Agreement on intrapartum cardiotocogram recordings between expert obstetricians

DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor

Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build and evaluate a convolutional neural network to detect neonatal acidemia from the CTG signals during delivery on a multicenter database with 27662 cases in five centers, including 3457 and 464 cases of moderate and severe neonatal acidemia respectively (defined by a fetal pH at birth between 7.05 and 7.20, and lower than 7.05 respectively). To use all the available records, the convolutional layers are pretrained on a task which consists in predicting several features known to be associated with neonatal acidemia from the raw CTG signals. In a cross-center evaluation, the AUC varies from 0.74 to 0.83 between the centers for the detection of severe acidemia, showing the ability of deep learning models to generalize from one dataset to the other and paving the way for more accurate models trained on larger databases. The model can still be significantly improved, by adding clinical variables to account for risk factors of acidemia that may not appear in the CTG signals. Further research will also be led to integrate the model in a tool that could assist humans in the interpretation of CTG.

Uterine contraction frequency after initiation of labour epidural analgesia using electrohysterography monitoring: a prospective pilot study

BACKGROUND

The introduction of electrohysterography into clinical practice provides new opportunities to study the impact of labour epidural analgesia on uterine contractility because electrohysterography has a greater sensitivity in detecting uterine contractions than external tocodynamometry. We determined the uterine contraction frequency before and after initiation of labour epidural analgesia using an electrohysterography-derived tocogram.

METHODS

This prospective study included 23 pregnant women between 36-42 weeks' gestation with a singleton cephalic presentation who requested epidural analgesia in active labour. The primary study outcome was the difference in mean uterine contraction frequency 60 minutes before and 120 minutes after epidural analgesia initiation. The secondary aim was to measure changes in mean contraction frequency over time, using the mean uterine contraction frequency per 10 minutes, derived from 30-minute averages.

RESULTS

In the 120 minutes after epidural analgesia initiation, the average contraction frequency decreased significantly (-0.37 contractions/10 minutes [95% CI -0.64 to -0.11]; P = 0.007 compared with the 60 minutes before epidural analgesia initiation. The largest decrease occurred 60-90 minutes after epidural analgesia initiation (-0.47 contractions/10 minutes [95% CI -0.89 to -0.05]; P = 0.029).

CONCLUSION

During active labour, electrohysterography identified a statistically significant, although clinically small, reduction in uterine contraction frequency after epidural analgesia initiation. This pilot study demonstrates the potential value of electrohysterography monitoring for obstetric anaesthesia research and might renew interest in the still poorly understood interaction between labour epidural analgesia and uterine activity.

The effect of continuous electronic fetal monitoring on mode of delivery and neonatal outcome among low-risk laboring mothers at Debre Markos comprehensive specialized hospital, Northwest Ethiopia

Background

Electronic fetal heart rate monitoring (EFM) has been widely used in obstetric practice for over 40 years to improve perinatal outcomes. Its popularity is growing in Ethiopia and other sub-Saharan African countries to reduce high perinatal morbidity and mortality rates. However, its impact on delivery mode and perinatal outcomes in low-risk pregnancies remains controversial. This study aimed to assess the effect of continuous EFM on delivery mode and neonatal outcomes among low-risk laboring mothers at Debre Markos Comprehensive Specialized Hospital, Northwest Ethiopia.

Methods

A prospective follow-up study was conducted from November 20, 2023, to January 10, 2024. All low-risk laboring mothers meeting the inclusion criteria were included. Data were collected via pretested structured questionnaires and observation, then analyzed using Epi-data 4.6 and SPSS. The incidences of cesarean delivery and continuous EFM were compared using the chi-squared test and Fisher's exact test.

Results

The study found higher rates of instrumental-assisted vaginal delivery (7% vs. 2.4%) and cesarean sections (16% vs. 2%) due to unsettling fetal heart rate patterns in the continuous EFM group compared to the intermittent auscultation group. However, there were no differences in immediate neonatal outcomes between the groups.

Conclusion

When compared to intermittent auscultation with a Pinard fetoscope, the routine use of continuous EFM among low-risk laboring mothers was associated with an increased risk of cesarean sections and instrumental vaginal deliveries, without significantly improving immediate newborn outcomes. However, it is important to note that our study faced significant logistical constraints due to the limited availability of EFM devices, which influenced our ability to use EFM comprehensively. Given these limitations, we recommend avoiding the routine use of continuous EFM for low-risk laboring mothers to help reduce the rising number of operative deliveries, particularly cesarean sections. Our findings should be interpreted with caution, and further research with adequate resources is needed to draw definitive conclusions.

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.

Intelligent classification of cardiotocography based on a support vector machine and convolutional neural network: Multiscene research

OBJECTIVE

To propose a computerized system utilizing multiscene analysis based on a support vector machine (SVM) and convolutional neural network (CNN) to assess cardiotocography (CTG) intelligently.

METHODS

We retrospectively collected 2542 CTG records of singleton pregnancies delivered at the maternity ward of the First Affiliated Hospital of Xi'an Jiaotong University from October 10, 2020, to August 7, 2021. CTG records were divided into five categories (baseline, variability, acceleration, deceleration, and normality). Apart from the category of normality, the other four different categories of abnormal data correspond to four scenes. Each scene was divided into training and testing sets at 9:1 or 7:3. We used three computer algorithms (dynamic threshold, SVM, and CNN) to learn and optimize the system. Accuracy, sensitivity, and specificity were performed to evaluate performance.

