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

Frequency Domain Template Subtraction Approach to Attenuate Maternal Electrocardiogram in Fetal Electrocardiogram

We develop a frequency domain template subtraction approach to attenuate the maternal ECG in the abdominal ECG measured from pregnant women. The proposed approach was tested on five public fetal ECG datasets simultaneously measured with ECG from the fetal scalp. The method's performance was compared with the template subtraction approach in the time domain using the accuracy and association metrics. The accuracy was calculated by counting the number of fetal complexes in the processed data that coincided with the fetal complexes in the scalp fetal ECG. The association is quantified as the coherence between the processed data and the gold standard. The maximum coherence values calculated for each approach were compared using the paired t-test. Our results showed no difference in the accuracy between the frequency and time domain approach (p = 0.733). However, the association was higher between the frequency domain data and the gold standard compared to the template subtraction data and the gold standard (p = 0.049), indicating that the frequency domain approach yielded a signal that resembled that of the scalp ECG compared to the time domain approach.

A deep learning framework for noninvasive fetal ECG signal extraction

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

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.

2023 HRS expert consensus statement on the management of arrhythmias during pregnancy

This international multidisciplinary expert consensus statement is intended to provide comprehensive guidance that can be referenced at the point of care to cardiac electrophysiologists, cardiologists, and other health care professionals, on the management of cardiac arrhythmias in pregnant patients and in fetuses. This document covers general concepts related to arrhythmias, including both brady- and tachyarrhythmias, in both the patient and the fetus during pregnancy. Recommendations are provided for optimal approaches to diagnosis and evaluation of arrhythmias; selection of invasive and noninvasive options for treatment of arrhythmias; and disease- and patient-specific considerations when risk stratifying, diagnosing, and treating arrhythmias in pregnant patients and fetuses. Gaps in knowledge and new directions for future research are also identified.

Ambulatory antenatal fetal electrocardiography in high-risk pregnancies (AMBER): protocol for a pilot prospective cohort study

INTRODUCTION

Fetal heart rate (FHR) monitoring is a vital aspect of fetal well-being assessment, and the current method of computerised cardiotocography (cCTG) is limited to the hospital setting. Non-invasive fetal ECG (NIFECG) has the ability to produce FHR patterns through R wave detection while eliminating confusion with maternal heart rate, but is presently limited to research use. Femom is a novel wireless NIFECG device that is designed to be placed without professional assistance, while connecting to mobile applications. It has the ability to achieve home FHR monitoring thereby allowing more frequent monitoring, earlier detection of deterioration, while reducing hospital attendances. This study aims to assess the feasibility, reliability, and accuracy of femom (NIFECG) by comparing its outputs to cCTG monitoring.

METHODS AND ANALYSIS

This is a single-centred, prospective pilot study, taking place in a tertiary maternity unit. Women with a singleton pregnancy over 28⁺⁰ weeks' gestation who require antenatal cCTG monitoring for any clinical indication are eligible for recruitment. Concurrent NIFECG and cCTG monitoring will take place for up to 60 min. NIFECG signals will be postprocessed to produce FHR outputs such as baseline FHR and short-term variation (STV). Signal acceptance criteria is set as <50% of signal loss for the trace duration. Correlation, precision and accuracy studies will be performed to compare the STV and baseline FHR values produced by both devices. The impact of maternal and fetal characteristics on the effectiveness of both devices will be investigated. Other non-invasive electrophysiological assessment parameters will be assessed for its correlation with the STV, ultrasound assessments and maternal and fetal risk factors.

ETHICS AND DISSEMINATION

Approval has been obtained from South-East Scotland Research Ethics Committee 02 and MHRA. The results of this study will be published in peer-reviewed journals, and presented at international conferences.

TRIAL REGISTRATION NUMBER

NCT04941534.

Deep learning identifies cardiac coupling between mother and fetus during gestation

In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less developed countries. Here, we propose deep coherence, a novel artificial intelligence (AI) approach that relies on 1 min non-invasive electrocardiography (ECG) to explain the association between maternal and fetal heartbeats during pregnancy. We validated the performance of this approach using a trained deep learning tool on a total of 941 one minute maternal-fetal R-peaks segments collected from 172 pregnant women (20–40 weeks). The high accuracy achieved by the tool (90%) in identifying coupling scenarios demonstrated the potential of using AI as a monitoring tool for frequent evaluation of fetal development. The interpretability of deep learning was significant in explaining synchronization mechanisms between the maternal and fetal heartbeats. This study could potentially pave the way toward the integration of automated deep learning tools in clinical practice to provide timely and continuous fetal monitoring while reducing triage, side-effects, and costs associated with current clinical devices.

Outcomes of sustained fetal tachyarrhythmias after transplacental treatment

Background

Fetal tachyarrhythmia is a condition that may lead to cardiac dysfunction, hydrops, and death. Despite a transplacental treatment, failure to obtain or maintain sinus rhythm may occur.

