Unsupervised denoising of the non-invasive fetal electrocardiogram with sparse domain Kalman filtering and vectorcardiographic loop alignment.

Objective.Even though the electrocardiogram (ECG) has potential to be used as a monitoring or diagnostic tool for fetuses, the use of non-invasive fetal ECG is complicated by relatively high amounts of noise and fetal movement during the measurement. Moreover, machine learning-based solutions to this problem struggle with the lack of clean reference data, which is difficult to obtain. To solve these problems, this work aims to incorporate fetal rotation correction with ECG denoising into a single unsupervised end-to-end trainable method.Approach.This method uses the vectorcardiogram (VCG), a three-dimensional representation of the ECG, as an input and extends the previously introduced Kalman-LISTA method with a Kalman filter for the estimation of fetal rotation, applying denoising to the rotation-corrected VCG.Main results.The resulting method was shown to outperform denoising auto-encoders by more than 3 dB while achieving a rotation tracking error of less than 33∘. Furthermore, the method was shown to be robust to a difference in signal to noise ratio between electrocardiographic leads and different rotational velocities.Significance.This work presents a novel method for the denoising of non-invasive abdominal fetal ECG, which may be trained unsupervised and simultaneously incorporates fetal rotation correction. This method might prove clinically valuable due the denoised fetal ECG, but also due to the method's objective measure for fetal rotation, which in turn might have potential for early detection of fetal complications.

[Fetal electrocardiogram signal extraction based on multi-scale residual shrinkage U-Net].

In the extraction of fetal electrocardiogram (ECG) signal, due to the unicity of the scale of the U-Net same-level convolution encoder, the size and shape difference of the ECG characteristic wave between mother and fetus are ignored, and the time information of ECG signals is not used in the threshold learning process of the encoder's residual shrinkage module. In this paper, a method of extracting fetal ECG signal based on multi-scale residual shrinkage U-Net model is proposed. First, the Inception and time domain attention were introduced into the residual shrinkage module to enhance the multi-scale feature extraction ability of the same level convolution encoder and the utilization of the time domain information of fetal ECG signal. In order to maintain more local details of ECG waveform, the maximum pooling in U-Net was replaced by Softpool. Finally, the decoder composed of the residual module and up-sampling gradually generated fetal ECG signals. In this paper, clinical ECG signals were used for experiments. The final results showed that compared with other fetal ECG extraction algorithms, the method proposed in this paper could extract clearer fetal ECG signals. The sensitivity, positive predictive value, and F1 scores in the 2013 competition data set reached 93.33%, 99.36%, and 96.09%, respectively, indicating that this method can effectively extract fetal ECG signals and has certain application values for perinatal fetal health monitoring.

Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals.

Standard clinical practice to assess fetal well-being during labour utilises monitoring of the fetal heart rate (FHR) using cardiotocography. However, visual evaluation of FHR signals can result in subjective interpretations leading to inter and intra-observer disagreement. Therefore, recent studies have proposed deep-learning-based methods to interpret FHR signals and detect fetal compromise. These methods have typically focused on evaluating fixed-length FHR segments at the conclusion of labour, leaving little time for clinicians to intervene. In this study, we propose a novel FHR evaluation method using an input length invariant deep learning model (FHR-LINet) to progressively evaluate FHR as labour progresses and achieve rapid detection of fetal compromise. Using our FHR-LINet model, we obtained approximately 25% reduction in the time taken to detect fetal compromise compared to the state-of-the-art multimodal convolutional neural network while achieving 27.5%, 45.0%, 56.5% and 65.0% mean true positive rate at 5%, 10%, 15% and 20% false positive rate respectively. A diagnostic system based on our approach could potentially enable earlier intervention for fetal compromise and improve clinical outcomes.

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement.

BACKGROUND: The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals. METHOD: The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal. RESULT: The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108-142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis. CONCLUSION: The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.

How pregnant women with diabetes experience telemonitoring of the fetal heart rate - An interview study.

