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.

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.

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.

Real-time fetal monitoring using photoacoustic measurement of placental oxygen saturation in a rabbit hypoxia model.

INTRODUCTION: Ensuring adequate fetal oxygenation is an essential aim of fetal monitoring. The purpose of this study was to establish a basic technique for real-time measurement of blood oxygen saturation of the placenta by photoacoustic (PA) technique as a new fetal monitoring method. METHODS: The hypoxia model established in our previous study was applied to 7 pregnant rabbits. Three phases were induced: normal phase, hypoxia phase, and recovery phase. Three methods were simultaneously used for real-time fetal monitoring: fetal heat rate (FHR) monitoring, oxygen saturation (SO2) measurement by near-infrared spectroscopy (SNO2), and placenta SO2 measured by PA technique (SplO2). The maternal hypoxia was assessed by skin SO2 measured by PA technique (SsO2), and arterial blood SO2 by blood gas analysis (SaO2). RESULTS: The average of SplO2 in normal phase was 52.6 ± 13.9 %. The averages of SNO2, SSO2, and SplO2 in the seven rabbits changed in parallel from the normal phase to hypoxia phase. In the recovery phase, the SplO2 rose in parallel with recovery of SaO2. There was lag in increase of the FHR compared to the change in the other values. In the detailed analysis of PA signals from the labyrinth and decidua, a unique change in oxygen saturation was seen in one case. DISCUSSION: Results of this study showed that sensitivity of our novel PA technique in detecting tissue hypoxia was similar to near-infrared spectroscopy (NIRS). As an advantage, unlike NIRS, monitoring with PA technique was unaffected by ischemia and surface changes in oxygen saturation because of its higher spatial resolution. We conclude that PA technique provides more accurate information about fetal blood placenta than NIRS. Ultrasound imaging, combined with oxygen saturation monitoring by PA technique, would improve fetal monitoring and fetal diagnosis in the future.

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.

Efectividad audiovisual del monitoreo independiente del bienestar fetal sobre el apego materno-fetal y los resultados del parto / Audiovisual Effectiveness of Independent Fetal Well-being Monitoring on Mother-Fetus Attachment and Birth Outcomes

Introducción: El vínculo madre-feto juega un papel importante en la atención del embarazo, impactando los resultados del nacimiento. El monitoreo del movimiento fetal es una competencia fundamental para que las mujeres embarazadas lo hagan de manera independiente. Objetivo: Producir monitoreo audiovisual del movimiento fetal independiente y probar su efectividad en el apego materno-fetal y los resultados del parto. Métodos: La etapa I, desarrollo de Monitoreo de Bienestar Fetal Audiovisual, con estudio de literatura, etapas, desarrollo de escenarios, creación de audiovisuales, prueba de validez de expertos. La etapa 2 probó la efectividad de los medios audiovisuales sobre el apego materno-fetal con el instrumento Inventario de Apego Prenatal y los resultados del nacimiento a partir del peso del bebé al nacer. Diseño de un verdadero enfoque experimental de grupo de control pretest-postest. Muestras de mujeres embarazadas con antecedentes de atención prenatal en el Centro de Salud Kasihan II, Bantul, Yogyakarta, Indonesia con los siguientes criterios: embarazo único, normal, edad gestacional de 28 a 36 semanas. Los encuestados de 60 sujetos se dividieron en grupos experimentales y de control. Los datos normales se probaron mediante la prueba t pareada, la prueba t independiente y MANOVA. Resultados: Puntaje de prueba de validez de experto en contenido 81% muy válido, puntaje de validez de experto en medios 80.33%, válido. La aplicación audiovisual mostró los resultados de la prueba t pareada, tanto en el grupo experimental como en el control hubo diferencias en el pretest y postest, P<0.05. La prueba t de muestra independiente P < 0,05 y los resultados MANOVA simultáneos mostraron una puntuación de apego materno-fetal y un resultado del nacimiento P < 0,05 (AU)

Introduction: The mother-fetus bond plays an important role in pregnancy care, impacting birth outcomes. Monitoring fetal movement is a fundamental competence for pregnant women to do independently. Objective: to produce audiovisual monitoring of independent fetal movement and prove its effectiveness on maternal-fetal attachment and birth outcomes. Methods: Phase I, developing Audiovisual Fetal Well-being Monitoring, with literature study steps, developing scenarios, creating audiovisuals, testing the validity of experts. Stage 2 tested the effectiveness of audiovisual media on maternal-fetal attachment with the Prenatal Attachment Inventory instrument and birth outcomes from infant birth weight. Design of true experimental pretest-posttest control group approach. Samples of pregnant women with a history of antenatal care at the Kasihan II Health Center, Bantul, Yogyakarta, Indonesia with the following criteria: single pregnancy, normal, gestational age 28-36 weeks. Respondents of 60 subjects were divided into experimental and control groups. Normal data were tested by paired t test, independent t-test and MANOVA. Results: Content expert validity test score 81% very valid, media expert validity score 80.33%, valid. The audiovisual application showed the results of the paired t-test, both in the experimental and control groups there were differences in pretest and posttest, P <0.05. Independent sample t-test P < 0.05 and simultaneous MANOVA results showed maternal-fetal attachment score and birth outcome P < 0.05. Conclusion: Independent monitoring of fetal well-being using audiovisual media simultaneously affects the increase in maternal-fetal attachment scores and birth outcomes so it is recommended that second trimester pregnant women be taught techniques for counting fetal movements and practicing them routinely (AU)

Template subtraction based methods for non-invasive fetal electrocardiography extraction.

