Fetal Signals Research Articles

Higher Order Spherical Harmonics Reconstruction of Fetal Diffusion MRI With Intensity Correction.

We present a novel method for higher order reconstruction of fetal diffusion MRI signal that enables detection of fiber crossings. We combine data-driven motion and intensity correction with super-resolution reconstruction and spherical harmonic parametrisation to reconstruct data scattered in both spatial and angular domains into consistent fetal dMRI signal suitable for further diffusion analysis. We show that intensity correction is essential for good performance of the method and identify anatomically plausible fiber crossings. The proposed methodology has potential to facilitate detailed investigation of developing brain connectivity and microstructure in-utero.

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography.

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

ST analysis of the fetal electrocardiogram - Comments on recent experimental data.

In their paper, Andriessen at al present a validation of fetal ECG analysis and the clinical STAN device in midgestation fetal lambs exposed to 25 minutes of umbilical cord occlusion. The study presents results that contrast remarkably from previously published experimental data which raises a number of questions and comments.The most striking finding of Andriessen et al is the recording of an extremely high number of alarms from the STAN equipment during control conditions when no alarms at all are expected. These patterns have never been seen, neither in the clinical situation nor in our own fetal sheep studies. The reason for this becomes apparent when their way of recording the FECG is scrutinized. In their assessment of STAN, Andriessen at al use an assumed negative aVF lead with the assumption that it will reflect the FECG in the same way as the unipolar scalp lead used clinically. The signal used for disqualification of STAN is itself not qualified to properly represent the fetal scalp lead signal that STAN is designed for. To question a proven technology is fully accepted but those attempting would be asked to argue along fully validated data and related analysis including questioning of their own data.

Human placental methylome in the interplay of adverse placental health, environmental exposure, and pregnancy outcome.

The placenta is the interface between maternal and fetal circulations, integrating maternal and fetal signals to selectively regulate nutrient, gas, and waste exchange, as well as secrete hormones. In turn, the placenta helps create the in utero environment and control fetal growth and development. The unique epigenetic profile of the human placenta likely reflects its early developmental separation from the fetus proper and its role in mediating maternal–fetal exchange that leaves it open to a range of exogenous exposures in the maternal circulation. In this review, we cover recent advances in DNA methylation in the context of placental function and development, as well as the interaction between the pregnancy and the environment.

Fetal androgen signaling defects affect pancreatic β-cell mass and function, leading to glucose intolerance in high-fat diet-fed male rats.

We previously demonstrated that androgen signaling expands pancreatic β-cell mass in the sexual maturation period (Am J Physiol Endocrinol Metab 314: E274-E286, 2018). The aim of this study was to elucidate whether fetal androgen signaling plays important roles in β-cell mass development and β-cell function in adulthood, defects of which are associated with type 2 diabetes mellitus. In the pancreas of male fetuses, androgen receptor (AR) was strongly expressed in the cytoplasm and at the cell membrane of Nkx6.1-positive β-cell precursor cells but was markedly reduced in insulin-positive β-cells. Administration of the anti-androgen flutamide to pregnant dams during late gestation reduced β-cell mass and Ki67-positive proliferating β-cells at birth in a male-specific manner without affecting body weight. The decrease of β-cell mass in flutamide-exposed male rats was not recovered when rats were fed a standard diet, whereas it was fully recovered when rats were fed a high-fat diet (HFD), at 6 and 12 wk of age. Flutamide exposure in utero led to the development of glucose intolerance in male rats due to a decrease in insulin secretion when fed HFD but not standard diet. Insulin sensitivity did not differ between the two groups irrespective of diet. These results indicated that the action of fetal androgen contributed to β-cell mass expansion in a sex-specific manner at birth and to the development of glucose intolerance by decreasing the secretion of insulin in HFD-fed male rats. Our data demonstrated the involvement of fetal androgen signaling in hypothesized sex differences in the developmental origins of health and disease by affecting pancreatic β-cell function.

