Evaluation and patient experience of wireless noninvasive fetal heart rate monitoring devices

INTRODUCTION

In clinical practice, fetal heart rate monitoring is performed intermittently using Doppler ultrasound, typically for 30 minutes. In case of a non-reassuring heart rate pattern, monitoring is usually prolonged. Noninvasive fetal electrocardiography may be more suitable for prolonged monitoring due to improved patient comfort and signal quality. This study evaluates the performance and patient experience of four noninvasive electrocardiography devices to assess candidate devices for prolonged noninvasive fetal heart rate monitoring.

MATERIAL AND METHODS

Non-critically sick women with a singleton pregnancy from 24 weeks of gestation were eligible for inclusion. Fetal heart rate monitoring was performed during standard care with a Doppler ultrasound device (Philips Avalon-FM30) alone or with this Doppler ultrasound device simultaneously with one of four noninvasive electrocardiography devices (Nemo Fetal Monitoring System, Philips Avalon-Beltless, Demcon Dipha-16 and Dräger Infinity-M300). Performance was evaluated by: success rate, positive percent agreement, bias, 95% limits of agreement, regression line, root mean square error and visual agreement using FIGO guidelines. Patient experience was captured using a self-made questionnaire.

RESULTS

A total of 10 women were included per device. For fetal heart rate, Nemo performed best (success rate: 99.4%, positive percent agreement: 94.2%, root mean square error 5.1 BPM, bias: 0.5 BPM, 95% limits of agreement: -9.7 - 10.7 BPM, regression line: y = -0.1x + 11.1) and the cardiotocography tracings obtained simultaneously by Nemo and Avalon-FM30 received the same FIGO classification. Comparable results were found with the Avalon-Beltless from 36 weeks of gestation, whereas the Dipha-16 and Infinity-M300 performed significantly worse. The Avalon-Beltless, Nemo and Infinity-M300 closely matched the performance of the Avalon-FM30 for maternal heart rate, whereas the performance of the Dipha-16 deviated more. Patient experience scores were higher for the noninvasive electrocardiography devices.

CONCLUSIONS

Both Nemo and Avalon-Beltless are suitable devices for (prolonged) noninvasive fetal heart rate monitoring, taking their intended use into account. But outside its intended use limit of 36 weeks' gestation, the Avalon-Beltless performs less well, comparable to the Dipha-16 and Infinity-M300, making them currently unsuitable for (prolonged) noninvasive fetal heart rate monitoring. Noninvasive electrocardiography devices appear to be preferred due to greater comfort and mobility.

Fetal Heart Rate Auscultation, 4th Edition

Intermittent auscultation (IA) is an evidence-based method of fetal surveillance during labor for birthing people with low-risk pregnancies. It is a central component of efforts to reduce the primary cesarean rate and promote vaginal birth (American College of Obstetricians and Gynecologists, 2019; Association of Women's Health, Obstetric and Neonatal Nurses, 2022a). The use of intermittent IA decreased with the introduction of electronic fetal monitoring, while the increased use of electronic fetal monitoring has been associated with an increase of cesarean births. This practice monograph includes information on IA techniques; interpretation and documentation; clinical decision-making and interventions; communication; education, staffing, legal issues; and strategies to implement IA.

ST waveform analysis vs cardiotocography alone for intrapartum fetal monitoring: An updated systematic review and meta-analysis of randomized trials

INTRODUCTION

ST waveform analysis (STAN) was introduced as an adjunct to cardiotocography (CTG) to improve neonatal and maternal outcomes. The aim of the present study was to quantify the efficacy of STAN vs CTG and assess the quality of the evidence using GRADE.

MATERIAL AND METHODS

We performed systematic literature searches to identify randomized controlled trials and assessed included studies for risk of bias. We performed meta-analyses, calculating pooled risk ratio (RR) or Peto odds ratio (OR). We also performed post hoc trial sequential analyses for selected outcomes to assess the risk of false-positive results and the need for additional studies.

RESULTS

Nine randomized controlled trials including 28 729 women were included in the meta-analysis. There were no differences between the groups in operative deliveries for fetal distress (10.9 vs 11.1%; RR 0.96; 95% confidence interval [CI] 0.82-1.11). STAN was associated with a significantly lower rate of metabolic acidosis (0.45% vs 0.68%; Peto OR 0.66; 95% CI 0.48-0.90). Accordingly, 441 women need to be monitored with STAN instead of CTG alone to prevent one case of metabolic acidosis. Women allocated to STAN had a reduced risk of fetal blood sampling compared with women allocated to conventional CTG monitoring (12.5% vs 19.6%; RR 0.62; 95% CI 0.49-0.80). The quality of the evidence was high to moderate.

CONCLUSIONS

Absolute effects of STAN were minor and the clinical significance of the observed reduction in metabolic acidosis is questioned. There is insufficient evidence to state that STAN as an adjunct to CTG leads to important clinical benefits compared with CTG alone.

Intrapartum fetal heart rate monitoring: Rationalise, refine or replace?

Monitoring the fetal heartbeat underpins assessment of fetal wellbeing in labour. Although commonly employed in clinical practice, shortcomings remain. A recent review of clinical practice guidelines highlights the variation in definitions of the fetal heart rate that will lead to differences in interpretation. Will intrapartum care be improved by greater consensus around clinical practice guidelines through rationalisation or refinement of guidelines, or will the future see this technique replaced by more accurate forms of fetal monitoring?

Performance of a Maternal Abdominal Surface Electrode System for Fetal Heart Rate and Uterine Contraction Monitoring from 34 to 37 Weeks

OBJECTIVE

 The objective of this study was to compare performance of a maternal surface electrode patch with ultrasound- and tocodynamometer-based monitoring to detect fetal heart rate and uterine contractility in late preterm labors.

STUDY DESIGN

 Thirty women between 340/7 and 366/7 weeks' gestation were monitored simultaneously with a Doppler/tocodynamometer system and a wireless fetal-maternal abdominal surface electrode system. Fetal and maternal heart rate and uterine contraction data from both systems were compared. Reliability was measured by the success rate and percent agreement. Deming regression and Bland-Altman analysis estimated the concordance between the systems. Uterine contractions were assessed by visual interpretation of monitor tracings.

RESULTS

 The success rate for the surface electrode system was 89.5% (95% confidence interval [CI], 85.7-93.3), and for ultrasound it was 88.4% (95% CI, 84.9-91.9; p = 0.73), with a percent agreement of 88.1% (95% CI, 84.2-92.8). Results were uninfluenced by the patients' body mass. The mean Deming slope was 1 and the y-intercept was -3.0 beats per minute (bpm). Bland-Altman plots also showed a close relationship between the methods, with limits of agreement less than 10 bpm. The percent agreement for maternal heart rate was 98.2% (95% CI, 97.4-98.8), and for uterine contraction detection it was 89.5% (95% CI, 85.5-93.4).

CONCLUSION

 Fetal heart rate and uterine contraction monitoring at 340/7 to 366/7 weeks using abdominal surface electrodes was not inferior to Doppler ultrasound/tocodynamometry for fetal-maternal assessment.

REGISTRATION

 clinicaltrials.gov/February 20, 2017/identifier NCT03057275.

KEY POINTS

· Monitoring the preterm fetal heart rate with surface electrodes is feasible.. · Preterm contractions can be monitored with surface electrodes.. · The technique was noninferior to standard external monitors..

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities

Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.

Joint Improved Fast Independent Component Analysis and Singular Value Decomposition for Fetal Electrocardiogram Extraction

Combined the improved fast independent component analysis (FastICA) algorithm with the singular value decomposition algorithm, a single-channel fetal electrocardiogram (fECG) extraction method is proposed. First, the improved FastICA algorithm is used to estimate the maternal ECG component from a single-channel abdominal signal of pregnant women using an overrelaxation factor. Then, a preliminary estimate of the fECG signal is obtained by subtracting from the single-channel abdominal signal. Subsequently, the singular value decomposition algorithm is used to denoise the preliminarily estimated fECG signal to obtain a high signal-to-noise ratio. In addition, in the singular value decomposition algorithm for fetal arrhythmia, an improved method for constructing the ECG signal reconstruction matrix is proposed. Finally, the fECG extraction experiments on synthetic abdominal signals and actual abdominal signals (data from 49 abdominal channels sourced from DAISY database and the non-invasive fECG database in PhysioNet) are carried out. The experimental results show that the method in this paper can effectively improve the signal-to-noise ratio and the accuracy of fECG signal extraction, and is suitable for maternal or fetal arrhythmias. Compared with the FastICA algorithm, the signal-to-noise ratio of the fECG signal extracted by the method in this paper is improved by about 5 dB, and the accuracy of fECG extraction in the PhysioNet database can reach 96.54%.

