SisPorto 4.0 - computer analysis following the 2015 FIGO Guidelines for intrapartum fetal monitoring

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

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

Fetal Heart Rate Preprocessing Techniques: A Scoping Review

Monitoring fetal heart rate (FHR) through cardiotocography is crucial for the early diagnosis of fetal distress situations, necessitating prompt obstetrical intervention. However, FHR signals are often marred by various contaminants, making preprocessing techniques essential for accurate analysis. This scoping review, following PRISMA-ScR guidelines, describes the preprocessing methods in original research articles on human FHR (or beat-to-beat intervals) signal preprocessing from PubMed and Web of Science, published from their inception up to May 2021. From the 322 unique articles identified, 54 were included, from which prevalent preprocessing approaches were identified, primarily focusing on the detection and correction of poor signal quality events. Detection usually entailed analyzing deviations from neighboring samples, whereas correction often relied on interpolation techniques. It was also noted that there is a lack of consensus regarding the definition of missing samples, outliers, and artifacts. Trends indicate a surge in research interest in the decade 2011-2021. This review underscores the need for standardizing FHR signal preprocessing techniques to enhance diagnostic accuracy. Future work should focus on applying and evaluating these methods across FHR databases aiming to assess their effectiveness and propose improvements.

Is lower fetal heart rate variability a susceptibility marker to the impact of negative coparenting on infant regulatory capacity?

Lower fetal heart rate variability (FHRV) may be a prenatal endophenotypic susceptibility marker and increase the impact of both positive and negative coparenting on infant regulatory capacity. This study analyzed the moderator role of FHRV in the association between positive and negative coparenting and infant regulatory capacity at 3 months. The sample comprised 86 first-born infants and their mothers and fathers recruited at a public Health Service in Northern Portugal. FHRV was recorded during routine cardiotocography examination at the third trimester of gestation. Mothers and fathers reported on coparenting and infant regulatory capacity at 2 weeks and 3 months postpartum. FHRV moderated the association between mother's and father's negative coparenting at 2 weeks postpartum and infant regulatory capacity at three months. Infants with low FHRV presented higher regulatory capacity when mothers or fathers reported less negative coparenting, while lower regulatory capacity when mothers or fathers reported more negative coparenting, than infants with high FHRV. Findings suggested lower FHRV as a prenatal endophenotypic susceptibility marker that increases the impact of negative coparenting on infant regulatory capacity.

Fetal heart rate variability and infant self-regulation: the impact of mother's prenatal depressive symptoms

BACKGROUND

Foetal heart rate (FHR) variability is considered a marker of foetal neurobehavioral development associated with infant self-regulation and thus may be an early precursor of the adverse impact of mother's prenatal depressive symptoms on infant self-regulation.

OBJECTIVE

This study analysed the mediator role of FHR variability in the association between mother's prenatal depressive symptoms and infant self-regulation at three months.

METHODS

The sample comprised 86 first-born infants and their mothers. Mothers reported on depressive symptoms at the first trimester of pregnancy and on depressive symptoms and infant self-regulation at three months postpartum. FHR variability was recorded during routine cardiotocography at the third trimester of pregnancy. A mediation model was tested, adjusting for mother's postnatal depressive symptoms.

RESULTS

Higher levels of mother's prenatal depressive symptoms were associated with both lower FHR variability and lower infant self-regulation at three months. FHR variability was associated with infant self-regulation and mediated the association between mother's prenatal depressive symptoms and infant self-regulation at three months.

CONCLUSION

Findings suggested FHR variability as an early precursor of infant self-regulation that underlies the association between mother's prenatal depressive symptoms and infant self-regulation. Infants of mothers with higher levels of prenatal depressive symptoms could be at risk of self-regulation problems, partially due to their lower FHR variability.

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.

Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data

Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0-10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors.

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.

A Fetal Well-Being Diagnostic Method Based on Cardiotocographic Morphological Pattern Utilizing Autoencoder and Recursive Feature Elimination

Cardiotocography (CTG), which measures the fetal heart rate (FHR) and maternal uterine contractions (UC) simultaneously, is used for monitoring fetal well-being during delivery or antenatally at the third trimester. Baseline FHR and its response to uterine contractions can be used to diagnose fetal distress, which may necessitate therapeutic intervention. In this study, a machine learning model based on feature extraction (autoencoder), feature selection (recursive feature elimination), and Bayesian optimization, was proposed to diagnose and classify the different conditions of fetuses (Normal, Suspect, Pathologic) along with the CTG morphological patterns. The model was evaluated on a publicly available CTG dataset. This research also addressed the imbalance nature of the CTG dataset. The proposed model has a potential application as a decision support tool to manage pregnancies. The proposed model resulted in good performance analysis metrics. Using this model with Random Forest resulted in a model accuracy of 96.62% for fetal status classification and 94.96% for CTG morphological pattern classification. In rational terms, the model was able to accurately predict 98% Suspect cases and 98.6% Pathologic cases in the dataset. The combination of predicting and classifying fetal status as well as the CTG morphological patterns shows potential in monitoring high-risk pregnancies.