Fully Automated Subtraction of Heart Activity for Fetal Magnetoencephalography Data.

Fetal magnetoencephalography (fMEG) is a method to record human fetal brain signals in pregnant mothers. Nevertheless the amplitude of the fetal brain signal is very small and the fetal brain signal is overlaid by interfering signals mainly caused by maternal and fetal heart activity. Several methods are used to attenuate the interfering signals for the extraction of the fetal brain signal. However currently used methods are often affected by a reduction of the fetal brain signal or redistribution of the fetal brain signal. To overcome this limitation we developed a new fully automated procedure for removal of heart activity (FAUNA) based on Principal Component Analysis (PCA) and Ridge Regression. We compared the results with an orthogonal projection (OP) algorithm which is widely used in fetal research. The analysis was performed on simulated data sets containing spontaneous and averaged brain activity. The new analysis was able to extract fetal brain signals with an increased signal to noise ratio and without redistribution of activity across sensors compared to OP. The attenuation of interfering heart signals in fMEG data was significantly improved by FAUNA and supports fully automated evaluation of fetal brain signal.

Automated Detection of Fetal Brain Signals with Principal Component Analysis.

Detection of fetal brain signals in fetal magnetoencephalographic recordings is - due to the low signal to noise ratio - challenging for researchers in this field. Up to now, state of the art is a manual evaluation of the signal. To make the evaluation more reproducible and less time consuming, an approach using Principal Component Analysis is introduced. Locations of the channels of most importance for the first three principal components are taken into account and their possibility of resembling brain activity evaluated. Data with auditory stimulation are taken for this analysis and trigger averaged signals from the channels selected as brain activity (manually & automatically) compared. Comparisons are done with regard to their average baseline activity, activity during a window of interest and timing and amplitude of their highest auditory event-related peak. The number of evaluable data sets showed to be lower for the automated compared to manual approach but auditory event-related peaks did not differ significantly in amplitude or timing and in both cases there was a significant activity change following the tone event. The given results and the advantage of reproducibility make this method a valid alternative.

Influence of averaged fetal heart rate in heart rate variability analysis.

In high-income countries, fetal hypoxia affects 3 to 8 newborns per 1000 live births with subsequent moderate or severe Hypoxic-Ischemic Encephalopathy (HIE) in 0.5 to 1 per 1000 live births. Visual interpretation of FHR signal issued from a Doppler ultrasound cardiotocography is the gold standard to monitor fetal condition. Unfortunately, its analysis presents a high rate of inter-observer variability and a low specificity to predict poor neonatal outcomes. Under hypoxia, the fetus develops several adaptive mechanisms regulated by the autonomic nervous system inducing changes in the fetal heart rate variability. Though fetal heart rate variability methods demonstrated abilities to predict perinatal asphyxia, most of the Doppler ultrasound technologies used in clinical practice do not provide sufficiently accurate fetal heart rate signals for heart rate variability analysis. Indeed, Doppler ultrasound cardiotocography usually provides fetal heart rate values averaged over 2 or 3 beats which can constitute a limitation for spectral analysis. We developed a fetal heart rate variability analysis method: the Fetal Stress Index (FSI). The objective of this study was to investigate the influence of averaged fetal heart rate on this new index in order to check the feasibility of computing the FSI from the signal issued from Doppler ultrasound cardiotocography.