Objective

We aimed to analyze the perinatal outcomes of sustained fetal tachyarrhythmias after in utero treatment.

Methods

We performed a retrospective evaluation of 69 cases with sustained fetal tachyarrhythmia. We compared the perinatal and long-term outcomes of prenatally converted and drug-resistant fetuses. Tachyarrhythmia subtypes were also evaluated.

Results

Conversion to sinus rhythm was obtained in 74% of cases; 26% of cases were drug-resistant and delivered arrhythmic. Three perinatal deaths occurred in both groups (6.7% vs 17%, P = .34). Neonates delivered arrhythmic were more frequently admitted to neonatal intensive care units (75% vs 31%, P < .01), and their hospital stay was longer (20.9 vs 6.64 days, P < .001). Multiple neonatal recurrences (81% vs 11%, P < .001), temporary hemodynamic dysfunction or heart failure (50% vs 6.7%, P < .001), and postnatal use of a combination treatment (44% vs 13%, P = .028) were also more frequent in this population. Beyond the neonatal period, rates of recurrences within the first 16 months were higher in drug-resistant fetuses (HR = 16.14, CI 95% [4.485; 193.8], P < .001). In this population, postnatal electrocardiogram revealed an overrepresentation of rare mechanisms, especially permanent junctional reciprocating tachycardia (PJRT) (31%).

Conclusion

Prenatal conversion to stable sinus rhythm is a major determinant of perinatal and long-term outcomes in fetal tachyarrhythmias. The underlying electrophysiological mechanisms have a major role in predicting these differential outcomes with an overrepresentation of PJRT in the drug-resistant population.

Estimating Fetal Age by Fetal Maternal Heart Rate Coupling Parameters

Beat-by-beat maternal and fetal heart couplings were reported to be evident throughout the fetal development. However, it is still unknown whether maternal-fetal heartbeat coupling parameters are associated with fetal development, and the potential interrelationships. Therefore, this study aims to investigate the associations of coupling parameters with fetal gestational age by multivariate regression models. Ten min abdominal lead-based maternal and fetal ECG signals were collected from 16 healthy pregnant women with healthy singleton pregnancies (19-32 weeks). Maternal and Fetal Heart Rate Variability (MHRV and FHRV) values as well as maternal-fetal heart rate coupling (strength, measured by A) parameters at various coupling ratios (associated with different Maternal:Fetal heartbeat ratios of 1:2, 1:3, 2:3, 2:4, 3:4, and 3:5) were calculated. Based on those features stepwise multivariate regression models were constructed by validating against the gold standard gestational age identified by crown-rump length from doppler echocardiogram. Among all models, the best model (Root Mean Square Error, RMSE=1.92) was found to be significantly (p<0.05) associated with mean fetal heart rate, mean maternal heart rate, standard deviation of maternal heart rate, λ[1:3], λ[2:3], λ[2:4]. Correlation coefficients and Bland Altman plots were constructed to statistically validate the results. The model developed based on coupling parameters only, showed the second-best performance (RMSE=2.50). Therefore, combining maternal and fetal heart rate variability parameters with maternal-fetal heart rate coupling values (rather than considering FHRV or MHRV parameters only) is found to be better associated with fetal development.Clinical relevance- This is a brief additional statement on why this might be of interest to practicing clinicians. Example: This establishes the anesthetic efficacy of 10% intraosseous injections with epinephrine to positively influence cardiovascular function.

Noninvasive Fetal Electrocardiography in the Diagnosis of Long QT Syndrome: A Case Series

INTRODUCTION

Early detection and monitoring for malignant arrhythmias is fundamental to prenatal care in long QT syndrome (LQTS). Recently, we studied the feasibility of isolating the fetal electrocardiogram (fECG) and measuring electrocardiographic intervals with a noninvasive fECG device using blind source separation with reference signal. Our aim was to evaluate the ability of fECG to diagnose LQTS.

CASE PRESENTATIONS

We identified 3 cases of clinically suspected LQTS based on fetal echocardiogram (2 had sinus bradycardia, 1 had second-degree atrioventricular block with negative maternal anti-SSA/SSB antibody titers). With institutional review board approval, these patients were prospectively enrolled for fECG acquisition. Offline post-processing generated fECG waveforms and calculated QT intervals. Case 1 and 3 had a maternal history of LQTS. Two of the three fetuses with suspected LQTS had confirmed LQTS by postnatal ECG and genetic testing. FECG was able to identify a prolonged corrected QT interval in both cases. One of these also had fetal magnetocardiography (fMCG), which yielded similar findings to the fECG. The third fetus had a normal fECG; fMCG and postnatal ECG were also normal.

CONCLUSIONS

In 3 cases, fECG findings corroborated the diagnosis of LQTS. Noninvasive fECG may offer a novel method for fECG that is portable and more clinically accessible.