Is the fetus fit for labor? Introducing fast-and-frugal trees (FFTrees) to simplify triage of women for STAN monitoring: An interobserver agreement comparison with traditional classification

INTRODUCTION

It is a shortcoming of traditional cardiotocography (CTG) classification table formats that CTG traces are frequently classified differently by different users, resulting in poor interobserver agreements. A fast-and-frugal tree (FFTree) flow chart may help provide better concordance because it is straightforward and has clearly structured binary questions with understandable "yes" or "no" responses. The initial triage to determine whether a fetus is suitable for labor when utilizing fetal ECG ST analysis (STAN) is very important, since a fetus with restricted capacity to respond to hypoxic stress may not generate STAN events and therefore may become falsely negative. This study aimed to compare physiology-focused FFTree CTG interpretation with FIGO classification for assessing the suitability for STAN monitoring.

MATERIAL AND METHODS

A retrospective study of 36 CTG traces with a high proportion of adverse outcomes (17/36) selected from a European multicenter study database. Eight experienced European obstetricians evaluated the initial 40 minutes of the CTG recordings and judged whether STAN was a suitable fetal surveillance method and whether intervention was indicated. The experts rated the CTGs using the FFTree and FIGO classifications at least 6 weeks apart. Interobserver agreements were calculated using proportions of agreement and Fleiss' kappa (κ).

RESULTS

The proportions of agreement for "not suitable for STAN" were for FIGO 47% (95% confidence interval [CI] 42%-52%) and for FFTree 60% (95% CI 56-64), ie a significant difference; the corresponding figures for "yes, suitable" were 74% (95% CI 71-77) and 70% (95% CI 67-74). For "intervention needed" the figures were 52% (95% CI 47-56) vs 58% (95% CI 54-62) and for "expectant management" 74% (95% CI 71-77) vs 72% (95% CI 69-75). Fleiss' κ agreement on "suitability for STAN" was 0.50 (95% CI 0.44-0.56) for the FIGO classification and 0.57 (95% CI 0.51-0.63) for the FFTree classification; the corresponding figures for "intervention or expectancy" were 0.53 (95% CI 0.47-0.59) and 0.57 (95% CI 0.51-0.63).

CONCLUSIONS

The proportion of agreement among expert obstetricians using the FFTree physiological approach was significantly higher compared with the traditional FIGO classification system in rejecting cases not suitable for STAN monitoring. That might be of importance to avoid false negative STAN recordings. Other agreement figures were similar. It remains to be shown whether the FFTree simplicity will benefit less experienced users and how it will work in real-world clinical scenarios.

A Novel Approach to Fetal ECG Extraction Using Temporal Convolutional Encoder-Decoder Network (TCED-Net)

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.

Deep learning with fetal ECG recognition

Objective.Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG recognition by using a deep learning strategy.Approach.A cross-domain consistent convolutional neural network (CDC-Net) is developed for the task of FECG recognition. The output of the ICA algorithm is used as input to the CDC-Net and the CDC-Net identifies which channel's signal is the target FECG.Main results.Signals from two databases are used to test the efficiency of the proposed method. The proposed deep learning method exhibits good performance on FECG recognition. Specifically, the Precision, Recall and F1-score of the proposed method on the ADFECGDB database are 91.69%, 91.37% and 91.52%, respectively. The Precision, Recall and F1-score of the proposed method on the Daisy database are 97.85%, 97.42% and 97.63%, respectively.Significance. This study is a proof of concept that the proposed method can automatically recognize the FECG signals in multi-channel ECG data. The development of FECG recognition technology contributes to automated FECG monitoring.

Prediction of fetal RR intervals from maternal factors using machine learning models

Previous literature has highlighted the importance of maternal behavior during the prenatal period for the upbringing of healthy adults. During pregnancy, fetal health assessments are mainly carried out non-invasively by monitoring fetal growth and heart rate (HR) or RR interval (RRI). Despite this, research entailing prediction of fHRs from mHRs is scarce mainly due to the difficulty in non-invasive measurements of fetal electrocardiogram (fECG). Also, so far, it is unknown how mHRs are associated with fHR over the short term. In this study, we used two machine learning models, support vector regression (SVR) and random forest (RF), for predicting average fetal RRI (fRRI). The predicted fRRI values were compared with actual fRRI values calculated from non-invasive fECG. fRRI was predicted from 13 maternal features that consisted of age, weight, and non-invasive ECG-derived parameters that included HR variability (HRV) and R wave amplitude variability. 156 records were used for training the models and the results showed that the SVR model outperformed the RF model with a root mean square error (RMSE) of 29 ms and an average error percentage (< 5%). Correlation analysis between predicted and actual fRRI values showed that the Spearman coefficient for the SVR and RF models were 0.31 (P < 0.001) and 0.19 (P < 0.05), respectively. The SVR model was further used to predict fRRI of 14 subjects who were not included in the training. The latter prediction results showed that individual error percentages were (≤ 5%) except in 3 subjects. The results of this study show that maternal factors can be potentially used for the assessment of fetal well-being based on fetal HR or RRI.

Intrapartum electronic fetal monitoring features associated with a clinical diagnosis of nonreassuring fetal status

BACKGROUND

Nonreassuring fetal status detected by continuous electronic fetal monitoring accounts for almost 1 in 4 primary cesarean deliveries. However, given the subjective nature of the diagnosis, there is a need to identify the electronic fetal monitoring patterns that are clinically considered nonreassuring.

OBJECTIVE

This study aimed to describe which electronic fetal monitoring features are most commonly associated with first-stage cesarean delivery for nonreassuring fetal status, and to evaluate the risk of neonatal acidemia following cesarean delivery for nonreassuring fetal status.

STUDY DESIGN

This was a nested case-control study in a prospectively collected cohort of patients with singleton pregnancies at ≥37 weeks' gestation, admitted in spontaneous labor or for induction of labor from 2010 to 2014 at a single tertiary care center. Patients with preterm pregnancies, multiple gestations, planned cesarean delivery, or nonreassuring fetal status in the second stage of labor were excluded. Cases were identified as having nonreassuring fetal status on the basis of what was documented in the operative note by the delivering physician. Controls were patients without nonreassuring fetal status within 1 hour of delivery. Cases were matched to controls in a 1:2 ratio by parity, obesity, and history of cesarean delivery. Electronic fetal monitoring data were abstracted by credentialed obstetrical research nurses for the 60 minutes before delivery. The primary exposure of interest was the incidence of high-risk category II electronic fetal monitoring features in the 60 minutes before delivery; in particular, the incidence of minimal variability, recurrent late decelerations, recurrent variable decelerations, tachycardia, and >1 prolonged deceleration were compared between groups. We also compared neonatal outcomes between cases and controls, including fetal acidemia (umbilical artery pH <7.1), other umbilical artery gas analytes, and neonatal and maternal outcomes.

RESULTS

Of the 8580 patients in the parent study, 714 (8.3%) underwent cesarean delivery for nonreassuring fetal status in the first stage of labor. Patients diagnosed with nonreassuring fetal status requiring cesarean delivery were more likely to have recurrent late decelerations, >1 prolonged deceleration, and recurrent variable decelerations compared with controls. More than 1 prolonged deceleration was associated with 6 times increased rate of nonreassuring fetal status diagnosis resulting in cesarean delivery (adjusted odds ratio, 6.73 [95% confidence interval, 2.47-8.33]). Rates of fetal tachycardia were similar between groups. Minimal variability was less common in the nonreassuring fetal status group compared with controls (adjusted odds ratio, 0.36 [95% confidence interval, 0.25-0.54]). Compared with control deliveries, cesarean delivery for nonreassuring fetal status was associated with nearly 7 times higher risk of neonatal acidemia (7.2% vs 1.1%; adjusted odds ratio, 6.93 [95% confidence interval, 3.83-12.54]). Composite neonatal morbidity and composite maternal morbidity were more likely among patients delivered for nonreassuring fetal status in the first stage (3.9% vs 1.1%; adjusted odds ratio, 5.70 [2.60-12.49]; and 13.3% vs 8.0%; adjusted odds ratio, 1.99 [1.41-2.80]).