OBJECTIVES: The use of telemonitoring in healthcare is generally increasing. Women with complicated pregnancies are using telemonitoring as an alternative to conventional management, encompassing hospitalization or frequent outpatient clinic visits. However, there is sparse evidence on how pregnant women experience monitoring of their unborn babies at home. Women might feel uncomfortable with this responsibility, and moreover they might miss face-to-face contact with healthcare personnel. STUDY DESIGN: The study setting was a Danish hospital with a tertiary obstetric unit attending approximately 3400 births annually. A qualitative study design with interview as method included 11 pregnant women with type 1 diabetes or Gestational Diabetes Mellitus. This design was used to investigate how pregnant women with complicated pregnancies experienced telemonitoring of the fetus. Reflexive thematic analysis was used to analyze the pregnant women's experiences of telemonitoring. RESULTS: Women with type 1 diabetes or Gestational Diabetes Mellitus found the advantages of telemonitoring to outweigh the disadvantages. They experienced telemonitoring as time-saving and that telemonitoring decreased the level of stress. Moreover, telemonitoring supports positive collaboration with healthcare professionals. The women also experienced a lack of coordination of consultations between different departments at the hospital and challenges with timing, feedback, and technical issues. Moreover, the women requested an opportunity to discuss family formation and emotions. CONCLUSIONS: Pregnant women with type 1 diabetes or Gestational Diabetes Mellitus benefit from the use of telemonitoring. To further improve the implementation and use of telemonitoring clinical implications, consider how timing and coordination of care, technical equipment, and feedback mechanisms could be improved.

How does the use of continuous electronic fetal monitoring influence women's experiences of labour? A systematic integrative review of the literature from high income countries.

BACKGROUND: A variety of technologies are used to monitor fetal wellbeing in labour. Different types of fetal monitoring devices impact women's experiences of labour and birth. AIM: This review aims to understand how continuous electronic fetal monitoring (CEFM) influences women's experiences, with a focus on sense of control, active decision-making and mobility. METHODS: A systematic search of the literature was conducted. Findings from qualitative, quantitative and mixed methods studies were analysed to provide a review of current evidence. FINDINGS: Eighteen publications were included. The findings were synthesised into three themes: 'Feeling reassured versus anxious about the welfare of their baby', 'Feeling comfortable and free to be mobile versus feeling uncomfortable and restricted', and 'Feeling respected and empowered to make decisions versus feeling depersonalised with minimal control '. Women experienced discomfort and a lack of mobility as a result of some CEFM technologies. They often felt anxious and had mixed feelings about their baby's welfare whilst these were in use. Some women valued the data produced by CEFM technologies about the welfare of their baby. Many women experienced a sense of depersonalisation and lack of control whilst CEFM technologies were used. DISCUSSION: Fetal monitoring technologies influence women's experiences of labour both positively and negatively. Wireless devices were associated with the most positive response as they enabled greater freedom of movement. CONCLUSION: The design of emerging fetal monitoring technologies should incorporate elements which foster freedom of movement, are comfortable and provide women with a sense of choice and control. The implementation of fetal monitoring that enables these elements should be prioritised by health professionals.

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings.

Objective.Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.Approach.In this work, we proposePower-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkPower-MFagainst three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).Main results.Our results show thatPower-MFoutperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.Significance.Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

Tending to the machine: The impact of intrapartum fetal surveillance on women in Australia.

Qualitative research about women and birthing people's experiences of fetal monitoring during labour and birth is scant. Labour and birth is often impacted by wearable or invasive monitoring devices, however, most published research about fetal monitoring is focused on the wellbeing of the fetus. This manuscript is derived from a larger mixed methods study, 'WOmen's Experiences of Monitoring Baby (The WOMB Study)', aiming to increase understanding of the experiences of women and birthing people in Australia, of being monitored; and about the information they received about fetal monitoring devices during pregnancy. We constructed a national cross-sectional survey that was distributed via social media in May and June, 2022. Responses were received from 861 participants. As far as we are aware, this is the first survey of the experiences of women and birthing people of intrapartum fetal monitoring conducted in Australia. This paper comprises the analysis of the free text survey responses, using qualitative and inductive content analysis. Two categories were constructed, Tending to the machine, which explores participants' perceptions of the way in which clinicians interacted with fetal monitoring technologies; and Impressions of the machine, which explores the direct impact of fetal monitoring devices upon the labour and birth experience of women and birthing people. The findings suggest that some clinicians need to reflect upon the information they provide to women and birthing people about monitoring. For example, freedom of movement is an important aspect of supporting the physiology of labour and managing pain. If freedom of movement is important, the physical restriction created by a wired cardiotocograph is inappropriate. Many participants noticed that clinicians focused their attention primarily on the technology. Prioritising the individual needs of the woman or birthing person is key to providing high quality woman-centred intrapartum care. Women should be provided with adequate information regarding the risks and benefits of different forms of fetal monitoring including how the form of monitoring might impact her labour experience.