NEW METHOD EXPLOITING A HYBRID TECHNIQUES FOR FETAL CARDIAC SIGNAL EXTRACTION

According to WHO, 2.6 million babies die during pregnancy. Good monitoring during the prenatal period could provide a significant reduction of this mortality rate. This is possible by detection and extraction of the fetal electrocardiogram (FECG). Extraction of that information is complex due to other noise coming from the mother and within the fetus that drowns out the fetal heart signal. However, new technology and improved filtering technique have provided ways to more accurately and efficiently gather various electrical components regarding fetal heart condition. In this paper, we propose a new source separation filtering method exploiting linear and nonlinear filtering techniques. Our method is a non-invasive extraction technique, where the source signal is the cardiac electrical signal acquired by non-invasive electrodes to facilitate the collection of signals and reduce the cost of the acquisition system; it differs from other existing methods in minimizing the number of input signals and the simplicity of its implementation. The fetal heart signal is drowned out by the maternal electrocardiogram (MECG). The problem that arises is the exact knowledge of the MECG signal affecting the chosen measuring electrode, since the MECG is dependent on the position of the electrode and the type of tissue that goes through. Therefore, its knowledge can be made only by a mathematical estimation. A DWT decomposition with adaptive thresholding based on an LMS filter is applied to extract the fetal signal. So first we extract the QRS complex of the FECG and detect the fetal heart rate (FHR).

Primary Jejunal Intussusception in Pregnancy: Clinical Challenges in Management

Intussusception is a common cause of abdominal pain in paediatric age group but infrequent in adults. We reported a 29-year-old lady, Gravida 2 para 1 with no known medical illness presented to the hospital at 35 weeks of gestations with complaint of abdominal pain and persistent nausea and vomiting. She had bilious vomiting more than 5 times a day which aggravated by food intake and epigastric pain. She also complained of had not passing flatus with no bowel opening 3 days prior to presentation. At presentation, she was dehydrated however not in shock and her vital signs were within normal range. Abdominal examination revealed mild tenderness over epigastric region without guarding. There was an evidence of electrolytes imbalance and kidney injury on her blood investigation. She was induced for labour due to lack of evidence of fetal signals. On day 2 post partum, she still complaining of persistent bilious vomiting, otherwise no abdominal pain. Radiologically, there is evidence to support a small bowel obstruction. An exploratory laparotomy was performed, intraoperatively noted presence of jejuno-jejunal intussusception closely related to duodenal-jejunal junction with 130cm of bowel involved in the intussusception were noted to be gangrene. Conclusion: Adult bowel intussusception is an uncommon and challenging condition for the surgeon. Intussusception in pregnancy is rare and difficult to diagnose. Preoperative diagnosis is usually missed or delayed. Abdominal CT is considered as the most sensitive imaging modality in the diagnosis of intussusception. However in pregnancy, the choice of imaging modality is ultrasonography.

Is portable foetal electrocardiogram monitor feasible for foetal heart rate monitoring of small for gestational age foetuses in the home environment

Small-for-gestational-age (SGA) foetuses are at greater risk of complications than normal foetuses. Studies have demonstrated that foetal electrocardiogram (ECG) device (Monica-AN24) can be employed for monitoring foetal heart rate (FHR). However, its reliability and acceptability has not been assessed in SGA foetuses. This study was aimed at: (a) determining the acceptability of wearing the device more than once, (b) investigating the reliability of acquiring successful FHR data at different gestation. Patients with singleton non-anomalous foetus >24 weeks gestation and an estimated foetal weight below tenth percentile were recruited. Thirty-five women wore the device once. Twenty-four of these wore second time (68.6%). Overall, success-rate of FHR signals was 48.6% and increased to 68.8% beyond 34 weeks gestation. Foetal ECG is a promising method for monitoring SGA foetus >34 weeks gestation. Compared to day recordings, foetal signals were more reliable at night. Further studies are required before its clinical utility can be ascertained.IMPACT STATEMENTWhat is already known on this subject? Pregnancies that are affected by small-for-gestational-age (SGA) foetus pose a major public health problem and are associated with increased perinatal morbidity and mortality. There is no consensus on the optimal timing of delivery. Current methods employed for monitoring SGA foetuses include ultrasound scans for foetal-biometry and Doppler assessments. However, they provide a snapshot of information on foetal well-being, restrict patient mobility and cannot be employed over long-time periods. It is plausible that ambulatory foetal-ECG device that enable monitoring over a longer time period may better inform decisions about the timing of delivery.What do the results of this study add? Trans-abdominal foetal-ECG is a promising method of monitoring SGA foetus in the home setting with a success rate of acquiring reliable foetal heart rate (FHR) data over 90% at night time.What are the implications of these findings for clinical practice and/or further research? Overall, the study approved the concept of long-term home monitoring and has highlighted the facilitators and barriers to wearing the monitor in clinical care. These observations can be used to undertake robust research to assess the use of foetal-ECG monitor singly or in conjunction with current monitoring techniques for optimal foetal surveillance.