CONCLUSION

Although multiple category II electronic fetal monitoring features have been traditionally linked to acidemia, the presence of recurrent late decelerations, recurrent variable decelerations, and prolonged decelerations seemed to concern obstetricians enough to surgically intervene for nonreassuring fetal status. A clinical intrapartum diagnosis of nonreassuring fetal status in the setting of these electronic fetal monitoring features is also associated with increased risk of acidemia, suggesting clinical validity to the diagnosis of nonreassuring fetal status.

Maternal mood and anxiety effects on the fetal nonstress test

BACKGROUND

Previous studies have suggested that acute mood states may influence levels of fetal activity. Because the fetal nonstress test relies on markers of fetal activity to suggest fetal wellbeing, its interpretation may be affected by maternal mood.

OBJECTIVE

This study sought to determine if there are differences in nonstress test characteristics between pregnant individuals with and without symptoms of mood disorder.

STUDY DESIGN

In this prospective cohort study, we recruited pregnant individuals undergoing nonstress test in the third trimester and compared the results of the nonstress test between pregnant individuals with scores above and below the cutoff values on validated screening questionnaires for depression and anxiety symptoms, the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7). Demographic information was collected from each participant at the time of recruitment, and medical information was extracted from the electronic medical record.

RESULTS

A total of 68 pregnant individuals were enrolled, 10 (15%) of which screened positive for perinatal mood disorders. There was no significant difference in means of time to reactivity (15.6 [4.8] minutes vs 15.0 [8.0] minutes, P=.77), number of accelerations (0.16/min [0.08] vs 0.16/min [0.10], P>.95), number of fetal movements (17.0 [14.7] vs 19.7 [20.4], P=.62), heart rate baseline (138.0 [7.5] bpm vs 139.2 [9.0] bpm, P=.67) or variability (8.5 [2.5] bpm vs 9.1 (4.3) bpm, P=.51) between pregnant individuals who screen positive for mood disorders and those who did not.

CONCLUSION

Fetal heart rate patterns are similar in pregnant individuals with and without symptoms of mood disorder. The results provide reassurance that acute symptoms of anxiety and depression do not have significant effects on the fetal nonstress test.

The Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise Detection

Monitoring the fetal heart rate (FHR) is common practice in obstetric care to assess the risk of fetal compromise. Unfortunately, human interpretation of FHR recordings is subject to inter-observer variability with high false positive rates. To improve the performance of fetal compromise detection, deep learning methods have been proposed to automatically interpret FHR recordings. However, existing deep learning methods typically analyse a fixed-length segment of the FHR recording after removing signal gaps, where the influence of this segment selection process has not been comprehensively assessed. In this work, we develop a novel input length invariant deep learning model to determine the effect of FHR segment selection for detecting fetal compromise. Using this model, we perform five times repeated five-fold cross-validation on an open-access database of 552 FHR recordings and assess model performance for FHR segment lengths between 15 and 60 minutes. We show that the performance after removing signal gaps improves with increasing segment length from 15 minutes (AUC = 0.50) to 60 minutes (AUC = 0.74). Additionally, we demonstrate that using FHR segments without removing signal gaps achieves superior performance across signal lengths from 15 minutes (AUC = 0.68) to 60 minutes (AUC = 0.76). These results show that future works should carefully consider FHR segment selection and that removing signal gaps might contribute to the loss of valuable information.

New Features for the Detection of Fetal QRS Complexes in Non-Invasive Fetal Electrocardiograms

Non-invasive fetal electrocardiography (NI-fECG) is a promising technique for continuous fetal heart rate (fHR) monitoring. However, the weak amplitude of the fetal electrocardiogram (fECG), and the presence of the dominant maternal ECG (mECG), makes it highly challenging to detect the fetal QRS (fQRS) complex, which is needed to obtain the fHR. This paper proposes a new method for automated fQRS detection from single-channel NI-fECG signals, without cancelling out the mECG. The proposed method leverages the different spectral behaviour exhibited by mECG and fECG signals. Fetal R-peaks are detected using a hybrid combination of k-means clustering with time and time-frequency features extracted from pre-processed NI-fECG recordings. The performance of our method is evaluated using real and synthetic signals from publicly available datasets, achieving a best of 96.3% sensitivity and 90.4% F1 score. The results obtained demonstrates the effectiveness of the proposed method for the detection of fQRS complexes with high sensitivity and low computational complexity.

Robust Fetal Heart Rate Tracking through Fetal Electrocardiography (ECG) and Photoplethysmography (PPG) Fusion. Annu Int Conf IEEE Eng Med Biol Soc 2023;2023:1-4. [PMID: 38083436 DOI: 10.1109/embc40787.2023.10341068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Fetal electrocardiogram (fECG) or photoplethysmogram (fPPG) devices are being developed for fetal heart rate (FHR) monitoring. However, deep tissue sensing is challenged by low fetal signal-to-noise ratio (SNR). Data quality is easily degraded by motion, or interference from maternal tissues and data losses can happen due to communication faults. In this paper, we propose to combine fECG and fPPG measurements in order to increase robustness against such dynamic challenges and increase FHR estimation accuracy. To the author's knowledge the fusion of two sensory data types (fECG, fPPG) has not been investigated for FHR tracking purposes in the literature. The proposed methods are evaluated on real-world data captured from gold-standard large pregnant animal experiments. A particle filtering algorithm with sensor fusion in the measurement likelihood, called KUBAI, is used to estimate FHR. Fusion of PPG&ECG data resulted in 36.6% improvement in root-mean-square-error (RMSE) and 20.3% improvement in R2 correlation between estimated and reference FHR values compared to single sensor-type (PPG-only or ECG-only) data. We demonstrate that using different types of sensory data improves the robustness and accuracy of FHR tracking.

Heterogeneity in management of category II fetal tracings: data from a multihospital healthcare system

BACKGROUND

Electronic fetal monitoring is widely used to identify and intervene in suspected fetal hypoxia and/or acidemia. Category II fetal heart rate tracings are the most common class of fetal monitoring in labor, and intrauterine resuscitation is recommended given the association of category II fetal heart rate tracings with fetal acidemia. However, limited published data are available to guide intrauterine resuscitation technique selection, leading to heterogeneity in the response to category II fetal heart rate tracings.

OBJECTIVE

This study aimed to characterize approaches to intrauterine resuscitation in response to category II fetal heart rate tracings.

STUDY DESIGN

This was a survey study administered to labor unit nurses and delivering clinicians (physicians and midwives) across 7 hospitals in a Midwestern healthcare system spanning 2 states. The survey posed 3 category II fetal heart rate tracing scenarios (recurrent late decelerations, minimal variability, and recurrent variable decelerations) and asked participants to select first- and second-line intrauterine resuscitation management strategies. The participants were asked to quantify the level of influence certain factors have on their choice using a scale from 1 to 5. Intrauterine resuscitation strategy selection was compared by clinical role and hospital type (nurses vs delivering clinicians and university-affiliated hospital vs non-university-affiliated hospital).

RESULTS

Of 610 providers invited to take the survey, 163 participated (response rate of 27%): 37% of participants from university-affiliated hospitals, 62% of nurses, and 37% of physicians. Maternal repositioning was the most selected first-line strategy, regardless of the type of category II fetal heart rate tracing. First-line management varied by clinical role and hospital affiliation for each fetal heart rate tracing scenario, particularly for minimal variability, which was associated with the most heterogeneity in the first-line approach. Previous experience and recommendations from professional societies were the most influential factors in intrauterine resuscitation selection overall. Of note, 16.5% of participants reported that published evidence did not influence their choice at all. Participants from a university-affiliated hospital were more likely than participants from a non-university-affiliated hospital to consider patient preference when selecting an intrauterine resuscitation technique. Nurses and delivering clinicians differed significantly in the rationale for management choices: nurses were more often influenced by advice from other healthcare providers on the team (P<.001), whereas delivering clinicians were more influenced by literature (P=.02) and ease of technique (P=.02).

