.

Placenta percreta with omental deciduosis- A rare case report

Placenta percreta is the most invasive form of placenta accreta spectrum (PAS) in which there is full thickness invasion of uterine wall by villous tissue through the serosa. Omental deciduosis is a benign transient condition uncommonly encountered during pregnancy. It can mimic granulomatous pathology and various metastatic carcinomas both grossly and microscopically. We report a case of 28 year Rh negative, female, primigravida who presented with amenorrhea since 7.5 months and absent fetal movements since 1 day. Ultrasonography depicted fetus with 33 weeks POG with intrauterine fetal death with breech presentation and severe oligohydramnios. Patient underwent laparotomy and subtotal hysterectomy along with extraction of fetus and omentectomy. Histopathology findings revealed placenta percreta and omental deciduosis.This case is reported as the concomitant existence of both placenta percreta and omental deciduosis in a single primigravida patient is an extremely rare occurrence.

Joint time-frequency analysis and non-linear estimation for fetal ECG extraction

This paper proposes a novel algorithm based on joint time–frequency analysis for extracting the fetal ECG using single-channel abdominal ECG. Stockwell transform is the time–frequency tool used in combination with Shannon Energy Entropy for identifying the maternal peaks. The single maternal beat is constructed as a Maximum likelihood estimator. The abdominal ECG free from the maternal component is again processed using S-transform to identify the fetal peaks. Masking and thresholding is done in the time–frequency domain to eliminate the high frequency and background noises. Inverse Stockwell transform combined with proposed region selective amplitude scaling (RSAS) is performed to denoise the FECG. Identifying maternal and fetal peaks in the time–frequency domain enables higher performance even in overlapping beats and further avoids the need for an explicit pre-processing approach. The proposed algorithm's performance is validated using Daisy database, Physionet Challenge 2013 Set-a dataset (PCDB), and Abdominal direct fetal ECG database (ABDFECGDB). Further to validate the consistency of proposed algorithm’s performance, results obtained using real-time recordings acquired using Powerlab data acquisition hardware and data from Non-Invasive Fetal ECG Arrhythmia Database are included. The obtained results show the proposed algorithm's superiority with97.37% of accuracy, 98.61% of sensitivity,98.72% of positive predictive value, and98.67% of F1 measure using PCDB database and 98.55% of accuracy, 99.16% of sensitivity,99.38% of positive predictive value, and99.27% of F1 measure using ABDFECGDB.Further, the algorithm shows consistent results using real-time data and arrhythmia dataset.

Maternal and neonatal outcomes associated with delivery techniques for impacted fetal head at cesarean section: a systematic review and meta-analysis.

Late first-stage or second-stage cesarean section is commonly associated with fetal head impaction, leading to maternal and neonatal complications. This situation requires safe delivery techniques, but the optimal management remains controversial. The aim of this meta-analysis was to compare maternal and neonatal outcomes associated with delivery techniques via cesarean section. An electronic search of three databases, from inception to June 2021, was conducted. Cohort and randomised comparative studies on maternal and neonatal outcomes associated with techniques to deliver an impacted fetal head during cesarean section were included. The methodological quality of the primary studies was assessed. Review Manager 5.4 was used for statistical analyses. Nineteen articles, including 2,345 women were analyzed. Three fetal extraction techniques were identified. Meta-analyses showed that the "pull" technique carries lower risks as compared to the "push" technique and the "Patwardhan" technique is safer compared to the "push" or the "push and pull" technique. In the absence of robust evidence to support the use of a specific technique, the choice of the obstetrician should be based on best available evidence. Our studysuggests that the "pull", as well as the "Patwardhan" technique represent safe options to deliver an impacted fetal head.

End-to-End Deep Learning Architecture for Separating Maternal and Fetal ECGs Using W-Net

Fetal cardiac monitoring and assessment during pregnancy play a critical role in the early detection of the potential risk of fetal cardiac problems, thus allowing for timely preventive measures and healthy births. It is necessary to continuously monitor the fetal heart for this purpose. Methods of fetal cardiac monitoring by extracting maternal and fetal electrocardiograms (ECGs) from maternal abdominal ECGs have been extensively investigated. However, the extraction of a clear fetal ECG is a major challenge because fetal signals are typically dominated by maternal ECG signals and noise. Most existing methods for fetal ECG extraction involve several steps, such as extracting and removing the maternal ECG and then extracting the fetal ECG. To address the complexity of this process, we propose a novel method for effectively decomposing a single-channel maternal abdominal ECG into a maternal ECG and fetal ECG without using multiple steps by employing an end-to-end deep learning network architecture using W-net. Model training is performed using a simulation dataset. Then, a fetal ECG is extracted from a real maternal abdominal ECG. The performance of the proposed architecture is compared with that of other state-of-the-art deep learning models on the basis of the detection of QRS complexes. The proposed model shows higher precision and recall values and F1 scores. This demonstrates that the proposed model can effectively extract a fetal ECG from a single-channel maternal abdominal ECG. The model is expected to contribute to commercial applications for long-term maternal and fetal monitoring.

Implementation of an Optimised Hybrid Algorithm Based on Independent Component Analysis and Empirical Mode Decomposition for Non-Invasive Fetal Electrocardiogram Signal Extraction and Processing

Implementation of an Optimised Hybrid Algorithm Based on Independent Component Analysis and Empirical Mode Decomposition for Non-Invasive Fetal Electrocardiogram Signal Extraction and Processing

A Fetal ECG Extraction Method Based on ELM Optimized by Improved PSO Algorithm.

The extraction of fetal electrocardiogram (FECG) is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of FECG, a FECG extraction method based on extreme learning machine (ELM) optimized by an improved particle swarm optimization (IPSO) was proposed (IPSO-ELM). First, according to the characteristics of the mixed signal on the maternal abdominal wall, and based on the global search ability of IPSO, the initial weight matrix and hidden layer bias of ELM were optimized to match with the mixed signal of the maternal abdominal wall and the network topology. Then, an ELM model was established using the optimal network parameters obtained by IPSO. The nonlinear transformation of the maternal ECG (MECG) signal to the abdominal wall was estimated by the IPSO-ELM network. Finally, the non-linearly transformed MECG signal was mixed with the abdominal wall subtract to obtain clear FECG. The experimental results of clinical ECG signals in DaISy dataset showed that, compared with the traditional normalized minimum mean square error, support vector machine method, and ELM neural network methods, the proposed method can extract clearer FECG signals and improve the signal-to-noise ratio of extracted FECG.