Feasibility of Non-invasive Fetal Electrocardiographic Interval Measurement in the Outpatient Clinical Setting.

Non-invasive fetal electrocardiography (ECG) is a promising method for evaluating fetal cardiac electrical activity. Despite advances in fetal ECG technology, its ability to provide reliable, interpretable results in a typical outpatient fetal cardiology setting remains unclear. We sought to determine the feasibility of measuring standard ECG intervals in an outpatient fetal cardiology practice using an abdominal fetal ECG device that employs blind source separation with reference, an innovative signal-processing technique for fetal ECG extraction. Women scheduled for clinically indicated outpatient fetal echocardiogram underwent 10min of fetal ECG acquisition from the maternal abdomen using specialized gel electrodes. A bedside laptop computer performed fetal ECG extraction, allowing real-time visualization of fetal and maternal ECG signals. Offline post-processing of 1min of recorded data yielded fetal P-wave duration, PR interval, QRS duration, RR interval, QT interval, and QTc. Fifty-five fetuses were studied with gestational age 18-37weeks, including 13 with abnormal fetal echocardiogram findings and three sets of twins. Interpretable results were obtained in 91% of fetuses, including 85% during the vernix period and 100% of twin fetuses. PR interval and RR interval of 18-24week gestation fetuses were significantly shorter than those with gestational age 25-31 and 32-37weeks. Of the six fetuses with abnormal rhythms on fetal echocardiogram, fetal ECG tracing was interpretable in five and matched the rhythm noted on fetal echocardiogram. Abdominal fetal ECG acquisition is feasible for arrhythmia detection and ECG interval calculation in a routine clinical setting.

Human placental GLUT1 glucose transporter expression and the fetal insulin-like growth factor axis in pregnancies complicated by diabetes

We have previously described regulation of syncytial GLUT1 glucose transporters by IGF-I. Despite this, it is not clear what signal regulates transplacental glucose transport. In this report we asked whether changes in GLUT1 expression and glucose transport activity in diabetic pregnancies were associated with alterations in the fetal IGF axis. Cord blood samples and paired syncytial microvillous and basal membranes were isolated from normal term pregnancies and pregnancies characterized by gestational diabetes type A2 (GDM A2) and pre-existing insulin-dependent diabetes mellitus (IDDM). Circulating IGF-I, basal membrane GLUT1 expression and glucose transporter activity were correlated with birth weight, but only in control, not diabetic groups. Basal membrane GLUT1 and transporter activity were correlated with IGF-I concentrations in control, but not diabetic groups. IGF binding protein (IGFBP) binding capacity showed a ≥50% reduction in the diabetic groups compared to control; both showed a higher level of free IGF-I. The absence of a correlation between birth weight and factors such as fetal IGF-I or GLUT1 expression in the diabetic groups suggests that IGF-I-stimulated effects may have reached a limiting threshold, such that further increases in IGF-I (or GLUT1) are without effect. These data support that fetal IGF-I acts as a fetal nutritional signal, modulating placental GLUT1 expression and birth weight via altered levels of fetal circulating IGFBPs. Diabetes appears to exert its effects on fetal and placental factors prior to the third trimester and, despite good glycemic control immediately prior to, and in the third trimester, these effects persist to term.