Henriques T. Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study

Introduction

Perinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model.

Objectives

This exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices.

Methods

Single gestations data from a retrospective unicentric study from Centro Hospitalar e Universitário do Porto de São João (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models.

Results

The data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%].

Conclusion

Both BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).

Artificial intelligence and machine learning in cardiotocography: A scoping review

INTRODUCTION

Artificial intelligence (AI) is gaining more interest in the field of medicine due to its capacity to learn patterns directly from data. This becomes interesting for the field of cardiotocography (CTG) interpretation, since it promises to remove existing biases and improve the well-known issues of inter- and intra-observer variability.

MATERIAL AND METHODS

The objective of this study was to map current knowledge in AI-assisted interpretation of CTG tracings and thus, to present different approaches with their strengths, gaps, and limitations. The search was performed on Ovid Medline and PubMed databases. The Preferred Reporting Items for Systematic Reviews and meta-Analysis for Scoping Reviews (PRISMA-ScR) guidelines were followed.

RESULTS

We summarized 40 different studies investigating at least one algorithm or system to classify CTG tracings. In addition, the Oxford Sonicaid system is presented because of its wide use in clinical practice.

CONCLUSIONS

There are several promising approaches in this area, but none of them has gained big acceptance in clinical practice. Further investigation and refinement of the algorithms and features are needed to achieve a validated decision-support system. For this purpose, larger quantities of curated and labeled data may be necessary.

Computerized analysis of cardiotocograms in clinical practice and the SisPorto® system thirty-two years after: technological, physiopathological and clinical studies

OBJECTIVES

The objective of this study is to present the why, what and how about computerized analysis of cardiotocograms (cCTG) and the SisPorto system for cCTG.

CONTENT

A narrative review about cCTG and the SisPorto system for cCTG is presented. The meta-analysis of randomized controlled trials (RCT) performed so far have evidenced that cCGT compared to traditional CTG analysis may save time spent in hospital for women, in the antepartum period, and is objective with at least equivalent results in maternal and perinatal outcomes, both in the ante and intrapartum periods. The SisPorto system for cCTG closely follows the FIGO guidelines for fetal monitoring. It may be used both in the ante and intrapartum periods, alone or connected to a central monitoring station, with simultaneous monitoring of fetal and maternal signals, not only in singletons but also in twins. It has been assessed in technical, physiopathological and clinical studies, namely in one large multicentric international RCT during labor and two meta-analysis.

SUMMARY AND OUTLOOK

There is evidence that cCTG may be useful in clinical practice with advantages compared to traditional CTG analysis, although without clear impact on the decrease of preventable maternal and perinatal mortality and morbidity. More studies are warranted, namely on technical improvements and assessment in larger studies in a wider range of clinical settings.

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.

Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework

Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models’ superior performance and effectiveness.

Heart rate markers for prediction of fetal acidosis in an experimental study on fetal sheep

To overcome the difficulties in interpreting fetal heart rate (FHR), several tools based on the autonomic nervous system and heart rate variability (HRV) have been developed. The objective of this study was to use FHR and HRV parameters for the prediction of fetal hypoxia. It was an experimental study in the instrumented fetal sheep. Repeated umbilical cord occlusions were performed to achieve severe acidosis. Hemodynamic parameters, ECG, and blood gases were analyzed. The variables used were heart rate baseline, HRV analysis (RMSSD, SDNN, LF, HF, HFnu, Fetal Stress Index (FSI), …), and morphological analysis of decelerations. The gold standard used to classify hypoxia was the fetal arterial pH (pH < 7.10). Different multivariable statistical methods (logistic regression and decision trees) were applied for the detection of acidosis. 21 lambs were instrumented. A total of 130 pairs of FHR/fetal pH analysis were obtained of which 29 in the acidosis group and 101 in the non-acidosis group. After logistic regression model with bootstrap resampling and stepwise backward selection, only one variable was selected, FSI. The AUC of FSI alone in this model was 0.81 with a sensitivity of 0.66, specificity of 0.88, PPV of 0.61, and NPV of 0.90 considering a threshold of 68. Decision trees with CHAID and CART algorithms showed a sensitivity of 0.48 and 0.59, respectively, and a specificity of 0.94 for both. All employed methods identified HRV variables as the most predictive of acidosis. The primary variables selected automatically were those from the HRV. Supporting the use of FHRV measures for the screening of fetal acidosis during labour is interesting.