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture.

The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model's generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model's discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely "Abdominal and Direct Fetal ECG Database" and "Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations", resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper's model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.

Multicentric intelligent cardiotocography signal interpretation using deep semi-supervised domain adaptation via minimax entropy and domain invariance.

BACKGROUND AND OBJECTIVE: Obstetricians use Cardiotocography (CTG), which is the continuous recording of fetal heart rate and uterine contraction, to assess fetal health status. Deep learning models for intelligent fetal monitoring trained on extensively labeled and identically distributed CTG records have achieved excellent performance. However, creation of these training sets requires excessive time and specialist labor for the collection and annotation of CTG signals. Previous research has demonstrated that multicenter studies can improve model performance. However, models trained on cross-domain data may not generalize well to target domains due to variance in distribution among datasets. Hence, this paper conducted a multicenter study with Deep Semi-Supervised Domain Adaptation (DSSDA) for intelligent interpretation of antenatal CTG signals. This approach helps to align cross-domain distribution and transfer knowledge from a label-rich source domain to a label-scarce target domain. METHODS: We proposed a DSSDA framework that integrated Minimax Entropy and Domain Invariance (DSSDA-MMEDI) to reduce inter-domain gaps and thus achieve domain invariance. The networks were developed using GoogLeNet to extract features from CTG signals, with fully connected, softmax layers for classification. We designed a Dynamic Gradient-driven strategy based on Mutual Information (DGMI) to unify the losses from Minimax Entropy (MME), Domain Invariance (DI), and supervised cross-entropy during iterative learning. RESULTS: We validated our DSSDA model on two datasets collected from collaborating healthcare institutions and mobile terminals as the source and target domains, which contained 16,355 and 3,351 CTG signals, respectively. Compared to the results achieved with deep learning networks without DSSDA, DSSDA-MMEDI significantly improved sensitivity and F1-score by over 6%. DSSDA-MMEDI also outperformed other state-of-the-art DSSDA approaches for CTG signal interpretation. Ablation studies were performed to determine the unique contribution of each component in our DSSDA mechanism. CONCLUSIONS: The proposed DSSDA-MMEDI is feasible and effective for alignment of cross-domain data and automated interpretation of multicentric antenatal CTG signals with minimal annotation cost.

Made to move: A review of measurement strategies to characterize heterogeneity in normal fetal movement.

Fetal movement is a crucial indicator of fetal well-being. Characteristics of fetal movement vary across gestation, posing challenges for researchers to determine the most suitable assessment of fetal movement for their study. We summarize the current measurement strategies used to assess fetal movement and conduct a comprehensive review of studies utilizing these methods. We critically evaluate various measurement approaches including subjective maternal perception, ultrasound, Doppler ultrasound, wearable technology, magnetocardiograms, and magnetic resonance imaging, highlighting their strengths and weaknesses. We discuss the challenges of accurately capturing fetal movement, which is influenced by factors such as differences in recording times, gestational ages, sample sizes, environmental conditions, subjective perceptions, and characterization across studies. We also highlight the clinical implications of heterogeneity in fetal movement assessment for monitoring fetal behavior, predicting adverse outcomes, and improving maternal attachment to the fetus. Lastly, we propose potential areas of future research to overcome the current gaps and challenges in measuring and characterizing abnormal fetal movement. Our review contributes to the growing body of literature on fetal movement assessment and provides insights into the methodological considerations and potential applications for research.

Home monitoring of fetal heart rhythm: Lived experiences of women with anti-SSA/Ro52 autoantibodies and their co-parents.