Anti-disturbance Fetal Heart Rate System Based on Combined Maternal-fetal Monitoring

The existing fetal monitor is prone to false detection in the case of high maternal interference in the second stage of labor. With this background, the article designs and implements a combined maternal-fetal fetal heart monitoring system. The system obtains the Doppler signal of the abdominal fetal heart and the blood oxygen signal of the mother's finger, and estimates the maternal interference degree in the fetal heart rate Doppler signal according to the maximum correlation value between the maternal finger blood oxygen signal and the abdominal fetal heart Doppler signal, and switches the fetal heart rate extraction algorithm between the autocorrelation method suitable for lower interference and improved template method suitable for higher interference according to the maternal interference degree. The accuracy of our method is 9.2% which is higher than that of the improved template matching method and 6.1% higher than that of the autocorrelation method.

Evaluating Complexity of Fetal MEG Signals: A Comparison of Different Metrics and Their Applicability.

In this work, we aim to investigate whether information based metrics of neural activity are a useful tool for the quantification of consciousness before and shortly after birth. Neural activity is measured using fetal magnetoencephalography (fMEG) in human fetuses and neonates. Based on recent theories on consciousness, information-based metrics are established to measure brain complexity and to assess different levels of consciousness. Different metrics (measures of entropy, compressibility and fractality) are, thus, explored in a reference population and their usability is evaluated. For comparative analysis, two fMEG channels were selected: one where brain activity was previously detected and one at least 15 cm away, that represented a control channel. The usability of each metric was evaluated and results from the brain and control channel were compared. Concerning the ease of use with fMEG data, Lempel-Ziv-Complexity (LZC) was evaluated as best, as it is unequivocal and needs low computational effort. The fractality measures have a high number of parameters that need to be adjusted prior to analysis and therefore forfeit comparability, while entropy measures require a higher computational effort and more parameters to adjust compared to LZC. Comparison of a channel with brain activity and a control channel in neonatal recordings showed significant differences in most complexity metrics. This clear difference can be seen as proof of concept for the usability of complexity metrics in fMEG. For fetal data, this comparison produced less clear results which can be related to leftover maternal signals included in the control channel. Further work is necessary to conclusively interpret results from the analysis of fetal recordings. Yet this study shows that complexity metrics can be used for fMEG data on early consciousness and the evaluation gives a guidance for future work. The inconsistency of results from different metrics highlights the challenges of working with complexity metrics as neural correlates of consciousness, as well as the caution one should apply to interpret them.

Fetal electrocardiogram modeling using hybrid evolutionary firefly algorithm and extreme learning machine

Extraction of fetal electrocardiogram (FECG) from the abdominal region of the mother’s skin is challenge task due to the high overlapping of maternal and fetal signals in this area. To overcome the problem, this paper proposes the utilization of extreme learning model (ELM) as the prediction algorithm to train on the FECG signal extracted by least mean square approach from the input abdominal and thoracic signals. The trained ELM model is used to model the FECG signal for the testing samples. Also, this paper investigates the firefly algorithm (FA) to tune the parameters of ELM and improve its performance. Due to the high complexity and too many parameters of FA, this paper embeds the evolutionary scheme into the FA algorithm which benefits from adaptive crossover and mutation probabilities. Evaluation of the proposed method on both synthetic and actual datasets proves its qualification in FECG extraction and modeling.

Alterations in Maternal-Fetal Heart Rate Coupling Strength and Directions in Abnormal Fetuses.