CONCLUSION

There was significant heterogeneity in the management of category II fetal heart rate tracing. In addition, motivations for choice in intrauterine resuscitation technique varied by hospital type and clinical role. These factors should be considered when creating fetal monitoring and intrauterine resuscitation protocols.

Independent component analysis algorithms for non-invasive fetal electrocardiography

The independent component analysis (ICA) based methods are among the most prevalent techniques used for non-invasive fetal electrocardiogram (NI-fECG) processing. Often, these methods are combined with other methods, such adaptive algorithms. However, there are many variants of the ICA methods and it is not clear which one is the most suitable for this task. The goal of this study is to test and objectively evaluate 11 variants of ICA methods combined with an adaptive fast transversal filter (FTF) for the purpose of extracting the NI-fECG. The methods were tested on two datasets, Labour dataset and Pregnancy dataset, which contained real records obtained during clinical practice. The efficiency of the methods was evaluated from the perspective of determining the accuracy of detection of QRS complexes through the parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). The best results were achieved with a combination of FastICA and FTF, which yielded mean values of ACC = 83.72%, SE = 92.13%, PPV = 90.16%, and F1 = 91.14%. Time of calculation was also taken into consideration in the methods. Although FastICA was ranked to be the sixth fastest with its mean computation time of 0.452 s, it had the best ratio of performance and speed. The combination of FastICA and adaptive FTF filter turned out to be very promising. In addition, such device would require signals acquired from the abdominal area only; no need to acquire reference signal from the mother's chest.

Morphology extraction of fetal ECG using temporal CNN-based nonlinear adaptive noise cancelling

OBJECTIVES

Noninvasive fetal electrocardiography (FECG) offers many advantages over alternative fetal monitoring techniques in evaluating fetal health conditions. However, it is difficult to extract a clean FECG signal with morphological features from an abdominal ECG recorded at the maternal abdomen; the signal is usually contaminated by the maternal ECG and various noises. The aim of the work is to extract an FECG signal that preserves the morphological features from the mother's abdominal ECG recording, which allows for accurately estimating the fetal heart rate (FHR) and analyzing the waveforms of the fetal ECG.

METHODS

We propose a novel nonlinear adaptive noise cancelling framework (ANC) based on a temporal convolutional neural network (CNN) to effectively extract fetal ECG signals from mothers' abdominal ECG recordings. The proposed framework consists of a two-stage network, using the ANC architecture; one network is for the maternal ECG component elimination and the other is for the residual noise component removal of the extracted fetal ECG signal. Then, JADE (one of the blind source separation algorithms) is applied as a postprocessing step to produce a clean fetal ECG signal.

RESULTS

Synthetic ECG data (FECGSYNDB) and clinical ECG data (NIFECGDB, PCDB) are used to evaluate the extraction performance of the proposed framework. The statistical and visual results demonstrate that our method outperforms the other state-of-the-art algorithms in the literature. Specifically, on the FECGSYNDB, the mean squared error (MSE), signal-to-noise ratio (SNR), correlation coefficient (R) and F1-score of our method are 0.16, 7.94, 0.95 and 98.89%, respectively. The F1-score on the NIFECGDB reaches 98.62%. The value of the F1-score on the PCDB is 98.62%.

CONCLUSION

As opposed to the existing algorithms being restricted to fetal QRS complex detection, the proposed framework can preserve the morphological features of the extracted fetal ECG signal well, which could support medical diagnoses based on the morphology of the fetal ECG signal.

ASW-Net: Adaptive Spectral Wavelet Network for Accurate Fetal ECG Extraction

Noninvasive fetal ECG (FECG) is of great significance for monitoring fetal health. However, it is challenging to extract FECG signals from the abdominal ECG signal (AECG) due to the complexity of the task: 1) FECG signals are routinely mixed with noise; 2) FECG signals are aliased with maternal ECG signals in the time and frequency domain. To solve such problems, an adaptive spectral wavelet network (ASW-Net) is proposed for FECG extraction, where the adaptive spectral wavelet module, which can improve the computational efficiency by replacing convolution operation with element-wise Hadamard product in the frequency domain, is first developed to extract FECG components with different frequencies; then, the residual attention module is devised to distinguish FECG signals from noise by capturing waveform details; finally, the inverse spectral wavelet module is designed to reconstruct FECG signals from multi-resolution FECG components. Experiments conducted on the benchmarks demonstrate that the proposed ASW-Net outperforms the state-of-the-art methods.

The clinical significance of electronic fetal heart rate monitoring in twins

OBJECTIVES

Fully effective intrapartum cardiotocographic (CTG) fetal heart monitoring is still missing. Visual analysis is far from credibility. Additional, computerized analysis techniques were proposed however they did not substantially decrease possible risks of fetal asphyxia. In twin pregnancies the problem is even more complicated. Our goal is to find the most valuable parameters in intrapartum CTG surveillance in twins, based on actual FIGO criteria.

MATERIAL AND METHODS

Study included 58 women in labor who had been admitted to Delivery Department of tertiary care hospital with twin pregnancy in a period of one year. The features of the CTG (e.g., baseline, oscillation, decelerations, brady- or tachycardia) were grouped to create three variables that were closest to the FIGO CTG scale. All three groups were compared according to neonatal status (Apgar score at 5 min ≥ 7 or < 7; pH value in umbilical artery ≥ 7.20, < 7.20 or < 7.10 and BE (base excess) > or ≤ -12). Fetal status and its acid - base equilibrium was compared either with long term variability (LTV), short term variability (STV), or percentage of the signal loss.

RESULTS

Out of 58 twin pregnancies, a total of 116 babies were born. One baby was born dead. From this group, 11 deliveries were natural births and 47 deliveries were C-sections. None of the analyzed features (pH, BE, Apgar, CTG features except tracing length, CTG FIGO categories) were statistically different between groups of singleton and twin pregnancies, except percentage of C-sections. No differences were found either for STV or LTV and fetal status.org CTG categories.

CONCLUSIONS

Prior to cardiotocographic tracing of twins during labor, ultrasound examination should be mandatory. Considerable loss of signal in CTG tracing in twins should provoke ultrasonographic confirmation of the fetal status.

Fetal Electrocardiogram Extraction Using Dual-Path Source Separation of Single-Channel Non-Invasive Abdominal Recordings

OBJECTIVE

The development of a method for non-invasive monitoring of fetal electrocardiogram (FECG) signals from single-channel abdominal recordings.

METHODS

The dual-path source separation (DPSS) architecture is introduced for the simultaneous separation of fetal and maternal ECG signals from abdominal ECG recordings. DPSS initially denoises abdominal ECG (AECG) recordings using a generative dual-path long short-term memory (DP-LSTM) network. An inception module along with a series of DP-LSTM blocks is then employed to extract the masking maps associated with fetal and maternal components. Finally, these masking maps are weighted by the AECG recording to separate maternal and fetal ECG signals. The performance of this network is evaluated on 10 pregnancies from the fetal ECG synthetic database (FECGSYNDB), 22 cases of labor and pregnancy from the abdominal and direct fetal ECG database (ADFECGDB), and 69 pregnancies from set A of non-invasive FECG challenge (NIFECGC) datasets.

RESULTS

F1-scores of 99.03%, 97.7%, and 95.3% are reported for the detection of fetal QRS complexes in FECGSYNDB, ADFECGDB, and NIFECGC respectively. This deep learning-based source separation technique is also evaluated in terms of separability of fetal and maternal clusters. According to the clustering-based analyses, the average purity index of 0.9750, Jaccard index of 0.9705, and Davies-Bouldin index of 0.7429 demonstrate the high source separation capability of DPSS.

CONCLUSION

The achieved performance suggests that DPSS enables accurate single-channel FECG extraction, and can replace state-of-the-art source separation techniques for this purpose.

SIGNIFICANCE

This study signifies a fundamental step towards non-invasive fetal ECG monitoring systems, which favors at-home prenatal care.

Fetal ECG denoising using dynamic time warping template subtraction

The analysis of the fetal electrocardiogram (ECG) requires to remove the mother ECG (mECG) from the abdominal ECG signals. Template subtraction is a method that consists in modeling and removing the mECG's mean period i.e. the signal waveform defined as the Euclidean mean of all periods. This mean period is then subtracted to all periods to extract the fetal ECG (fECG). Such a method is not accurate because each mECG's period is not correctly aligned with the mean period. We propose to take account of the diffeomorphism of each period to improve the precision of the model and remove the mECG more efficiently. The soft-dynamic time warping (DTW) algorithm is used to compute the mean mECG period and the alignment between the mean period and all periods. Our approach is compared to a classic template subtraction on synthetic and real databases. Results show that considering the dynamic time warping allows a better removal of the mECG. Clinical relevance - The template subtraction is modified in this paper to consider the time warping for each mother ECG's period in order to improve the fetal ECG extraction from the abdominal ECG.

Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach

The non-invasive fetal electrocardiogram (FECG) derived from abdominal surface electrodes has been widely used for fetal heart rate (FHR) monitoring to assess fetal well-being. However, the accuracy of FECG-based FHR estimation heavily depends on the quality of FECG signal itself, which can generally be affected by several interference sources such as maternal heart activities and fetal movements. Hence, FECG signal quality assessment (SQA) is an essential task to improve the accuracy of FHR estimation by removing or interpolating low-quality FECG signals. In recent research, various SQA methods based on supervised learning have been proposed. Although these methods could perform accurate SQA, they require large labeled datasets. Nevertheless, the labeled datasets for the FECG SQA are very limited. In this paper, to address this limitation, we propose an unsupervised learning-based SQA method for identifying high and low-quality FECG signal segments. Specifically, a fully convolutional network (FCN)-based autoencoder (AE) is trained for reconstructing a spectrogram derived from FECG. An AE-based feature related to reconstruction error is then calculated to identify high and low-quality FECG segments. In addition, entropy-based features, statistical features, and ECG signal quality indices (SQIs) are also extracted. The high and low-quality segments are identified by feeding the extracted features into self-organizing map (SOM). The experimental results showed that our proposal achieved an accuracy of 98% in high and low-quality signal classification.

A Review of Recent Advances and Future Developments in Fetal Phonocardiography

Fetal phonocardiography (fPCG) is receiving attention as it is a promising method for continuous fetal monitoring due to its non-invasive and passive nature. However, it suffers from the interference from various sources, overlapping the desired signal in the time and frequency domains. This paper introduces the state-of-the-art methods used for fPCG signal extraction and processing, as well as means of detection and classification of various features defining fetal health state. It also provides an extensive summary of remaining challenges, along with the practical insights and suggestions for the future research directions.

Review of optical methods for fetal monitoring in utero

The current technology for monitoring fetal wellbeing during child birth is cardiotocography. However, CTG has high false positive rates that lead to unnecessary emergency Cesarean deliveries and false negatives that result in birth injuries. To curtail these issues, fetal pulse oximetery has been a topic of interest for many decades. Fetal pulse oximetry would yield the oxygen saturation of the fetus in utero and provide a more robust marker for clinicians to make decisions about performing emergency Cesarean deliveries. Here, we present a review of biomedical optical developments related to transabdominal fetal pulse oximetery in the biophotonics field and the challenges that must be overcome to make transabdominal pulse oximetry a clinical reality.

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.

Photoacoustic assessment of the fetal brain and placenta as a method of non-invasive antepartum and intrapartum monitoring

A noninvasive monitor for concurrent evaluation of placental and fetal sagittal sinus sO 2 for both antepartum surveillance at the late 2nd and 3rd trimesters and intrapartum monitoring would be a great advantage over current methods. A PA fetal brain and placental monitor has potential value to rapidly identify the fetus at risk for developing hypoxia and ischemia of a sufficient degree that brain injury or death may develop, which may be prevented by intervention with delivery and other follow-up treatments.

Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN

A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7 % F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the very good and good ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion.

Clinical evaluation of labor: an evidence- and experience-based approach

During labor mother and fetus are evaluated at intervals to assess their well-being and determine how the labor is progressing. These assessments require skillful physical diagnosis and the ability to translate the acquired information into meaningful prognostic decision-making. We describe a coordinated approach to the assessment of labor. Graphing of serial measurements of cervical dilatation and fetal station creates "labor curves," which provide diagnostic and prognostic information. Based on these curves we recognize nine discrete labor abnormalities. Many may be related to insufficient or disordered contractile mechanisms. Several factors are strongly associated with development of labor disorders, including cephalopelvic disproportion, excess analgesia, fetal malpositions, intrauterine infection, and maternal obesity. Clinical cephalopelvimetry involves assessing pelvic traits and predicting their effects on labor. These observations must be integrated with information derived from the labor curves. Exogenous oxytocin is widely used. It has a high therapeutic index, but is easily misused. Oxytocin treatment should be restricted to situations in which its potential benefits clearly outweigh its risks. This requires there be a documented labor dysfunction or a legitimate medical reason to shorten the labor. Normal labor and delivery pose little risk to a healthy fetus; but dysfunctional labors, especially if stimulated excessively by oxytocin or terminated by complex operative vaginal delivery, have the potential for considerable harm. Conscientiously implemented, the approach to the evaluation of labor outlined in this review will result in a reasonable cesarean rate and minimize risks that may accrue from the labor and delivery process.

Fetal heart rate development during labour

BACKGROUND

Fresh stillbirths (FSB) and very early neonatal deaths (VEND) are important global challenges with 2.6 million deaths annually. The vast majority of these deaths occur in low- and low-middle income countries. Assessment of the fetal well-being during pregnancy, labour, and birth is normally conducted by monitoring the fetal heart rate (FHR). The heart rate of newborns is reported to increase shortly after birth, but a corresponding trend in how FHR changes just before birth for normal and adverse outcomes has not been studied. In this work, we utilise FHR measurements collected from 3711 labours from a low and low-middle income country to study how the FHR changes towards the end of the labour. The FHR development is also studied in groups defined by the neonatal well-being 24 h after birth.

METHODS

A signal pre-processing method was applied to identify and remove time periods in the FHR signal where the signal is less trustworthy. We suggest an analysis framework to study the FHR development using the median FHR of all measured heart rates within a 10-min window. The FHR trend is found for labours with a normal outcome, neonates still admitted for observation and perinatal mortality, i.e. FSB and VEND. Finally, we study how the spread of the FHR changes over time during labour.

RESULTS

When studying all labours, there is a drop in median FHR from 134 beats per minute (bpm) to 119 bpm the last 150 min before birth. The change in FHR was significant ([Formula: see text]) using Wilcoxon signed-rank test. A drop in median FHR as well as an increased spread in FHR is observed for all defined outcome groups in the same interval.

CONCLUSION

A significant drop in FHR the last 150 min before birth is seen for all neonates with a normal outcome or still admitted to the NCU at 24 h after birth. The observed earlier and larger drop in the perinatal mortality group may indicate that they struggle to endure the physical strain of labour, and that an earlier intervention could potentially save lives. Due to the low amount of data in the perinatal mortality group, a larger dataset is required to validate the drop for this group.

Concordance analysis of intrapartum cardiotocography between physicians and artificial intelligence-based technique using modified one-dimensional fully convolutional networks

BACKGROUND

Cardiotocography is a common method of electronic fetal monitoring (EFM) for fetal well-being. Data-driven analyses have shown potential for automated EFM assessment. For this preliminary study, we used a novel artificial intelligence method based on fully convolutional networks (FCNs), with deep learning for EFM evaluation and correct recognition, and its possible role in evaluation of nonreassuring fetal status.

METHODS

We retrospectively collected 3239 EFM labor records from 292 deliveries and neonatal Apgar scores between December 2018 and July 2019 at a single medical center. We analyzed these data using an FCN model and compared the results with clinical practice.

RESULTS

The FCN model recognized EFM traces like physicians, with an average Cohen's kappa coefficient of agreement of 0.525 and average area under the receiver operating characteristic curve of 0.892 for six fetal heart rate (FHR) categories. The FCN model showed higher sensitivity for predicting fetal compromise (0.528 vs 0.132) but a higher false-positive rate (0.632 vs 0.012) compared with clinical practice.

CONCLUSION

FCN is a modern technique that may be useful for EFM trace recognition based on its multiconvolutional layered analysis. Our model showed a competitive ability to identify FHR patterns and the potential for evaluation of nonreassuring fetal status.

