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This article describes the methods used to build a large-scale database of more than 250,000 electronic fetal monitoring (EFM) records linked to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome. The database can be used to investigate how birth outcome is related to clinical and EFM features. The main steps involved in building the database were: (1) Acquiring the raw EFM recording and clinical records for each birth. (2) Assigning each birth to an objectively defined outcome class that included normal, acidosis, and hypoxic-ischemic encephalopathy. (3) Removing all personal health information from the EFM recordings and clinical records. (4) Preprocessing the deidentified EFM records to eliminate duplicates, reformat the signals, combine signals from different sensors, and bridge gaps to generate signals in a format that can be readily analyzed. (5) Post-processing the repaired EFM recordings to extract key features of the fetal heart rate, uterine activity, and their relations. (6) Populating a database that links the clinical information, EFM records, and EFM features to support easy querying and retrieval. •A multi-step process is required to build a comprehensive database linking electronic temporal fetal monitoring signals to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome.•The current database documents more than 250,000 births including almost 4,000 acidosis and 400 HIE cases. This represents more than 80% of the births that occurred in 15 Northern California Kaiser Permanente Hospitals between 2011-2019. This is a valuable resource for studying the factors predictive of outcome.•The signal processing code and schemas for the database are freely available. The database will not be permitted to leave Kaiser firewalls, but a process is in place to allow interested investigators to access it.

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Artificial intelligence (AI) is gaining increasing interest in the field of medicine because of its capacity to process big data and pattern recognition. Cardiotocography (CTG) is widely used for the assessment of foetal well-being and uterine contractions during pregnancy and labour. It is characterised by inter- and intraobserver variability in interpretation, which depends on the observers' experience. Artificial intelligence (AI)-assisted interpretation could improve its quality and, thus, intrapartal care. Cardiotocography (CTG) raw signals from labouring women were extracted from the database at the University Hospital of Bern between 2006 and 2019. Later, they were matched with the corresponding foetal outcomes, namely arterial umbilical cord pH and 5-min APGAR score. Excluded were deliveries where data were incomplete, as well as multiple births. Clinical data were grouped regarding foetal pH and APGAR score at 5 min after delivery. Physiological foetal pH was defined as 7.15 and above, and a 5-min APGAR score was considered physiologic when reaching ≥7. With these groups, the algorithm was trained to predict foetal hypoxia. Raw data from 19,399 CTG recordings could be exported. This was accomplished by manually searching the patient's identification numbers (PIDs) and extracting the corresponding raw data from each episode. For some patients, only one episode per pregnancy could be found, whereas for others, up to ten episodes were available. Initially, 3400 corresponding clinical outcomes were found for the 19,399 CTGs (17.52%). Due to the small size, this dataset was rejected, and a new search strategy was elaborated. After further matching and curation, 6141 (31.65%) paired data samples could be extracted (cardiotocography raw data and corresponding maternal and foetal outcomes). Of these, half will be used to train artificial intelligence (AI) algorithms, whereas the other half will be used for analysis of efficacy. Complete data could only be found for one-third of the available population. Yet, to our knowledge, this is the most exhaustive and second-largest cardiotocography database worldwide, which can be used for computer analysis and programming. A further enrichment of the database is planned.

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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.

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BACKGROUND: Fetal cardiac well-being is essential during labor as the delivery is at risk for fetal distress. Continuous monitoring by cardiotocography (CTG) is daily used to record the fetal heart rate (FHR) but this technique has important drawbacks in clinical use. OBJECTIVES: We propose to monitor FHR with a non-invasive technique, using multimodal recordings of the fetus cardiac activity, associating electrocardiographic (ECG) and phonocardiographic (PCG) sensors. The aim of this study is to evaluate the quality of these multimodal FHR estimations by comparison with CTG, based on clinical criteria. METHODS: A clinical protocol was established and a prospective open label study was carried out in the University Hospital of Grenoble. The objective was to record thoracic and abdominal PCG and ECG signals on pregnant women over 37 WG (weeks of gestation), simultaneously with CTG recordings. Adapted signal processing algorithms were then applied on abdominal PCG and ECG signals to extract FHR. Quantitative evaluation was carried out on FHR estimations compared with FHR extracted from CTG. RESULTS: A total of 40 recordings were performed. Due to technical mistakes the analysis was made possible for 38. 35 recordings allowed a FHR follow-up by ECG or PCG, 30 recordings allowed a FHR follow-up by PCG only, 25 recordings allowed a FHR follow-up by ECG only and 20 recordings allowed a FHR follow-up by both ECG and PCG. CONCLUSION: Reliable multimodal recording of FHR associating ECG and PCG sensors is possible during the last month of pregnancy. These positive results encourage the study of multimodal FHR recording during labor and delivery.