RESULTS

The global accuracy, sensitivity, and specificity of the system were 93.88%, 93.06%, and 94.33%, respectively. In acceleration and deceleration scenes, when the convolution kernel was 3, the test data set reached the highest performance.

CONCLUSION

The multiscene research model using SVM and CNN is a potential effective tool to assist obstetricians in classifying CTG intelligently.

Prediction of labour outcomes using prelabour computerised cardiotocogram and maternal and fetal Doppler indices: A prospective cohort study

OBJECTIVES

To investigate the association and the potential value of prelabour fetal heart rate short-term variability (STV) determined by computerised cardiotocography (cCTG) and maternal and fetal Doppler in predicting labour outcomes.

DESIGN

Prospective cohort study.

SETTING

The Prince of Wales Hospital, a tertiary maternity unit, in Hong Kong SAR.

POPULATION

Women with a term singleton pregnancy in latent phase of labour or before labour induction were recruited during May 2019-November 2021.

METHODS

Prelabour ultrasonographic assessment of fetal growth, Doppler velocimetry and prelabour cCTG monitoring including Dawes-Redman CTG analysis were registered shortly before induction of labour or during the latent phase of spontaneous labour.

MAIN OUTCOME MEASURES

Umbilical cord arterial pH, emergency delivery due to pathological CTG during labour and neonatal intensive care unit (NICU)/special care baby unit (SCBU) admission.

RESULTS

Of the 470 pregnant women invited to participate in the study, 440 women provided informed consent and a total of 400 participants were included for further analysis. Thirty-four (8.5%) participants underwent emergency delivery for pathological CTG during labour. A total of 6 (1.50%) and 148 (37.00%) newborns required NICU and SCBU admission, respectively. Middle cerebral artery pulsatility index (MCA-PI) and MCA-PI z-score were significantly lower in pregnancies that required emergency delivery for pathological CTG during labour compared with those that did not (1.23 [1.07-1.40] versus 1.40 [1.22-1.64], p = 0.002; and 0.55 ± 1.07 vs. 0.12 ± 1.06), p = 0.049]. This study demonstrated a weakly positive correlation between umbilical cord arterial pH and prelabour log10 STV (r = 0.107, p = 0.035) and the regression analyses revealed that the contributing factors for umbilical cord arterial pH were smoking (p = 0.006) and prelabour log10 STV (p = 0.025).

CONCLUSIONS

In pregnant women admitted in latent phase of labour or for induction of labour at term, prelabour cCTG STV had a weakly positive association with umbilical cord arterial pH but was not predictive of emergency delivery due to pathological CTG during labour.

Large-scale analysis of interobserver agreement and reliability in cardiotocography interpretation during labor using an online tool

BACKGROUND

While the effectiveness of cardiotocography in reducing neonatal morbidity is still debated, it remains the primary method for assessing fetal well-being during labor. Evaluating how accurately professionals interpret cardiotocography signals is essential for its effective use. The objective was to evaluate the accuracy of fetal hypoxia prediction by practitioners through the interpretation of cardiotocography signals and clinical variables during labor.

MATERIAL AND METHODS

We conducted a cross-sectional online survey, involving 120 obstetric healthcare providers from several countries. One hundred cases, including fifty cases of fetal hypoxia, were randomly assigned to participants who were invited to predict the fetal outcome (binary criterion of pH with a threshold of 7.15) based on the cardiotocography signals and clinical variables. After describing the participants, we calculated (with a 95% confidence interval) the success rate, sensitivity and specificity to predict the fetal outcome for the whole population and according to pH ranges, professional groups and number of years of experience. Interobserver agreement and reliability were evaluated using the proportion of agreement and Cohen's kappa respectively.

RESULTS

The overall ability to predict a pH level below 7.15 yielded a success rate of 0.58 (95% CI 0.56-0.60), a sensitivity of 0.58 (95% CI 0.56-0.60) and a specificity of 0.63 (95% CI 0.61-0.65). No significant difference in the success rates was observed with respect to profession and number of years of experience. The success rate was higher for the cases with a pH level below 7.05 (0.69) and above 7.20 (0.66) compared to those falling between 7.05 and 7.20 (0.48). The proportion of agreement between participants was good (0.82), with an overall kappa coefficient indicating substantial reliability (0.63).

CONCLUSIONS

The use of an online tool enabled us to collect a large amount of data to analyze how practitioners interpret cardiotocography data during labor. Despite a good level of agreement and reliability among practitioners, the overall accuracy is poor, particularly for cases with a neonatal pH between 7.05 and 7.20. Factors such as profession and experience level do not present notable impact on the accuracy of the annotations. The implementation and use of a computerized cardiotocography analysis software has the potential to enhance the accuracy to detect fetal hypoxia, especially for ambiguous cardiotocography tracings.

Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data

Purpose

Cardiotocography (CTG), which measures uterine contraction (UC) and fetal heart rate (FHR), is a crucial tool for assessing fetal health during pregnancy. However, traditional computerized cardiotocography (cCTG) approaches have non-negligible calibration errors in feature extraction and heavily rely on the expertise and prior experience to define diagnostic features from CTG or FHR signals. Although previous works have studied deep learning methods for extracting CTG or FHR features, these methods still neglect the clinical information of pregnant women.

Methods

In this paper, we proposed a multimodal deep learning architecture (MMDLA) for intelligent antepartum fetal monitoring that is capable of performing automatic CTG feature extraction, fusion with clinical data and classification. The multimodal feature fusion was achieved by concatenating high-level CTG features, which were extracted from preprocessed CTG signals via a convolution neural network (CNN) with six convolution layers and five fully connected layers, and the clinical data of pregnant women. Eventually, light gradient boosting machine (LGBM) was implemented as fetal status assessment classifier. The effectiveness of MMDLA was evaluated using a dataset of 16,355 cases, each of which includes FHR signal, UC signal and pertinent clinical data like maternal age and gestational age.