Because fetal gas exchange takes place via the maternal placenta, there has been growing interests in investigating the patterns and directions of maternal-fetal cardiac coupling to better understand the mechanisms of placental gas transfer. We recently reported the evidence of short-term maternal–fetal cardiac couplings in normal fetuses by using Normalized Short Time Partial Directed Coherence (NSTPDC) technique. Our results have shown weakening of coupling from fetal heart rate to maternal heart rate as the fetal development progresses while the influence from maternal to fetal heart rate coupling behaves oppositely as it shows increasing coupling strength that reaches its maximum at mid gestation. The aim of this study is to test if maternal-fetal coupling patterns change in various types of abnormal cases of pregnancies. We applied NSTPDC on simultaneously recorded fetal and maternal beat-by-beat heart rates collected from fetal and maternal ECG signals of 66 normal and 19 abnormal pregnancies. NSTPDC fetal-to-maternal coupling analyses revealed significant differences between the normal and abnormal cases (normal: normalized factor (NF) = −0.21 ± 0.85, fetus-to-mother coupling area (A_fBBI→ mBBI) = 0.44 ± 0.13, mother-to-fetus coupling area (A_mBBI→ fBBI) = 0.46 ± 0.12; abnormal: NF = −1.66 ± 0.77, A_fBBI→ mBBI = 0.08 ± 0.12, A_mBBI→ fBBI = 0.66 ± 0.24; p < 0.01). In conclusion, maternal-fetal cardiac coupling strength and direction and their associations with regulatory mechanisms (patterns) of developing autonomic nervous system function could be novel clinical markers of healthy prenatal development and its deviation. However, further research is required on larger samples of abnormal cases.

Fetal lung C4BPA induces p100 processing in human placenta

The non-canonical NF-κB signaling may be a central integrator of a placental clock that governs the length of human pregnancy. We sought to identify fetal signals that could activate this NF-κB pathway in the placenta, and in turn, contribute to the onset of labor. Proteomics analysis of exosomes purified from fetal cord arterial blood revealed a total of 328 proteins, among which 48 were more significantly abundant (p < 0.01) in samples from women who delivered following elective Cesarean-section at term (39 to 40 weeks of estimated gestational age, EGA) compared to those who had elective Cesarean deliveries near term (35 to 36 weeks of EGA). Computational, crystal structural, and gene functional analyses showed that one of these 48 proteins, C4BPA, binds to CD40 of placental villous trophoblast to activate p100 processing to p52, and in turn, pro-labor genes. These results suggest that fetal C4BPA-induced activation of non-canonical NF-κB in human placenta may play a critical role in processes of term or preterm labor.

SQUID Gradiometer Module for Fetal Magnetocardiography Measurements Inside a Thin Magnetically Shielded Room

Gradiometer module plays a significant role in fetal magnetocardiography (fMCG) system and its performance determines the signal qualities directly. In order to obtain the best signal-to-noise ratio (SNR) of the fMCG signals in a thin magnetically shielded room (tMSR), the baseline of the axial gradiometers need to be optimized. In this paper, direct current superconducting quantum interference devices which is composed of weakly damped Josephson Junctions with βc > 3 and has a flux-to-voltage transfer coefficient of 500 μ V/Φ0 is employed for our gradiometers. Superconducting connectivity technique is utilized to connect input coil and pickup coil. First-order gradiometers with different baselines are thus developed. After the evaluation of the magnetic flux density and gradient flux density inside the tMSR, measurement of fMCG signals are taken with the gradiometers mentioned earlier. The simulated fetal QRS peak signals about 2.5 pT could easily be identified in the realtime gradiometer module outputs with an SNR larger than 11 dB.

Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data

Background and Objective: Fetal magnetoencephalography (fMEG) is a method for recording fetal brain signals, fetal and maternal heart activity simultaneously. The identification of the R-peaks of the heartbeats forms the basis for later heart rate (HR) and heart rate variability (HRV) analysis. The current procedure for the evaluation of fetal magnetocardiograms (fMCG) is either semi-automated evaluation using template matching (SATM) or Hilbert transformation algorithm (HTA). However, none of the methods available at present works reliable for all datasets.Methods: Our aim was to develop a unitary, responsive and fully automated R-peak detection algorithm (FLORA) that combines and enhances both of the methods used up to now.Results: The evaluation of all methods on 55 datasets verifies that FLORA outperforms both of these methods as well as a combination of the two, which applies in particular to data of fetuses at earlier gestational age.Conclusion: The combined analysis shows that FLORA is capable of providing good, stable and reproducible results without manual intervention.