A new microdialysis probe for continuous lactate measurement during fetal monitoring: Proof of concept in an animal model

INTRODUCTION

Cardiotocography (CTG) is currently the most commonly used method for intrapartum fetal monitoring during labor. However, a high false-positive rate of fetal acidosis indicated by CTG leads to an increase in obstetric interventions. We developed a microdialysis probe that is integrated into a fetal scalp electrode allowing continuous measurement of lactate subcutaneously, thus giving instant information about the oxygenation status of the fetus. Our aim was to establish proof of concept in an animal model using a microdialysis probe to monitor lactate subcutaneously.

MATERIAL AND METHODS

We performed an in vivo study in adult male wild-type Wistar rats. We modified electrodes used for CTG monitoring in human fetuses to incorporate a microdialysis membrane. Optimum flow rates for microdialysis were determined in vitro. For the in vivo experiment, a microdialysis probe was inserted into the skin on the back of the animal. De-oxygenation and acidosis were induced by lowering the inspiratory oxygen pressure. Oxygenation and heart rate were monitored. A jugular vein cannula was inserted to draw blood samples for analysis of lactate, pH, pco2 , and saturation. Lactate levels in dialysate were compared with plasma lactate levels.

RESULTS

Baseline blood lactate levels were around 1 mmol/L. Upon de-oxygenation, oxygen saturation fell to below 40% for 1 h and blood lactate levels increased 2.5-fold. Correlation of dialysate lactate levels with plasma lactate levels was 0.89 resulting in an R2 of .78 in the corresponding linear regression.

CONCLUSIONS

In this animal model, lactate levels in subcutaneous fluid collected by microdialysis closely reflected blood lactate levels upon transient de-oxygenation, indicating that our device is suitable for subcutaneous measurement of lactate. Microdialysis probe technology allows the measurement of multiple compounds in the dialysate, such as glucose, albumin, or inflammatory mediators, so this technique may offer the unique possibility to shed light on fetal physiology during the intrapartum period.

Is short-term-variation of fetal-heart-rate a better predictor of fetal acidaemia in labour? A feasibility study

Background

Continuous intrapartum fetal monitoring is challenging and its clinical benefits are debated. The project evaluated whether short-term-variation (STV) and other computerised fetal heart rate (FHR) parameters (baseline FHR, long-term-variation, accelerations and decelerations) predicted acidaemia at birth. The aims of the study were to assess the changes in FHR pattern during labour and determine the feasibility of undertaking a definitive trial by reporting the practicalities of using the monitoring device, participant recruitment, data collection and staff training.

Methods

200 high-risk women carrying a term singleton, non-anomalous fetus, requiring continuous FHR monitoring in labour were consented to participate from the Jessop Wing maternity unit, Sheffield, UK. The trans-abdominal fetal ECG monitor was placed as per clinical protocol. During the monitoring session, clinicians were blinded to the computerised FHR parameters. We analysed the last hour of the FHR and its ability to predict umbilical arterial blood pH <7.20 using receiver operator characteristics (ROC) curves.

Results

Of 200 women, 137 cases were excluded as either the monitor did not work from the onset of labour (n = 30), clinical staff did not return or used the monitor on another patient (n = 37), umbilical cord blood not obtained (n = 25), FHR data not recorded within an hour of birth (n = 34) and other reasons (n = 11). In 63 cases included in the final analysis, the computer-derived FHR parameters did not show significant correlation with umbilical artery cord pH <7.20. Labour was associated with a significant increase in short and long term variation of FHR and number of deceleration (P<0.001). However, baseline FHR decreased significantly before delivery (P<0.001).

Conclusions

The project encountered a number of challenges, with learning points crucial to informing the design of a large study to evaluate the potential place of intrapartum computerised FHR parameters, using abdominal fetal ECG monitor before its clinical utility and more widespread adoption can be ascertained.

Ultrasonographic analysis of fetal gastrointestinal motility during the peripartum period in the dog

The aim of this study was to investigate fetal gastrointestinal motility (FGM) of dogs using ultrasonic imaging and its association with vaginal and rectal temperature, serum progesterone concentrations and fetal heart rate. Pregnant bitches were examined after day 54 of gestation and there were determinations of vaginal and rectal temperature and serum progesterone concentrations. The fetal abdomen was evaluated for 30 s using longitudinal and transversal assessments, and FGM was scored as 0 (no peristalsis) or 1 (evident peristalsis). Number of fetuses with a 1 or 0 score were determined for each bitch (number and the percentage of fetuses with FGM). A total of 135 FGM measurements were recorded. There was FGM in 0/3, 0/6, 1/6 (16.7 %), 3/20 (15 %), 5/18 (27.3 %), 18/28 (64.3 %), 12/17 (70.6 %), 14/22 (63.6 %), 6/9 (66.7 %), 4/6 (66.7 %) fetuses from day -9 until 0 preceding parturition, respectively. In the last 5 days before parturition, 63.3 % of fetuses had FGM. Vaginal and rectal temperature were strongly and positively correlated (P < 0.001). Vaginal temperature was positively correlated with progesterone concentrations and fetal heart rate (P < 0.01), and there was a small negative correlation with FGM (r = -0.331, P < 0.05). Due to ease of data collection, the assessment of FGM is a valuable procedure for evaluation of fetal maturity in dogs. Vaginal and rectal temperatures are reliable variables to be assessed during the last week of pregnancy for estimating the time of parturition.

Partograph Utilization and Associated Factors among Obstetric Care Providers Working in Public Health Facilities of Wolaita Zone, 2017

BACKGROUND

Obstructed or prolonged labor is a major cause of maternal deaths. Prolonged and obstructed labor contributed to 13% of global maternal deaths which can be reduced by proper utilization of a partograph during labor. Obstetric caregivers' use of the partograph during labor has paramount importance in identifying any deviation during labor. Even though partograph use is influenced by different factors as obtained from the literatures, the magnitude of partograph utilization and the factors associated with its use are not well determined in the health facilities of Wolaita Zone.

OBJECTIVE

To assess the magnitude of partograph utilization and factors that affect its utilization among obstetric caregivers in public health facilities of Wolaita Zone, Ethiopia, 2017.

METHODS

An institution-based cross-sectional study was conducted on obstetric caregivers. A pretested and structured questionnaire was used to collect data. Data was entered to EpiData version 3.01 and exported to SPSS version 23.0 for further analysis. Logistic regression analyses were used to see the association of different variables.

RESULT

A total of 269 obstetric caregivers participated in the study. Among those who were utilizing the partograph, 193 (71.7%) routinely used it for all laboring mothers and 76 (28.3%) of participants reported that they do not routinely utilize it. Greater number of service years (AOR = 4.93, 95% CI: 1.53-15.88), on-the-job training (AOR = 0.16, 95% CI: 0.06-0.43), good knowledge (AOR = 3.35, 95% CI: 1.61-6.97), and favorable attitude towards partograph utilization (AOR = 2.99, 95% CI: 1.28-7.03) were significantly associated with partograph utilization. Conclusion and Recommendation. Partograph utilization among obstetric caregivers in the public health facilities was good. Greater years of work experience, in-service training, having good knowledge, and favorable attitude towards partograph utilization among obstetric caregivers independently determined partograph utilization. Provision of on-the-job training to make obstetric caregivers improve knowledge and skill on partograph utilization, maintaining caregivers' retention to decrease turnover by providing different incentives to more experienced obstetric care providers, and establishing favorable attitude could improve the proper use of the tool.

Reference ranges and Z-scores for fetal cardiac measurements from two-dimensional echocardiography in Asian population

Currently available fetal echocardiographic reference values are derived mainly from North American and European population studies, and there is a lack of reference z-score for fetal echocardiographic measurement in Asian populations. The aim of this study was to establish normal ranges of echocardiographic measurements and z-scores in healthy Asian fetuses. A total of 575 healthy pregnant Taiwanese with an estimated gestational age from 14 to 38 weeks were enrolled voluntarily for this observational study. Standard two-dimensional echocardiography was performed to obtain measurements of the cardiac chambers and great arteries of the developing fetuses. In contrast to past studies, our sample was more evenly distributed for estimated gestational age (p<0.001). We present percentile graphs for 13 fetal echocardiographic measurements from the knowledge of estimated gestational age, biparietal distance, head circumference, abdominal circumference, and femur length. Most cardiac structures and developmental markers had linear models as the best-fitting, except for transverse aortic isthmus by estimated gestational age and transverse ductus arteriosus by femur length. Our findings indicate that estimated gestational age was generally the best model for fetal heart development, while head circumferences could be used as an optimal developmental marker to predict left atrium, right atrium, right ventricle, pulmonary annulus, and ductus arteriosus. Lastly, we developed nomograms for each of the 13 fetal heart measurements by each developmental markers. This is the first study providing echocardiographic reference ranges and nomograms for Asian fetuses. Computing z-scores from nomograms helps in standardizing comparisons and adds additional prognostic information to the diagnosis of congenital heart disease.