Fetal Heart Rate Fragmentation

Intrapartum fetal monitoring's primary goal is to avoid adverse perinatal outcomes related to hypoxia/acidosis without increasing unnecessary interventions. Recently, a set of indices were proposed as new biomarkers to analyze heart rate (HR), termed HR fragmentation (HRF). In this work, the HRF indices were applied to intrapartum fetal heart rate (FHR) traces to evaluate fetal acidemia. The fragmentation method produces four indices: PIP-Percentage of inflection points; IALS-Inverse of the average length of acceleration/deceleration segments; PSS-Percentage of short segments; PAS-Percentage of alternating segments. On the other hand, the symbolic approach studied the existence of different patterns of length four. We applied the measures to 246 selected FHR recordings sampled at 4 and 2 Hz, where 39 presented umbilical artery's pH ≤ 7.15. When applied to the 4 Hz FHR, the PIP, IASL, and PSS showed significantly higher values in the traces from acidemic fetuses. In comparison, the percentage of "words"W 1 h andW 2 s showed lower values for those traces. Furthermore, when using the 2 Hz, only IASL, W 0, andW 2 m achieved significant differences between traces from both acidemic and normal fetuses. Notwithstanding, the ideal sampling frequency is yet to be established. The fragmentation indices correlated with Sisporto variability measures, especially short-term variability. Accordingly, the fragmentation indices seem to be able to detect pathological patterns in FHR tracings. These indices have the advantage of being suitable and straightforward to apply in real-time analysis. Future studies should combine these indexes with others used successfully to detect fetal hypoxia, improving the power of discrimination in a larger dataset.

Electronic intrapartum fetal monitoring: a systematic review of international clinical practice guidelines

BACKGROUND

Electronic fetal monitoring or fetal assessment using a cardiotocograph is currently the most commonly employed tool for intrapartum surveillance. Furthermore, there are numerous guidelines informing best practice worldwide.

OBJECTIVE

This systematic review aimed to compare and appraise all available practice guidelines on intrapartum electronic fetal monitoring to describe the similarities and variations in recommendations.

STUDY DESIGN

A systematic protocol was developed per Preferred Reporting Item for Systematic Review and Meta-Analysis Protocols. A total of 4 independent reviewers were involved with independent searches and quality assessment using the Appraisal of Guidelines for Research and Evaluation Instrument for guideline quality reporting.

RESULTS

Overall, 7 international practice guidelines were included in this systematic review. Appraisal of Guidelines for Research and Evaluation Instrument showed higher scores for scope and purpose and for clarity of presentation; however, the overall assessment varied between 25% and 89%. When individual characteristics of electronic fetal monitoring or cardiotocograph were compared, all guidelines and guidance were essentially trying to describe the characters similarly, with critical differences described in the full article.

CONCLUSION

In the context of globalization, a uniform approach for defining terminology, classifying characters and similar interpretation of results is needed for electronic fetal monitoring. Therefore, we should consider a unified, simple, logistically approved, and acceptable guideline, which is probably accepted worldwide.

A randomized controlled trial studying the effect of maternal hyperoxygenation on fetal heart rate in suspected fetal distress

OBJECTIVE

To investigate the effect of maternal hyperoxygenation on fetal heart rate (FHR) when applied for suspected fetal distress during the second stage of term labor.

APPROACH

A single-center randomized controlled trial was conducted in a tertiary care hospital in The Netherlands. Participants were included during the second stage of labor in case of an intermediary or abnormal FHR pattern. Patients were randomized to receive either 100% oxygen at 10 l/min until delivery, or conventional care without additional oxygen. The primary outcome was the change in FHR pattern before and after the onset of the study, measured as the change in depth and duration of FHR decelerations. Secondary outcome measures were features based on phase-rectified signal averaging (PRSA), baseline assignability, and deceleration characteristics of the FHR pattern.