Results

With an accuracy of 90.77% and an area under the curve (AUC) value of 0.9201, the multimodal features performed admirably. The data imbalance issue was also effectively resolved by the LGBM classifier, with a normal-F1 value of 0.9376 and an abnormal-F1 value of 0.8223.

Conclusion

In summary, the proposed MMDLA is conducive to the realization of intelligent antepartum fetal monitoring.

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them.

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.

DeepCTG® 1.0: an interpretable model to detect fetal hypoxia from cardiotocography data during labor and delivery

Introduction

Cardiotocography, which consists in monitoring the fetal heart rate as well as uterine activity, is widely used in clinical practice to assess fetal wellbeing during labor and delivery in order to detect fetal hypoxia and intervene before permanent damage to the fetus. We present DeepCTG® 1.0, a model able to predict fetal acidosis from the cardiotocography signals.

Materials and methods

DeepCTG® 1.0 is based on a logistic regression model fed with four features extracted from the last available 30 min segment of cardiotocography signals: the minimum and maximum values of the fetal heart rate baseline, and the area covered by accelerations and decelerations. Those four features have been selected among a larger set of 25 features. The model has been trained and evaluated on three datasets: the open CTU-UHB dataset, the SPaM dataset and a dataset built in hospital Beaujon (Clichy, France). Its performance has been compared with other published models and with nine obstetricians who have annotated the CTU-UHB cases. We have also evaluated the impact of two key factors on the performance of the model: the inclusion of cesareans in the datasets and the length of the cardiotocography segment used to compute the features fed to the model.

Results

The AUC of the model is 0.74 on the CTU-UHB and Beaujon datasets, and between 0.77 and 0.87 on the SPaM dataset. It achieves a much lower false positive rate (12% vs. 25%) than the most frequent annotation among the nine obstetricians for the same sensitivity (45%). The performance of the model is slightly lower on the cesarean cases only (AUC = 0.74 vs. 0.76) and feeding the model with shorter CTG segments leads to a significant decrease in its performance (AUC = 0.68 with 10 min segments).

Discussion

Although being relatively simple, DeepCTG® 1.0 reaches a good performance: it compares very favorably to clinical practice and performs slightly better than other published models based on similar approaches. It has the important characteristic of being interpretable, as the four features it is based on are known and understood by practitioners. The model could be improved further by integrating maternofetal clinical factors, using more advanced machine learning or deep learning approaches and having a more robust evaluation of the model based on a larger dataset with more pathological cases and covering more maternity centers.

CTG interpretation templates affect residents' decision making

OBJECTIVE

To study whether a revision of CTG guidelines and educational program influenced the perceived need for intervention by residents in obstetrics and gynecology. A secondary aim was to study the sensitivity and specificity of the classification pathological after classification by residents using two different guidelines in identifying neonates with acidemia.

STUDY DESIGN

Cardiotocograms, CTGs, from 223 neonates with acidemia at birth (cord blood pH < 7.05 at vaginal birth or second stage cesarean, or pH < 7.10 at first stage cesarean) were included, as well as 223 CTGs from neonates with cord blood pH ≥ 7.15. Two separate groups of residents, who each were educated in and had clinical experience only from either of the two different guidelines, SWE09 and SWE17, classified the patterns according to the at the time current template and judged whether the patterns indicated an intervention. Sensitivity, specificity, and agreement were calculated.

RESULTS

Residents using SWE09 found indication to intervene in a higher proportion of neonates with acidemia (84.8%) than residents using SWE17 (75.8%; p = 0.002), as well as in cases without acidemia (29.6% vs 22.4%; p = 0.038). Among residents using SWE09 the perceived need for intervention had a sensitivity of 85% and a specificity of 70% to identify acidemia. With SWE17 the corresponding rates were 76% and 78%. The sensitivity to identify neonates with acidemia by classification pathological was 91% with SWE09 and 72% with SWE17. The specificity was 53% and 76% respectively. The agreement rate between perception of indication to intervene and classification pathological using the SWE09 was κ 0.73, moderate, and with the SWE17 κ 0.77, moderate. The agreement on subjective perception of necessity to intervene between users of the two templates was weak to moderate, κ 0.60, and on classification pathological weak, κ 0.47.

CONCLUSION

The perceived need for intervention by residents interpreting CTGs was significantly affected by the guidelines in use. The difference in decisions were less pronounced than the difference in classification. The sensitivity for both perceived need for intervention and for classification pathological to identify acidosis was higher with SWE09, and the specificity higher with SWE17, when assessed by the two comparable groups of residents.

Prevalence and risk factor of postoperative adhesions following repeated cesarean section: A prospective cohort study

OBJECTIVE

To evaluate the prevalence of intraperitoneal adhesions after repeated cesarean delivery and its associated personal and surgical risk factors.

METHODS

This prospective cohort study was conducted at the delivery ward at Fayoum University Hospital from October 2020 to December 2021. Women were recruited according to predetermined inclusion and exclusion criteria. Eligible women were interviewed, and data were obtained for personal history, past surgical and obstetrical history, and data about the current delivery. Nair's scoring system was used to evaluate intraperitoneal adhesions. Postoperative data and complications were reported.