The Influence of Vectorcardiogram Orientation on the T/QRS Ratio Obtained Via Non-Invasive Fetal ECG

Non-invasive fetal electrocardiography (NI-FECG) is an emerging technology that demonstrates potential for providing novel physiological information compared to traditional ultrasound-based cardiotocography (CTG). However, few studies have investigated the reliability of signal features derived via this technique for diagnostic use. One feature of NI-FECG recordings proposed for the purpose of identifying fetal distress is the T/QRS ratio, which has been indicated to change in response to fetal hypoxia. As the T/QRS ratio measures characteristics of the heart's electrical activity in 3D space (represented as the vectorcardiogram), it is critical to understand how changes in the vectorcardiogram orientation may influence the reliability of this feature. To study this influence, this work simulates NI-FECG recordings using eight finite element models of the maternal-fetal anatomy and calculates the T/QRS ratio for a range of vector-cardiogram orientations and sensor positions. To quantify the potential for T/QRS ratio estimation error in real world data, these results are compared to those observed in a homogeneous volume conductor model, as assumed by many existing signal processing techniques. Our results demonstrate that the fetal vectorcardiogram orientation has a significant influence on the reliability of the T/QRS ratio obtained via NI-FECG. Varying the vectorcardiogram orientation through a range of -30 to +30 degrees along each coordinate axis results in the potential for the T/QRS ratio to be underestimated by up to 94% and overestimated by up to 240% if a homogeneous volume conductor model is assumed. Furthermore, we find that the sensor positioning on the maternal abdomen strongly affects the range of the T/QRS ratio estimation error. These results confirm that further study must be undertaken to determine the relationship between the physiological and signal processing domains before utilizing the T/QRS ratio obtained via NI-FECG for diagnostic purposes.

Labour admission assessment results of index pregnancy as predictors of intrapartum stillbirth in public health facilities of Addis Ababa: A case-control study

BACKGROUND

Approximately one-third of the global stillbirth burden occurs during intrapartum period. The ability to assess obstetric parameters including effacement, dilatation, uterine contraction, decent, rupture of the uterus, and moulding of the foetal head are among the essential competencies required by obstetric service providers admitting women for labour in health facilities. Misdiagnosis of these conditions could result in unnecessary obstetric interventions and unfavourable obstetric outcomes including intrapartum stillbirth. This study aimed to assess associations between missed diagnosis plus complication of labour on admission and intrapartum stillbirth.

METHODS

A case-control study using primary data from chart review of medical records of women who experienced intrapartum stillbirth in 20 public health centres and three public hospitals of Addis Ababa between 01 July 2010 and 30 June 2015 was conducted. Data were collected from charts of all cases meeting the inclusion criteria. Medical records of women with livebirths were randomly selected and reviewed from each public health facilities in two to one (2:1) control to case ratio. Accordingly, 728 cases of stillbirth out of 1,056 charts met the inclusion criteria whereas 1,551 controls out of 1,705 were also considered in the study.

RESULTS

Proportionally, more women in the stillbirth group (39.4%) than in the livebirth group (30.2%) experienced ruptured membrane on admission, with the difference being statistically significant (OR 1.7, 95% CI 1.37-2.03). Significantly higher proportion of women in the intrapartum stillbirth group experienced FRH lower than 110/min, a result suggestive of foetal distress on admission. Proportionally, more women in the intrapartum stillbirth group (14.5%) than in the livebirth group (4.5%) had breech foetal presentation on admission for labour, the difference being statistically significant (aOR 3.26 95% CI 1.93-5.50). Intrapartum stillbirth was slightly higher among women with cervical dilatation 4cm or more on admission (OR 1.2, 95% CI 1.00-1.45). This could be owing to delay in seeking obstetric care or misdiagnosis of the condition, a situation that seeks more rigorous study to determine the underlying causal links. Diagnosis of foetal member was missed among more cases than controls where the difference was statistically significant (aOR 1.51, CI 1.03-2.19).

CONCLUSION

Low FHR, non-vertex foetal presentations and ruptured cervical membrane were predictors of intrapartum stillbirth. Health facilities could avert unnecessary foetal loss by undertaking timely actions to manage obstetric emergencies on admission to labour.

Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring

BACKGROUND AND OBJECTIVES

Intrauterine Growth Restriction (IUGR) is a fetal condition defined as the abnormal rate of fetal growth. The pathology is a documented cause of fetal and neonatal morbidity and mortality. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. Therefore, designing an accurate model for the early and prompt identification of pathology in the antepartum period is crucial in view of pregnancy management.

METHODS

We tested the performance of 15 machine learning techniques in discriminating healthy versus IUGR fetuses. The various models were trained with a set of 12 physiology based heart rate features extracted from a single antepartum CardioTocographic (CTG) recording. The reason for the utilization of time, frequency, and nonlinear indices is based on their standalone documented ability to describe several physiological and pathological fetal conditions.

RESULTS

We validated our approach on a database of 60 healthy and 60 IUGR fetuses. The machine learning methodology achieving the best performance was Random Forests. Specifically, we obtained a mean classification accuracy of 0.911 [0.860, 0.961 (0.95 confidence interval)] averaged over 10 test sets (10 Fold Cross Validation). Similar results were provided by Classification Trees, Logistic Regression, and Support Vector Machines. A features ranking procedure highlighted that nonlinear indices showed the highest capability to discriminate between the considered fetal conditions. Nevertheless, is the combination of features investigating CTG signal in different domains, that contributes to an increase in classification accuracy.

CONCLUSIONS

We provided validation of an accurate artificially intelligence framework for the diagnosis of IUGR condition in the antepartum period. The employed physiology based heart rate features constitute an interpretable link between the machine learning results and the quantitative estimators of fetal wellbeing.

Weight discordance and perinatal mortality in monoamniotic twin pregnancy: analysis of MONOMONO, NorSTAMP and STORK multiple-pregnancy cohorts

OBJECTIVES

The primary objective was to quantify the risk of perinatal mortality in non-anomalous monochorionic monoamniotic (MCMA) twin pregnancies complicated by birth-weight (BW) discordance. The secondary objectives were to investigate the effect of inpatient vs outpatient fetal monitoring on the risk of mortality in weight-discordant MCMA twin pregnancies, and to explore the predictive accuracy of BW discordance for perinatal mortality.

METHODS

This analysis included data on 242 MCMA twin pregnancies (484 fetuses) from three major research collaboratives on twin pregnancy (MONOMONO, STORK and NorSTAMP). The primary outcomes were the risks of intrauterine (IUD), neonatal (NND) and perinatal (PND) death, according to weight discordance at birth from ≥ 10% to ≥ 30%. The secondary outcomes were the association of inpatient vs outpatient fetal monitoring with the risk of mortality in weight-discordant pregnancies, and the accuracy of BW discordance in predicting mortality. Logistic regression and receiver-operating-characteristics-curve analyses were used to analyze the data.

RESULTS

The risk of IUD was significantly increased in MCMA twin pregnancies with BW discordance ≥ 10% (odds ratio (OR), 2.2; 95% CI, 1.1-4.4; P = 0.022) and increased up to an OR of 4.4 (95% CI, 1.3-14.4; P = 0.001) in those with BW discordance ≥ 30%. This association remained significant on multivariate logistic regression analysis for BW-discordance cut-offs ≥ 20%. However, weight discordance had low predictive accuracy for mortality, with areas under the receiver-operating-characteristics curve of 0.60 (95% CI, 0.46-0.73), 0.52 (95% CI, 0.33-0.72) and 0.57 (95% CI, 0.45-0.68) for IUD, NND and PND, respectively. There was no difference in the risk of overall IUD, single IUD, double IUD, NND or PND between pregnancies managed as an inpatient compared with those managed as an outpatient, for any BW-discordance cut-off.