Assessment of fetal heart rate (fHR) through non-invasive fetal electrocardiogram (fECG) is challenging task. This study compares the performance of five template subtraction (TS) methods on Labor (12 5-min recordings) and Pregnancy datasets (10 20-min recordings). The methods include TS without adaptation, TS using singular value decomposition (TS[Formula: see text]), TS using linear prediction (TS[Formula: see text]), TS using scaling factor (TS[Formula: see text]), and sequential analysis (SA). The influence of the chosen maternal wavelet for the continuous wavelet transform (CWT) detector is also compared. The F1 score was used to measure performance. Each recording in both datasets consisted of four signals, resulting in a total comparison of 88 signals for the TS-based methods. The study reported the following results: F1 = 95.71% with TS, F1 = 95.93% with TS[Formula: see text], F1 = 95.30% with TS[Formula: see text], F1 = 95.82% with TS[Formula: see text], and F1 = 95.99% with SA. The study identified gaus3 as the suitable maternal wavelet for fetal R-peak detection using the CWT detector. Furthermore, the study classified signals from the tested datasets into categories of high, medium, and low quality, providing valuable insights for subsequent fECG signal extraction. This research contributes to advancing the understanding of non-invasive fECG signal processing and lays the groundwork for improving fetal monitoring in clinical settings.

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.

Re-evaluating fetal scalp pH thresholds: An examination of fetal pH variations during labor.

INTRODUCTION: Since the 1970s, fetal scalp blood sampling (FSBS) has been used as a second-line test of the acid-base status of the fetus to evaluate fetal well-being during labor. The commonly employed thresholds that delineate normal pH (>7.25), subnormal (7.20-7.25), and pathological pH (<7.20) guide clinical decisions. However, these experienced-based thresholds, based on observations and common sense, have yet to be confirmed. The aim of the study was to investigate if pH drop rate accelerates at the common thresholds (7.25 and 7.20) and to explore the possibility of identifying more accurate thresholds. MATERIAL AND METHODS: A retrospective study was conducted at a tertiary maternity hospital between June 2017 and July 2021. Patients with at least one FSBS during labor for category II fetal heart rate and delivery of a singleton cephalic infant were included. The rate of change in pH value between consecutive samples for each patient was calculated and plotted as a function of pH value. Linear regression models were used to model the evolution of the pH drop rate estimating slope and standard errors across predefined pH intervals. Exploration of alternative pH action thresholds was conducted. To explore the independence of the association between pH value and pH drop rate, multiple linear regression adjusted on age, body mass index, parity, oxytocin stimulation and suspected small for gestational age was performed. RESULTS: We included 2047 patients with at least one FSBS (total FSBS 3467); with 2047 umbilical cord blood pH, and a total of 5514 pH samples. Median pH values were 7.29 1 h before delivery, 7.26 30 min before delivery. The pH drop was slow between 7.40 and 7.30, then became more pronounced, with median rates of 0.0005 units/min at 7.25 and 0.0013 units/min at 7.20. Out of the alternative pH thresholds, 7.26 and 7.20 demonstrated the best alignment with our dataset. Multiple linear regression revealed that only pH value was significantly associated to the rate of pH change. CONCLUSIONS: Our study confirms the validity and reliability of current guideline thresholds for fetal scalp pH in category II fetal heart rate.

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?

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.

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.

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.

Evaluation of The Fetal Heart Rate Monitoring with The Non-Invasive Electrocardiography Signals.

Fetal heart rate monitoring is a crucial element in determining the health of the fetus during pregnancy. In this paper, we evaluate the fetal heart rate (FHR) and maternal heart rate (MHR) between our non-invasive fetal monitoring system, Femom, developed by a Biorithm and the Huntleigh computerized cardiotocography (cCTG) together with the Sonicaid FetalCare3 software by comparing the accuracy, sensitivity, and reliability through using Bland-Altman analysis, Positive Percent Agreement (PPA) and Intraclass Correlation Coefficient (ICC) respectively. Femom device is a part of the Femom system which collects abdominal electrocardiogram (aECG) signals. Femom sever then processes the collected signals to generate FHR and MHR using novel algorithms. We collected data from 285 pregnant participants who were at least of 28 weeks of gestational age. FHR accuracy consists of mean bias and limit-of-agreement (LoA). The FHR bias is 0.05 beat per minute (BPM) and LoA is [-8.7 8.8] with 95% confidence interval (95% CI) measured using Bland Altman analysis. The PPA of 90.9% reflects FHR sensitivity. Reliability is measured with absolute ICC and consistency ICC. The absolute ICC is of 88% and consistency ICC of 94%. For MHR evaluation, accuracy is measured using Bland Altman analysis which provided a bias of 0.35 BPM and LoA of [-7 6.2] with 95% CI. The MHR sensitivity calculated using PPA is 98% while the MHR reliability is with the absolute value of 99% and consistency ICC of 99%.

Robust Fetal Heart Rate Tracking through Fetal Electrocardiography (ECG) and Photoplethysmography (PPG) Fusion.

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.

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.

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.

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.