RESULTS

Three hundred women were recruited. Moderate to severe adhesions occurred in 186 patients (62%). These patients had a significantly prolonged hospital stay and were delivered by expert surgeons (P < 0.001 and P = 0.008, respectively). The adhesion score correlated positively with patients' age (P < 0.001), parity (P < 0.001), interpregnancy interval (P = 0.033), duration of hospital admission either previously or in the current delivery (P = 0.001 and P < 0.001), time to ambulation (P < 0.001), time to intestinal movement (P < 0.001), operative time (P < 0.001), and surgeons' age and experience (both P = 0.015).

CONCLUSION

Adhesions led to increased maternal morbidity. Multiple contributing factors were significantly related to adhesions with multiple cesarean deliveries.

Computerized cardiotocography analysis during labor - A state-of-the-art review

Cardiotocography is defined as the recording of fetal heart rate and uterine contractions and is widely used during labor as a screening tool to determine fetal wellbeing. The visual interpretation of the cardiotocography signals by the practitioners, following common guidelines, is subject to a high interobserver variability, and the efficiency of cardiotocography monitoring is still debated. Since the 1990s, researchers and practitioners work on designing reliable computer-aided systems to assist practitioners in cardiotocography interpretation during labor. Several systems are integrated in the monitoring devices, mostly based on the guidelines, but they have not clearly demonstrated yet their usefulness. In the last decade, the availability of large clinical databases as well as the emergence of machine learning and deep learning methods in healthcare has led to a surge of studies applying those methods to cardiotocography signals analysis. The state-of-the-art systems perform well to detect fetal hypoxia when evaluated on retrospective cohorts, but several challenges remain to be tackled before they can be used in clinical practice. First, the development and sharing of large, open and anonymized multicentric databases of perinatal and cardiotocography data during labor is required to build more accurate systems. Also, the systems must produce interpretable indicators along with the prediction of the risk of fetal hypoxia in order to be appropriated and trusted by practitioners. Finally, common standards should be built and agreed on to evaluate and compare those systems on retrospective cohorts and to validate their use in clinical practice.

A second look at intrapartum fetal surveillance and future directions

Intrapartum fetal surveillance aims to predict significant fetal hypoxia and institute timely intervention to avoid fetal injury, and do so without unnecessary operative delivery of fetuses at no risk of intrapartum hypoxia. However, the configuration and application of current clinical guidelines inadvertently undermine these aims because of persistent failure to incorporate increased understanding of fetal cardiovascular physiology and adaptations to oxygen deprivation, advances in signal acquisition/processing, and related technologies. Consequently, the field on intrapartum fetal surveillance is stuck in rudimentary counts of the fetal R-R intervals and visual assessment of very common, but nonspecific fetal heart decelerations and fetal heart rate variability. The present authors argue that the time has come to move away from classifications of static morphological appearances of FHR decelerations, which do not assist the thinking clinician in understanding how the fetus defends itself and compensates for intrapartum hypoxic ischaemic insults or the patterns that suggest progressive loss of compensation. We also reappraise some of the controversial aspects of intrapartum fetal surveillance in modern obstetric practice, the current state of flux in training and certification, and contemplate the future of the field particularly in the context of the emerging role of artificial intelligence.

Cardiotocography analysis by empirical dynamic modeling and Gaussian processes

Introduction: During labor, fetal heart rate (FHR) and uterine activity (UA) can be continuously monitored using Cardiotocography (CTG). This is the most widely adopted approach for electronic fetal monitoring in hospitals. Both FHR and UA recordings are evaluated by obstetricians for assessing fetal well-being. Due to the complex and noisy nature of these recordings, the evaluation by obstetricians suffers from high interobserver and intraobserver variability. Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. Methods: Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. In this paper, we propose to model intrapartum CTG recordings from a dynamical system perspective using empirical dynamic modeling with Gaussian processes, which is a Bayesian nonparametric approach for estimation of functions. Results and Discussion: In the context of our paper, Gaussian processes are capable for simultaneous estimation of the dimensionality of attractor manifolds and reconstructing of attractor manifolds from time series data. This capacity of Gaussian processes allows for revealing causal relationships between the studied time series. Experimental results on real CTG recordings show that FHR and UA signals are causally related. More importantly, this causal relationship and estimated attractor manifolds can be exploited for several important applications in computerized analysis of CTG recordings including estimating missing FHR samples, recovering burst errors in FHR tracings and characterizing the interactions between FHR and UA signals.

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data

Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient’s outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation.

Artificial intelligence in perinatal diagnosis and management of congenital heart disease

Prenatal diagnosis and management of congenital heart disease (CHD) has progressed substantially in the past few decades. Fetal echocardiography can accurately detect and diagnose approximately 85% of cardiac anomalies. The prenatal diagnosis of CHD results in improved care, with improved risk stratification, perioperative status and survival. However, there is much work to be done. A minority of CHD is actually identified prenatally. This seemingly incongruous gap is due, in part, to diminished recognition of an anomaly even when present in the images and the need for increased training to obtain specialized cardiac views. Artificial intelligence (AI) is a field within computer science that focuses on the development of algorithms that "learn, reason, and self-correct" in a human-like fashion. When applied to fetal echocardiography, AI has the potential to improve image acquisition, image optimization, automated measurements, identification of outliers, classification of diagnoses, and prediction of outcomes. Adoption of AI in the field has been thus far limited by a paucity of data, limited resources to implement new technologies, and legal and ethical concerns. Despite these barriers, recognition of the potential benefits will push us to a future in which AI will become a routine part of clinical practice.