CONCLUSIONS

MCMA twin pregnancies with BW discordance are at increased risk of fetal death, signaling a need for increased levels of monitoring. Despite this, the predictive accuracy for mortality is low; thus, detection of BW discordance alone should not trigger intervention, such as iatrogenic delivery. The current data do not demonstrate an advantage of inpatient over outpatient management in these cases. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Acquisition and Analysis of Electrohysterogram Signal

Electrohysterogram (EHG) signal is the signal related to action potentials propagating through smooth muscle cells of the uterus (myometrium) to the abdomen of pregnant women which is also known as uterine contraction signal. Cardiotocography (CTG) is the most common method used for monitoring fetal heart rate (FHR) and uterine contractions during pregnancy and labor. This method detects mechanical activity of fetal heart and uterus, however, it provides low accuracy and sensibility and hence more accurate methods are required. The abdominal electrode method of FECG monitoring and Electrohysterography (EHG) are alternative noninvasive method to monitor the FHR and uterine contractions during pregnancy which provides better results compared to CTG. Each information such as the frequency of uterine contractions, length of the contraction and contraction power of uterus, indicates the condition of the uterus which will help the obstetricians to identify the progress of labor. All these above mentioned parameters can be identified from the EHG signal acquired non-invasively by placing the electrodes on the abdomen of the pregnant women. In this work the acquisition of EHG signal as well as analysis of EHG signal in both antepartum condition and labor condition have been carried out and parameters such as number of contractions, contraction duration, amplitude, power of contraction are computed and the quantitative analysis of EHG signals in both above mentioned conditions are performed and it is compared with the simultaneously recorded uterine contraction signal parameters from Cardiotocography (CTG).

Evaluation of 3-tier and 5-tier FHR pattern classifications using umbilical blood pH and base excess at delivery

Objective

The relevance between time-series fetal heart rate (FHR) pattern changes during labor and outcomes such as arterial blood gas data at delivery has not been studied. Using 3-tier and 5-tier classification systems, we studied the relationship between time-series FHR pattern changes before delivery and umbilical artery blood gas data at delivery.

Methods

The subjects were 1,909 low-risk women with vaginal delivery (age: 29.1 ± 4.4 years, parity: 1.7 ± 0.8). FHR patterns were classified by a skilled obstetrician based on each 10 min-segment of the last 60 min before delivery from continuous CTG records in an obstetric clinic.

Results

The relationship between each 10 min-segment FHR pattern classification from 60 minutes before delivery and umbilical artery blood pH and base excess (BE) values at delivery changed with time. In the 3-tier classification, mean pH of Category I group in each 10 min-segment was significantly higher than that of Category II group. For Category I groups in each 10-minute segment, its number decreased and its average pH increased as the delivery time approached. In the 5-tier classification, there was the same tendency. About each level group in 10 min-segment, the higher the level, the lower the blood gas values, and mean pH of higher level groups decreased as the delivery time approached.

Conclusions

The relationship between classifications and outcomes was clear at any time from 60 min before delivery in 3- and 5-tier classifications, and the 5-tier classification was more relevant.

Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis

Early detection of potential hazards in the fetal physiological state during pregnancy and childbirth is very important. Noninvasive fetal electrocardiogram (FECG) can be extracted from the maternal abdominal signal. However, due to the interference of maternal electrocardiogram and other noises, the task of extraction is challenging. This paper introduces a novel single-lead noninvasive fetal electrocardiogram extraction method based on the technique of clustering and PCA. The method is divided into four steps: (1) pre-preprocessing; (2) fetal QRS complexes and maternal QRS complexes detection based on k-means clustering algorithm with the feature of max-min pairs; (3) FQRS correction step is to improve the performance of step two; (4) template subtraction based on PCA is introduced to extract FECG waveform. To verify the performance of the proposed algorithm, two clinical open-access databases are used to check the performance of FQRS detection. As a result, the method proposed shows the average PPV of 95.35%, Se of 96.23%, and F1-measure of 95.78%. Furthermore, the robustness test is carried out on an artificial database which proves that the algorithm has certain robustness in various noise environments. Therefore, this method is feasible and reliable to detect fetal heart rate and extract FECG. Graphical abstract Early detection of potential hazards in the fetal physiological state during pregnancy and childbirth is very important. Noninvasive fetal electrocardiogram (FECG) can be extracted from maternal abdominal signal. However, due to the interference of maternal electrocardiogram and other noises, the task of extraction is challenging. This paper introduces a novel single-lead noninvasive fetal electrocardiogram extraction method based on the technique of clustering and PCA. The method is divided into four steps: (1) pre-preprocessing; (2) fetal QRS complexes and maternal QRS complexes detection based on k-means clustering algorithm with the feature of max-min pairs; (3) FQRS correction step is to improve the performance of step two; (4) template subtraction based on PCA is introduced to extract FECG waveform. To verify the performance of algorithm, two clinical open-access databases are used to check the performance of FQRS detection. As a result, the method proposed shows the average PPV of 95.35%, Se of 96.23%, and F1-measure of 95.78%. Furthermore, the robustness test is carried out on an artificial database which proves that the algorithm has certain robustness in various noise environments. Therefore, this method is feasible and reliable to detect fetal heart rate and extract FECG.

Quantification of maternal-fetal cardiac couplings in normal and abnormal pregnancies applying high resolution joint symbolic dynamics

Maternal psycho-physiological activities affect the fetal development and its heart rate variability. In this work, the short-term maternal-fetal cardiac couplings in normal and abnormal fetuses were investigated by using the high resolution joint symbolic dynamics method. The analysis was applied on maternal and fetal beat-to-beat intervals of 66 normal and 19 abnormal fetuses that includes different types of congenital heart defects, tachycardia, Atrioventricular block and other types of abnormalities. Results showed that the weak decrease in maternal beat-to-beat variations associated with the strong increase in fetal beat-to-beat variations was found to be significantly higher for the abnormal cases compared to normal cases despite the heterogeneity of abnormality and gestational age (abnormal: 0.032 ±0.013, normal: 0.014 ±0.007, p < 0.01). These differences could be interpreted as impairment in the autonomic nervous system in abnormal cases. The atrioventricular block cases showed a rise in the strong increase and decrease fetal beat-to-beat variations compared to the normal cases while the tachycardia cases showed a decay in these coupling patterns.

Spontaneous rupture of unscarred uterus in the third trimester after in vitro fertilization-embryo transfer because of bilateral salpingectomy: A case report

RATIONALE

Rupture of an unscarred uterus after in vitro fertilization-embryo transfer (IVF-ET) in a primiparous woman is rare. Assisted reproductive technology (ART)-induced rupture of an unscarred uterus is usually attributable to increased dizygotic twinning rates. Salpingectomy can result in cornual scarring and increase the risk of uterine rupture as well as the mortality rate in a subsequent ectopic pregnancy. Here, we present the first reported case of a spontaneous, third-trimester, uterine rupture in a primiparous woman after IVF-ET due to a history of bilateral salpingectomy because of bilateral oviduct and ovarian cysts; the patient did not have an ectopic pregnancy or any cornual or other uterine scarring during this pregnancy after IVF-ET.

PATIENT CONCERNS

A 24-year-old woman with a history of IVF-ET and bilateral salpingectomy was admitted to our hospital with unexplained acute upper abdominal pain during the third trimester.

DIAGNOSIS

The fetal heart rate was abnormal. Abdominal ultrasonography was negative. Computed tomography revealed a small amount of abdominal and pericardial effusion. Laboratory tests revealed increased white blood cells. A diagnosis of pregnancy complicated by acute abdomen was considered. Emergent exploratory laparotomy revealed a uterine rupture at the right fundus adjacent to the right cornual area.

INTERVENTIONS

The patient was successfully managed with simultaneous exploratory laparotomy and lower-segment cesarean section. The rupture site was repaired.

OUTCOMES

Two live infants were uneventfully delivered. Follow-up assessments of the mother and the female baby on the 42nd postpartum day yielded normal results. The male infant was diagnosed with left hydronephrosis and required an operation.

LESSONS

We conclude that the ART-associated increase in dizygotic twinning rates may be a neglected risk factor for spontaneous rupture of the unscarred uterus, especially in patients who have undergone salpingectomy. Uterine rupture should be considered in a patient with multiple pregnancy following IVF-ET who presents with acute abdominal pain and abnormal fetal heart rate. Timely exploratory laparotomy is the key to a good prognosis.