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OBJECTIVES: Congenital heart defects (CHD) are the most common inherited abnormalities. Intrapartum cardiotocography (CTG) is still considered a "gold standard" during labor. However, there is a lack of evidence regarding the interpretation of intrapartum CTG in fetuses with CHD. Therefore, the study aimed to compare intrapartum CTG in normal fetuses and fetuses with CHD and describe the association between CTG and neonatal outcomes. METHODS: The present study is a retrospective analysis of the CTG of 395 fetuses. There were three study groups: Group 1: 185 pregnancies with a prenatal diagnosis of CHD, Group 2: 132 high-risk pregnancies without CHD, and Group 3: 78 low-risk pregnancies without CHD. RESULTS: Abnormal CTG was present statistically OR=3.4 (95%CI: 1.61-6.95) more often in Group 1. The rate of the emergency CS was higher in this group OR=3 (95%CI: 1.3-3.1). Fetuses with CHD and abnormal CTG were more often scored ≤7 Apgar, with no difference in acidemia. The multivariate regression model for Group 1 does not show clinical differences between Apgar scores or CTG assessment in neonatal acidemia prediction. CONCLUSIONS: CTG in fetuses with CHD should be interpreted individually according to the type of CHD and conduction abnormalities. Observed abnormalities in CTG are associated with the fetal heart defect itself. Preterm delivery and rapid cesarean delivery lead to a higher rate of neonatal complications. Health practitioners should consider this fact during decision-making regarding delivery in cases complicated with fetal cardiac problems.

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OBJECTIVES: We aimed to evaluate the cardiotocograph (CTG) traces of 224 women infected with novel coronavirus 2019 (COVID-19) and analyze whether changes in the CTG traces are related to the severity of COVID-19. METHODS: We designed a prospective cohort study. Two-hundred and twenty-four women who had a single pregnancy of 32 weeks or more, and tested positive for SARS-CoV-2 were included. Clinical diagnosis and classifications were made according to the Chinese management guideline for COVID-19 (version 6.0). Patients were classified into categories as mild, moderate, severe and the CTG traces were observed comparing the hospital admission with the third day of positivity. RESULTS: There was no statistically significant relationship between COVID-19 severity and CTG category, variability, tachycardia, bradycardia, acceleration, deceleration, and uterine contractility, Apgar 1st and 5th min. CONCLUSIONS: Maternal COVID-19 infection can cause changes that can be observed in CTG. Regardless of the severity of the disease, COVID-19 infection is associated with changes in CTG. The increase in the baseline is the most obvious change.

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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.

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OBJECTIVES: Computer CTG analysis (cCTG) included short-term variation (STV) is one of the methods of monitoring fetal condition during delivery. The aim of our study was to define appropriability of STV measured within 1 hour before delivery in prediction of neonatal outcomes. MATERIAL AND METHODS: The retrospective study included 1014 pregnant women, who gave birth in the Department of Obstetrics and Perinatology. Participants were divided into two groups: group 1 - term pregnancies (37-41 weeks) and group 2 - preterm pregnancies (lower than 37 weeks). In each of them, two subgroups have been separated: control (STV ≥ 3 ms) and study group (STV < 3 ms). RESULTS: In both groups 1 and 2, there were no statistically significant differences related to Apgar scores in 1st, 3rd and 5th minute between group with STV < 3 ms and group with STV > 3 ms Moreover, for 37-41 weeks the sensitivity, specificity, positive predictive value and negative predictive value were: 22.7%, 83.9%, 3.3% and 97.8% and for lower than 37: 45.7%, 65.4%, 47.1%, 64.2% in 1st minute after delivery. In group 1 the area under curve (AUC) measurements were 0.45 (95% CI: 0.32-0.58) for 1st minute and 0.55 (95% CI: 0.35-0.74) for 5th minute and in group 2: 0.58 (95% CI: 0.45-0.71) for 1st minute and 0.57 (95% CI: 0.42-0.72) for 5th minute. CONCLUSIONS: High specificity and negative predictive value of STV indicates a good Apgar score of newborns in term pregnancies. Analysis of STV in preterm pregnancy is not clear. Fetal well-being in preterm pregnancy should include STV and other non-invasive and invasive tools.