BOOST ENSEMBLE LEARNING FOR CLASSIFICATION OF CTG SIGNALS

During the process of childbirth, fetal distress caused by hypoxia can lead to various abnormalities. Cardiotocography (CTG), which consists of continuous recording of the fetal heart rate (FHR) and uterine contractions (UC), is routinely used for classifying the fetuses as hypoxic or non-hypoxic. In practice, we face highly imbalanced data, where the hypoxic fetuses are significantly underrepresented. We propose to address this problem by boost ensemble learning, where for learning, we use the distribution of classification error over the dataset. We then iteratively select the most informative majority data samples according to this distribution. In our work, in addition to addressing the imbalanced problem, we also experimented with features that are not commonly used in obstetrics. We extracted a large number of statistical features of fetal heart tracings and uterine activity signals and used only the most informative ones. For classification, we implemented several methods: Random Forest, AdaBoost, k-Nearest Neighbors, Support Vector Machine, and Decision Trees. The paper provides a comparison in the performance of these methods on fetal heart rate tracings available from a public database. Our results show that most applied methods improved their performances considerably when boost ensemble was used.

UNSUPERVISED CLUSTERING AND ANALYSIS OF CONTRACTION-DEPENDENT FETAL HEART RATE SEGMENTS

The computer-aided interpretation of fetal heart rate (FHR) and uterine contraction (UC) has not been developed well enough for wide use in delivery rooms. The main challenges still lie in the lack of unclear and nonstandard labels for cardiotocography (CTG) recordings, and the timely prediction of fetal state during monitoring. Rather than traditional supervised approaches to FHR classification, this paper demonstrates a way to understand the UC-dependent FHR responses in an unsupervised manner. In this work, we provide a complete method for FHR-UC segment clustering and analysis via the Gaussian process latent variable model, and density-based spatial clustering. We map the UC-dependent FHR segments into a space with a visual dimension and propose a trajectory-based FHR interpretation method. Three metrics of FHR trajectory are defined and an open-access CTG database is used for testing the proposed method.

IMPROVING PHASE-RECTIFIED SIGNAL AVERAGING FOR FETAL HEART RATE ANALYSIS

Low umbilical artery pH is a marker for neonatal acidosis and is associated with an increased risk for neonatal complications. The phase-rectified signal averaging (PRSA) features have demonstrated superior discriminatory or diagnostic ability and good interpretability in many biomedical applications including fetal heart rate analysis. However, the performance of PRSA method is sensitive to values of the selected parameters which are usually either chosen based on a grid search or empirically in the literature. In this paper, we examine PRSA method through the lens of dynamical systems theory and reveal the intrinsic connection between state space reconstruction and PRSA. From this perspective, we then introduce a new feature that can better characterize dynamical systems comparing with PRSA. Our experimental results on an open-access intrapartum Cardiotocography database demonstrate that the proposed feature outperforms state-of-the-art PRSA features in pH-based fetal heart rate analysis.

Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring

Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation. Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made. Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.

Distance to Healthy Metabolic and Cardiovascular Dynamics From Fetal Heart Rate Scale-Dependent Features in Pregnant Sheep Model of Human Labor Predicts the Evolution of Acidemia and Cardiovascular Decompensation

The overarching goal of the present work is to contribute to the understanding of the relations between fetal heart rate (FHR) temporal dynamics and the well-being of the fetus, notably in terms of predicting the evolution of lactate, pH and cardiovascular decompensation (CVD). It makes uses of an established animal model of human labor, where 14 near-term ovine fetuses subjected to umbilical cord occlusions (UCO) were instrumented to permit regular intermittent measurements of metabolites lactate and base excess, pH, and continuous recording of electrocardiogram (ECG) and systemic arterial blood pressure (to identify CVD) during UCO. ECG-derived FHR was digitized at the sampling rate of 1,000 Hz and resampled to 4 Hz, as used in clinical routine. We focused on four FHR variability features which are tunable to temporal scales of FHR dynamics, robustly computable from FHR sampled at 4 Hz and within short-time sliding windows, hence permitting a time-dependent, or local, analysis of FHR which helps dealing with signal noise. Results show the sensitivity of the proposed features for early detection of CVD, correlation to metabolites and pH, useful for early acidosis detection and the importance of coarse time scales (2.5-8 s) which are not disturbed by the low FHR sampling rate. Further, we introduce the performance of an individualized self-referencing metric of the distance to healthy state, based on a combination of the four features. We demonstrate that this novel metric, applied to clinically available FHR temporal dynamics alone, accurately predicts the time occurrence of CVD which heralds a clinically significant degradation of the fetal health reserve to tolerate the trial of labor.

Unsupervised Detection of Anomalies in Fetal Heart Rate Tracings using Phase Space Reconstruction

Detection of anomalies in time series is still a challenging problem. In this paper, we provide a new approach to unsupervised detection of anomalies in time series based on the concept of phase space reconstruction and manifolds. We propose a rotation-insensitive metric for quantifying the similarity of manifolds and a method that uses it for estimating the probability of an outlier. The proposed method does not rely on any features and can be used for signals with variable lengths. We tested it on both synthetic signals and real fetal heart rate tracings. The method has promising performance and can be used for interpreting the severity of fetal asphyxia.

Cardiotocography signal abnormality classification using time-frequency features and Ensemble Cost-sensitive SVM classifier

BACKGROUND

Cardiotocography (CTG) signal abnormality classification plays an important role in the diagnosis of abnormal fetuses. This classification problem is made difficult by the non-stationary nature of CTG and the dataset imbalance. This paper introduces a novel application of Time-frequency (TF) features and Ensemble Cost-sensitive Support Vector Machine (ECSVM) classifier to tackle these problems.