MAIN RESULTS

Between March 2016 and April 2018, 117 women were included. The FHR pattern could be analyzed for 71 participants, the other 46 women delivered before the end of the post time-frame. A 2.3% reduction in depth and duration of FHR decelerations was found after maternal hyperoxygenation, compared to a 10% increase in the control group (p = 0.24). Maternal hyperoxygenation had a significantly positive effect on PRSA metrics, with a decrease in PRSA-acceleration capacity (p = 0.03) and PRSA-deceleration capacity (p = 0.02) in the intervention group compared to the control group.

SIGNIFICANCE

The difference in depth and duration of decelerations after the start of the study was not significantly different between both study groups. A statistically significant positive effect on PRSA-deceleration capacity and PRSA-acceleration capacity was found after maternal hyperoxygenation, which might be associated with a positive effect on neonatal outcome.

Complexity of Cardiotocographic Signals as A Predictor of Labor

Prediction of labor is of extreme importance in obstetric care to allow for preventive measures, assuring that both baby and mother have the best possible care. In this work, the authors studied how important nonlinear parameters (entropy and compression) can be as labor predictors. Linear features retrieved from the SisPorto system for cardiotocogram analysis and nonlinear measures were used to predict labor in a dataset of 1072 antepartum tracings, at between 30 and 35 weeks of gestation. Two groups were defined: Group A—fetuses whose traces date was less than one or two weeks before labor, and Group B—fetuses whose traces date was at least one or two weeks before labor. Results suggest that, compared with linear features such as decelerations and variability indices, compression improves labor prediction both within one (C-Statistics of 0.728) and two weeks (C-Statistics of 0.704). Moreover, the correlation between compression and long-term variability was significantly different in groups A and B, denoting that compression and heart rate variability look at different information associated with whether the fetus is closer to or further from labor onset. Nonlinear measures, compression in particular, may be useful in improving labor prediction as a complement to other fetal heart rate features.

Combined prenatal and postnatal prediction of early neonatal compromise risk

OBJECTIVE

Electronic fetal monitoring/cardiotocography (EFM) is nearly ubiquitous, but almost everyone acknowledges there is room for improvement. We have contextualized monitoring by breaking it down into quantifiable components and adding to that, other factors that have not been formally used: i.e. the assessment of uterine contractions, and the presence of maternal, fetal, and obstetrical risk factors. We have created an algorithm, the Fetal Reserve Index (FRI) that significantly improves the detection of at-risk cases. We hypothesized that extending our approach of monitoring to include the immediate newborn period could help us better understand the physiology and pathophysiology of the decrease in fetal reserve during labor and the transition from fetal to neonatal homeostasis, thereby further honing the prediction of outcomes. Such improved and earlier understanding could then potentiate earlier, and more targeted use of neuroprotective attempts during labor treating decreased fetal reserve and improving the fetus' transition from fetal to neonatal life minimizing risk of neurologic injury.

STUDY DESIGN

We have analyzed a 45-year-old research database of closely monitored labors, deliveries, and an additional hour of continuous neonatal surveillance. We applied the FRI prenatally and created a new metric, the INCHON index that combines the last FRI with umbilical cord blood and 4-minute umbilical artery blood parameters to predict later neonatal acid/base balance. Using the last FRI scores, we created 3 neonatal groups. Umbilical cord and catheterized umbilical artery bloods at 4, 8, 16, 32, and 64 minutes were measured for base excess, pH, and PO₂. Continuous neonatal heart rate was scored for rate, variability, and reactivity.

RESULTS

Neonates commonly do not make a smooth transition from fetal to postnatal physiology. Even in low risk babies, 85% exhibited worsening pH and base excess during the first 4 minutes; 34% of neonates reached levels considered at high risk for metabolic acidosis (≤-12 mmol/L) and neurologic injury. Neonatal heart rate commonly exhibited sustained, significant tachycardia with loss of reactivity and variability. One quarter of all cases would be considered Category III if part of the fetal tracing. Our developed metrics (FRI and INCHON) clearly discriminated and predicted low, medium, and high-risk neonatal physiology.

CONCLUSIONS

The immediate neonatal period often imposes generally unrecognized risks for the newborn. INCHON improves identification of decreased fetal reserve and babies at risk, thereby permitting earlier intervention during labor (intrauterine resuscitation) or potentially postnatally (brain cooling) to prevent neurologic injury. We believe that perinatal management would be improved by routine, continuous neonatal monitoring - at least until heart rate reactivity is restored. FRI and INCHON can help identify problems much earlier and more accurately than currently and keep fetuses and babies in better metabolic shape.

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.