OBJECTIVE: The aim of this study was to explore the parents' experiences of home monitoring of the fetal heart rhythm. Women with anti-SSA/Ro52 autoantibodies carry a 2%-3% risk of giving birth to a child with congenital heart block (CHB), following transplacental transfer and antibody-mediated inflammation in the fetal conduction system during 18th to 24th gestational week. Early detection and subsequent treatment have been reported to decrease morbidity and mortality. Therefore, home monitoring of the fetal heart rhythm by Doppler has been offered at our fetal cardiology center. This study was undertaken to explore the lived experience of the routine. METHODS: Participants were recruited from a single fetal cardiology center. Consecutive sampling was used. The inclusion criteria were women with SSA/Ro52 antibodies who had undergone Doppler examinations within the last two and a half years at the hospital and had monitored the fetal heartbeat at home. A semi-structured questionnaire was created, and the participants were interviewed individually. The interviews were transcribed verbatim and analyzed according to qualitative content analysis. RESULTS: The overall theme was defined as "walking on thin ice," with six underlying categories: reality, different strategies, gain and loss, healthcare providers, underlying tension, and conducting the examinations again, all with a focus on how to handle the home monitoring during the risk period. CONCLUSION: Both the mother and the co-parent expressed confidence in their own abilities and that the monitoring provided them with the advantage of growing a bond with the expected child. However, all the participants described a feeling of underlying tension during the risk period. The results show that home monitoring is not experienced as complicated or a burden for the parents-to-be and should be considered a vital part of the chain of care for mothers at risk for giving birth to a child with CHB. However, explaining the teamwork between the different caregivers, for the patients involved, their areas of expertise, and how they collaborate with the patient continues to be a pedagogic challenge and should be developed further.

The utility of fetal heart rate deceleration's descending slope in searching for a non-National Institute of Child Health and Human Development parameter for the detection of fetal acidosis.

OBJECTIVE: To identify new parameters predicting fetal acidemia. METHODS: A retrospective case-control study in a cohort of deliveries from a tertiary referral hospital-based cohort deliveries in Zaragoza, Spain between 2018 and 2021 was performed. To predict fetal acidemia, the NICHD categorizations and non-NICHD parameters were analyzed in the electronic fetal monitoring (EFM). Those included total reperfusion time, total deceleration area and the slope of the descending limb of the fetal heart rate of the last deceleration curve. The accuracy of the parameters was evaluated using the specificity for (80%, 85%, 90%, 95%) sensitivity and the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 10 362 deliveries were reviewed, with 224 cases and 278 controls included in the study. The NICHD categorizations showed reasonable discriminatory ability (AUC = 0.727). The non-NICHD parameters measured during the 30-min fetal monitoring, total deceleration area (AUC = 0.807, 95% CI: 0.770, 0.845) and total reperfusion time (AUC = 0.750, 95% CI: 0.707, 0.792), exhibited higher discriminatory ability. The slope of the descending limb of the fetal heart rate of the last deceleration curve had the best AUC value (0.853, 95% CI: 0.816, 0.889). The combination of total deceleration area or total reperfusion time with the slope demonstrated high discriminatory ability (AUC = 0.908, 95% CI: 0.882, 0.933; specificities of 71.6% and 72.7% for a sensitivity of 90%). CONCLUSIONS: The slope of the descending limb of the fetal heart rate of the last deceleration curve is the strongest predictor of fetal acidosis, but its combination with the total reperfusion time shows better clinical utility.

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.

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.

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%.

Anesthetic recommendations for maternal and fetal safety in nonobstetric surgery: a balancing act.

PURPOSE OF REVIEW: Nonobstetric surgery during pregnancy is associated with maternal and fetal risks. Several physiologic changes create unique challenges for anesthesiologists. This review highlights physiologic changes of pregnancy and presents clinical recommendations based on recent literature to guide anesthetic management for the pregnant patient undergoing nonobstetric surgery. RECENT FINDINGS: Nearly every anesthetic technique has been safely used in pregnant patients. Although it is difficult to eliminate confounding factors, exposure to anesthetics could endanger fetal brain development. Perioperative fetal monitoring decisions require an obstetric consult based on anticipated maternal and fetal concerns. Given the limitations of fasting guidelines, bedside gastric ultrasound is useful in assessing aspiration risk in pregnant patients. Although there is concern about appropriateness of sugammadex for neuromuscular blockade reversal due its binding to progesterone, preliminary literature supports its safety. SUMMARY: These recommendations will equip anesthesiologists to provide safe care for the pregnant patient and fetus undergoing nonobstetric surgery.