METHODS

Firstly, CTG signals are converted into TF-domain representations by Continuous Wavelet Transform (CWT), Wavelet Coherence (WTC), and Cross-wavelet Transform (XWT). From these representations, a novel image descriptor is used to extract the TF features. Then, the linear feature is derived from the time-domain representation of the CTG signal. The linear and TF features are fed to the ECSVM classifier for prediction and classification of fetal outcome.

RESULTS

The TF features show the significant difference (p-value<0.05) in distinguishing abnormal CTG signals, but not for traditional nonlinear features. In ECSVM abnormality classification, using only linear features, the sensitivity, specificity, and quality index are 59.3%, 78.3%, and 68.1%, respectively, whereas more effective results (sensitivity: 85.2%, specificity: 66.1%, and quality index: 75.0%) are obtained using a combination of linear and TF features, with a performance improvement index of 10.1%. Especially, the area under the receiver operating characteristic curve (0.77 vs. 0.64) is significantly increased with the ECSVM vs. SVM.

CONCLUSION

Our method can greatly improve the classification results, especially for sensitivity. It improves the true positive rate of CTG abnormality classification and reduces the false positive rate, which may help detect and treat abnormal fetuses during labor.

A review of fetal cardiac monitoring, with a focus on low- and middle-income countries

There is limited evidence regarding the utility of fetal monitoring during pregnancy, particularly during labor and delivery. Developed countries rely on consensus 'best practices' of obstetrics and gynecology professional societies to guide their protocols and policies. Protocols are often driven by the desire to be as safe as possible and avoid litigation, regardless of the cost of downstream treatment. In high-resource settings, there may be a justification for this approach. In low-resource settings, in particular, interventions can be costly and lead to adverse outcomes in subsequent pregnancies. Therefore, it is essential to consider the evidence and cost of different fetal monitoring approaches, particularly in the context of treatment and care in low-to-middle income countries. This article reviews the standard methods used for fetal monitoring, with particular emphasis on fetal cardiac assessment, which is a reliable indicator of fetal well-being. An overview of fetal monitoring practices in low-to-middle income counties, including perinatal care access challenges, is also presented. Finally, an overview of how mobile technology may help reduce barriers to perinatal care access in low-resource settings is provided.

Qualitative assessment of interpretability and observer agreement of three uterine monitoring techniques

OBJECTIVE

The aim of this research was to assess the quality and inter- and intra-observer agreement of tracings obtained by three different techniques for uterine contraction monitoring: the external tocodynamometer (TOCO), the intrauterine pressure catheter (IUPC) and a recently introduced method based on electrohysterography (EHG).

STUDY DESIGN

We included 150 uterine activity registrations from a previous prospective observational study (W3 study), conducted at Máxima Medical Centre in Veldhoven, the Netherlands. Term singleton pregnant women were simultaneously monitored with TOCO, IUPC and EHG during labor. Six clinicians, blinded to the source (TOCO, IUPC, or EHG) and subject, evaluated all tracings that were subsequently presented in random order. They annotated contractions and assigned each tracing a score for interpretability of 2 (good), 1 (moderate) or 0 (poor). To evaluate inter-observer agreement, we calculated kappa values for the qualitative assessment, and intraclass correlation coefficients (ICC) for the number of contractions annotated by clinicians. Four clinicians repeated this procedure to evaluate intra-observer agreement.

RESULTS

IUPC tracings received the highest quality rating, with a mean score of 1.95, followed by a mean score of 1.60 for EHG and 0.80 for TOCO (p < 0.05). Mean weighted kappa values were 0.63 for TOCO and 0.45 for EHG. The average number of contractions that was picked up by clinicians was 59.8 for the intrauterine pressure catheter, 49.8 for EHG and 26.4 for TOCO. The ICC of the intrauterine pressure catheter was significantly higher than the external methods, regarding both inter- and intra-observer agreement (0.98 and 0.99 respectively).

CONCLUSION

IUPC recordings scored best regarding quality, inter- and intra-observer agreement. However, due to safety issues, in many countries this technique is not used anymore. The quality of TOCO was rated as poor and many contractions were missed as compared to the gold standard. From a clinical interpretational point of view, EHG is favorable to TOCO. EHG recordings were assigned higher quality scores, but with less agreement between clinicians. An explanation could be that EHG is a relatively new technique, while IUPC and the TOCO are being used for decades. Building experience with EHG (training) is therefore recommended.

Prelabor short-term variability in fetal heart rate by computerized cardiotocogram and maternal fetal doppler indices for the prediction of labor outcomes

Objectives: To investigate (i) the association between pre-labor maternal-fetal Dopplers and fetal heart rate short-term variability (FHR STV) with arterial cord blood pH and (ii) the potential value of pre-labor maternal-fetal Dopplers, FHR STV and Dawes-Redman criteria in predicting composite neonatal morbidity at term in a cohort of unselected women.Method: A prospective study in 218 women with term singleton pregnancy in latent phase of labor or due to undergo induction of labor. Data on maternal characteristics, maternal-fetal Dopplers indices and computerized cardiotocography (CTG) findings of FHR STV and Dawes-Redman criteria were collected. Pearson correlation analysis was used to determine the relationship between maternal-fetal Dopplers and FHR STV and arterial cord blood pH. Logistic regression analysis was used to determine which factors amongst maternal characteristics, labor onset, indication of labor induction, estimated fetal weight (EFW), maternal-fetal Dopplers, FHR STV and Dawes-Redman criteria were significant predictors of composite neonatal morbidity and arterial cord blood pH less than 7.2.Result: Of the 218 cases, 12 (5.5%) women were delivered by emergency operative delivery for pathological CTG, and 42 babies (19.3%) had composite neonatal morbidities. Arterial cord blood pH was not associated with maternal-fetal Doppler indices and FHR STV, but rather it was associated with maternal age and body mass index. The composite neonatal morbidity and arterial cord blood pH less than 7.2 were not significantly associated with maternal characteristics, labor onset, indication of labor induction, pre-labor assessment of EFW, maternal-fetal Doppler indices, FHR STV and Dawes-Redman criteria by computerized CTG.Conclusion: In unselected women in latent phase of labor or undergoing induction of labor at term, admission maternal-fetal Doppler indices, FHR STV and Dawes-Redman criteria are not predictive of composite neonatal morbidity.