Computerized analysis of cardiotocograms and ST signals is associated with significant reductions in hypoxic-ischemic encephalopathy and cesarean delivery: an observational study in 38,466 deliveries

BACKGROUND

Intrapartum cardiotocography is widely used in high-resource countries and remains at the center of fetal monitoring and the decision to intervene, but there is ample evidence of poor reliability in visual interpretation as well as limited accuracy in identifying fetal hypoxia. Combined monitoring of intrapartum cardiotocography and ST segment signals was developed to increase specificity, but analysis relies heavily on intrapartum cardiotocography interpretation and is therefore also affected by the previously referred problems. Computerized analysis was developed to overcome these limitations, aiding in the quantification of parameters that are difficult to evaluate visually, such as variability, integrating the complex guidelines of combined intrapartum cardiotocography and ST analysis, and using visual and sound alerts to prompt health care professionals to reevaluate features associated with fetal hypoxia.

OBJECTIVE

The objective of the study was to evaluate the effect of introducing a central fetal monitoring system with computerized analysis of intrapartum cardiotocography and ST signals into the labor ward of a tertiary care university hospital in which all women are continuously monitored with intrapartum cardiotocography. The incidence of adverse perinatal outcomes and intervention rates was evaluated over time.

STUDY DESIGN

In this retrospective cohort study, yearly rates of hypoxic-ischemic encephalopathy, instrumental vaginal delivery, overall cesarean delivery, and urgent cesarean delivery were obtained from the hospital's clinical databases. The rates occurring in the period from January 2001 to December 2003, before the introduction of the central monitoring system with computerized analysis of intrapartum cardiotocography and ST signals (Omniview-SisPorto), were compared with those occurring from January 2004 to December 2014, after the introduction of the system. All rates were calculated with 95% confidence intervals.

RESULTS

A total of 38,466 deliveries occurred during this period. After introduction of the system, there was a significant decrease in the number of hypoxic-ischemic encephalopathy cases per 1000 births (5.3%, 95% confidence interval [4.0-7.0] vs 2.2%, 95% confidence interval [1.7-2.8]; relative risk, 0.42, 95% confidence interval [0.29-0.61]), overall cesarean delivery rates (29.9%, 95% confidence interval [28.9-30.8] vs 28.3%, 95% confidence interval [27.8-28.8]; relative risk, 0.96, 95% confidence interval [0.92-0.99]), and urgent cesarean deliveries (21.6%, 95% confidence interval [20.7-22.4] vs 19.2%, 95% confidence interval [18.8-19.7]; relative risk, 0.91, 95% confidence interval [0.87-0.95]). The instrumental vaginal delivery rate increased (19.5%, 95% confidence interval [18.7-20.3] vs 21.4%, 95% confidence interval [21.0-21.9; relative risk, 1.07, 95% confidence interval 1.02-1.13].

CONCLUSION

Introduction of computerized analysis of intrapartum cardiotocography and ST signals in a tertiary care hospital was associated with a significant reduction in the incidence of hypoxic-ischemic encephalopathy and a modest reduction in cesarean deliveries.

Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment

Cardiotocography (CTG) is applied routinely for fetal monitoring during the perinatal period to decrease the rates of neonatal mortality and morbidity as well as unnecessary interventions. The analysis of CTG traces has become an indispensable part of present clinical practices; however, it also has serious drawbacks, such as poor specificity and variability in its interpretation. The automated CTG analysis is seen as the most promising way to overcome these disadvantages. In this study, a novel prognostic model is proposed for predicting fetal hypoxia from CTG traces based on an innovative approach called image-based time-frequency (IBTF) analysis comprised of a combination of short time Fourier transform (STFT) and gray level co-occurrence matrix (GLCM). More specifically, from a graphical representation of the fetal heart rate (FHR) signal, the spectrogram is obtained by using STFT. The spectrogram images are converted into 8-bit grayscale images, and IBTF features such as contrast, correlation, energy, and homogeneity are utilized for identifying FHR signals. At the final stage of the analysis, different subsets of the feature space are applied as the input to the least square support vector machine (LS-SVM) classifier to determine the most informative subset. For this particular purpose, the genetic algorithm is employed. The prognostic model was performed on the open-access intrapartum CTU-UHB CTG database. The sensitivity and specificity obtained using only conventional features were 57.33% and 67.24%, respectively, whereas the most effective results were achieved using a combination of conventional and IBTF features, with a sensitivity of 63.45% and a specificity of 65.88%. Conclusively, this study provides a new promising approach for feature extraction of FHR signals. In addition, the experimental outcomes showed that IBTF features provided an increase in the classification accuracy.

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.