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network

Background

Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions.

Methods

In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML.

Results

Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively

Conclusions

Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.

A novel modular fetal ECG STAN and HRV analysis: Towards robust hypoxia detection

This paper introduces a comprehensive fetal Electrocardiogram (fECG) Signal Extraction and Analysis Virtual Instrument that integrates various methods for detecting the R-R Intervals (RRIs) as a means to determine the fetal Heart Rate (fHR) and therefore facilitates fetal Heart Rate Variability (HRV) signal analysis. Moreover, it offers the capability to perform advanced morphological fECG signal analysis called ST segment Analysis (STAN) as it seamlessly allows the determination of the T-wave to QRS complex ratio (also called T/QRS) in the fECG signal. The integration of these signal processing and analytical modules could help clinical researchers and practitioners to noninvasively monitor and detect the life threatening hypoxic conditions that may arise in different stages of pregnancy and more importantly during delivery and could therefore lead to the reduction of unnecessary C-sections. In our experiments we used real recordings from a Fetal Scalp Electrode (FSE) as well as maternal abdominal electrodes. This Virtual Instrument (Toolbox) not only serves as a desirable platform for comparing various fECG extraction signal processing methods, it also provides an effective means to perform STAN and HRV signal analysis based on proven ECG morphological as well as Autonomic Nervous System (ANS) indices to detect hypoxic conditions.

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.

Types of intrapartum hypoxia on the cardiotocograph (CTG): do they have any relationship with the type of brain injury in the MRI scan in term babies?

Electronic foetal monitoring using cardiotocography is aimed at the timely recognition and management of foetal hypoxia. The primary objective of this study was to examine whether a relationship exists between the types of foetal hypoxia (acute, subacute, evolving, chronic), as identified on cardiotocography and the nature of hypoxic ischaemic encephalopathy, as observed on MRI scans after birth. We conducted a retrospective study of 16 babies born (out of 52,187 births) at St George's Hospital in London during 2006-2017 with a postnatal diagnosis of HIE. Of the 16 babies, only 11 had both MRI scans and CTG traces available. Of those, 9 showed evidence of intrapartum hypoxia on CTG, but only 6 demonstrated evidence of HIE on MRI. Those with acute hypoxia showed abnormalities in the basal ganglia and thalami. A gradually evolving hypoxia or subacute hypoxia was associated with lesions in myelination and cerebral cortex.Impact StatementWhat is already known on this subject? It has been reported that inter-observer agreement for CTG interpretation is low (30%) when pattern recognition based guidelines are used (Rhöse et al. 2014; Reif et al. 2016), even amongst 'experts' (Hruban et al. 2015). Furthermore, it has been shown that CTG traces do not reliably predict neonatal encephalopathy (Spencer et al. 1997).What do the results of this study add? Our study indicates that if 'types of intrapartum hypoxia' are used for interpretation, then inter-observer agreement increases to 81%, from the reported 30% when traces are classified into 'normal, suspicious and pathological' using guidelines based on 'pattern recognition'. Furthermore, our study shows a good correlation between the type of intrapartum hypoxia observed on CTG trace and the nature of injury observed on the MRI.What are the implications of these findings for clinical practise and/or further research? Improving inter-observer agreement of CTGs with the use of pattern recognition in combination with the good correlation to MRI scan findings ultimately leads to better management and post-natal outcomes. This is evidenced by the fact that after the introduction of physiology-based CTG interpretation and mandatory competency testing on CTG interpretation for all staff in 2010, St. George's Maternity Unit has half the nationally reported rate of cerebral palsy.

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

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

Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK)

The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH <7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis "mimicking" expert clinicians and those derived from "data-driven" analyses; a critical review of the results from two randomized controlled trials testing the former in clinical practice; and relevant insights from modern physiology-based studies. We concluded that the automated algorithms performed comparably to each other and to clinical assessment of the CTG. However, the sensitivity and specificity urgently need to be improved (both computerized and visual assessment). Data-driven CTG evaluation requires further work with large multicenter datasets based on well-defined labor outcomes. And before first tests in the clinic, there are important lessons to be learnt from clinical trials that tested automated algorithms mimicking expert CTG interpretation. In addition, transabdominal fetal electrocardiogram monitoring provides reliable CTG traces and variability estimates; and fetal electrocardiogram waveform analysis is subject to promising new research. There is a clear need for close collaboration between computing and clinical experts. We believe that progress will be possible with multidisciplinary collaborative research.

Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network

Background: Electronic fetal monitoring (EFM) is widely applied as a routine diagnostic tool by clinicians using fetal heart rate (FHR) signals to prevent fetal hypoxia. However, visual interpretation of the FHR usually leads to significant inter-observer and intra-observer variability, and false positives become the main cause of unnecessary cesarean sections. Goal: The main aim of this study was to ensure a novel, consistent, robust, and effective model for fetal hypoxia detection. Methods: In this work, we proposed a novel computer-aided diagnosis (CAD) system integrated with an advanced deep learning (DL) algorithm. For a 1-dimensional preprocessed FHR signal, the 2-dimensional image was transformed using recurrence plot (RP), which is considered to greatly capture the non-linear characteristics. The ultimate image dataset was enriched by changing several parameters of the RP and was then used to feed the convolutional neural network (CNN). Compared to conventional machine learning (ML) methods, a CNN can self-learn useful features from the input data and does not perform complex manual feature engineering (i.e., feature extraction and selection). Results: Finally, according to the optimization experiment, the CNN model obtained the average performance using optimal configuration across 10-fold: accuracy = 98.69%, sensitivity = 99.29%, specificity = 98.10%, and area under the curve = 98.70%. Conclusion: To the best of our knowledge, this approached achieved better classification performance in predicting fetal hypoxia using FHR signals compared to the other state-of-the-art works. Significance: In summary, the satisfied result proved the effectiveness of our proposed CAD system for assisting obstetricians making objective and accurate medical decisions based on RP and powerful CNN algorithm.

MicroRNA-210 expression during childbirth and postpartum as a potential biomarker of acute fetal hypoxia

OBJECTIVE

To explore whether miR-210 expression can be used as a diagnostic and prognostic marker in acute fetal hypoxia.

METHODS

Whole blood samples of 29 women and their fetuses without hypoxia and 24 women and their fetuses with hypoxia were analysed in this study. Reverse transcription and quantitative real-time PCR were used to measure the expression of miR-210. Expression level differences between the control and hypoxic group in labour time and postpartum change fold were analyzed by standard statistical tests.

RESULTS

We confirmed that miR-210 is significantly more upregulated in fetal blood with acute hypoxia when compared to maternal blood (P Conclusions: Our study confirmed miR-210 upregulation in the blood of pregnant women with acute fetal hypoxia at the time of labour compared to pregnant women without acute fetal hypoxia. Additional investigation should be done to determine miR-210 clearance and the possibility of using miR-210 as a diagnostic and prognostic marker.

A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.

[Fetal heart rate analysis: Evaluation of an in situ training program on cardiotocography interpretation during labor in the Auvergne-Rhône-Alpes region (France)]

OBJECTIVES

To evaluate an in situ training program on caradiotocography interpretation during labor in the Auvergne-Rhône-Alpes region (France).

METHODS

Fifteen hospital maternity unit took part to an "outreach visit" training on fetal cardiotocography interpretation between November 2011 and 2015. Professionals were asked to answer to a 10 questions test based on the French classification of fetal heart rate, at inclusion (Test 0: T0), immediately after (Test 1: T1), and long time after the training (Test 2: T2). The mean score for each maternity (T0, T1, T2) was compared individually. Subgroup analysis considered the level of perinatal care of each maternity (level 1 or 2) and the type of practice (public or private).

RESULTS

The study included 332 healthcare professionals belonging to 8 level 1 (53.5%) and 7 level 2 (47.7%) maternity units. The T0 mean score was 4.79 (IC 95% [4.54; 5.02]) instead of 6.71(IC 95% [6.49; 6.93]) at T1 (P<0.05). Seventeen professionals (22.9%) answered T2 with a mean time of 35.2 months (Median value: 40 months) and a mean score of 5.32. The mean score was significantly higher at T2 than at T0 (5.32-IC 95%[4.94-5.70] (P<0.001) but lower than the score at T1 (P<0.05).

CONCLUSION

An "outreach visit" training on fetal cardiotocography interpretation improves theknowledge of healthcare professionals at short and long term.

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring

Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01-real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direct Fetal Electrocardiogram Database), which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data (i.e., around 88,000 R waves) for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting.

Investigating pH based evaluation of fetal heart rate (FHR) recordings

Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms.

FETAL HEART RATE CLASSIFICATION BY NON-PARAMETRIC BAYESIAN METHODS

In this paper, we propose an application of non-parametric Bayesian (NPB) models to classification of fetal heart rate recordings. More specifically, the models are used to discriminate between fetal heart rate recordings that belong to fetuses that may have adverse asphyxia outcomes and those that are considered normal. In our work we rely on models based on hierarchical Dirichlet processes. Two mixture models were inferred from recordings that represent healthy and unhealthy fetuses, respectively. The models were then used to classify new recordings. We compared the classification performance of the NPB models with that of support vector machines on real data and concluded that the NPB models achieved better performance.

Cardiotocography and the evolution into computerised cardiotocography in the management of intrauterine growth restriction

Timely recognition and appropriate management of high-risk pregnancies, such as intrauterine growth restriction (IUGR), are of paramount importance for every obstetrician. After the initial screening of IUGR fetuses through sonographic fetometry and Doppler, the focus is shifted to the appropriate monitoring and timing of delivery. This can, especially in cases of early IUGR, become a very difficult task. At this point, cardiotocography (CTG) is introduced as a major tool in the day-to-day monitoring of the antenatal well-being of the IUGR fetus. Since the first introduction of CTG up to the nowadays widely spreading implementation of computerised CTG in the clinical practice, there has been great progress in the recording of the fetal heart rate, as well as its interpretation. Focus of this review is to offer an understanding of the evolution of CTG from its early development to modern computerised methods and to provide an insight as to where the future of CTG is leading, especially in the monitoring of IUGR.