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OBJECTIVE: To determine the perinatal outcomes in fetuses with baseline fetal heart rate changes with preceding decelerations on the cardiotocography (CTG) trace, and to interpret CTG traces from the aspect of fetal physiology. MATERIALS AND METHODS: A retrospective analysis of 500 consecutive CTG traces was carried out. The presence of repetitive variable and late decelerations followed by the changes in the baseline including baseline tachycardia and abnormal baseline variability were determined. Perinatal outcomes including Apgar scores and umbilical arterial pH at birth, NNU admission, and meconium-stained amniotic fluid were analyzed. We interpreted the changes in CTG based on fetal physiology. RESULTS: When repetitive variable and late decelerations were present without tachycardia (n = 81), none of the fetuses had an Apgar score <7 at 5 minutes or an umbilical cord pH <7. After the onset of fetal tachycardia (n = 262), fetuses showed decreased Apgar scores and umbilical arterial pH(p < .01), however, there was no significant difference in the rate of abnormal 5 min Apgar score, abnormal PH and NNU admission, if the baseline variability remained normal. However, if the baseline variability was abnormal (n = 44), (either increased or reduced) after tachycardia, there was a statistically significant increase in poor perinatal outcomes. Fetuses with abnormal versus normal variability had lower Apgar scores ≤7 at 5 min (29.6 versus 0.9%, p = .000); umbilical cord arterial pH <7 at birth (29.5 versus 0%, p = .000); increased admission to the NNU (27.3 versus 3.7%, p = .000) and increased incidence of meconium-stained amniotic fluid (38.6 versus 22.5%, p = .024). These serial changes in CTG could be interpreted and predicted by the application of fetal physiology. CONCLUSIONS: There were significant differences in perinatal outcomes when fetuses were exposed to evolving intrapartum hypoxic stress culminating in an abnormal baseline fetal heart rate variability, which was preceded by repetitive decelerations, followed by an increase in the baseline heart rate. However, despite ongoing decelerations, if the baseline variability remained normal, none of the fetuses had a pH of <7. Therefore, the knowledge of fetal physiological response to evolving hypoxic stress can be reliably used to determine fetal compensation.

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Introduction: The intrapartum cardiotocography (CTG) classification system by FIGO in 2015 (FIGO2015) was introduced to simplify CTG interpretation, but it is not harmonized with the fetal ECG ST analysis (STAN) algorithm from 2007 (STAN2007), which is based on the FIGO CTG system from 1987. The study aimed to determine time courses and sensitivity between the systems in classifying CTG + ST events to indicate metabolic acidosis at birth.Material and methods: Forty-four cases with umbilical cord artery metabolic acidosis were retrieved from a European multicenter database. CTG patterns and timing of the first occurring significant ST events were evaluated post hoc in consensus by an expert panel and sensitivity statistics were performed. Wilcoxon's matched-pairs signed-ranks test and McNemar's test were used with a two-tailed p < .05 regarded significant.Results: STAN2007 had a higher sensitivity (73 versus 43%, p = .0002) and alarmed for metabolic acidosis in mean 34 min earlier than the FIGO2015 system did (p = .002). In every fourth case, the time difference was ≥20 min.Conclusions: In this simulation study, surveillance with STAN2007 combined with fetal ECG ST analysis had a significantly higher sensitivity and would have alarmed for metabolic acidosis significantly earlier than the new FIGO system would have.

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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.

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The aim of this study is to investigate that fetal heart rates (fHR) extracted from fetal phonocardiography (fPCG) could convey similar information of fHR from cardiotocography (CTG). Four-channel fPCG sensors made of low cost (<$1) ceramic piezo vibration sensor within 3D-printed casings were used to collect abdominal phonogram signals from 20 pregnant mothers (>34 weeks of gestation). A novel multi-lag covariance matrix-based eigenvalue decomposition technique was used to separate maternal breathing, fetal heart sounds (fHS) and maternal heart sounds (mHS) from abdominal phonogram signals. Prior to the fHR estimation, the fPCG signals were denoised using a multi-resolution wavelet-based filter. The proposed source separation technique was first tested in separating sources from synthetically mixed signals and then on raw abdominal phonogram signals. fHR signals extracted from fPCG signals were validated using simultaneous recorded CTG-based fHR recordings.The experimental results have shown that the fHR derived from the acquired fPCG can be used to detect periods of acceleration and deceleration, which are critical indication of the fetus' well-being. Moreover, a comparative analysis demonstrated that fHRs from CTG and fPCG signals were in good agreement (Bland Altman plot has mean = -0.21 BPM and ±2 SD = ±3) with statistical significance (p < 0.001 and Spearman correlation coefficient ρ = 0.95). The study findings show that fHR estimated from fPCG could be a reliable substitute for fHR from the CTG, opening up the possibility of a low cost monitoring tool for fetal well-being.

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An automated method to assess the fetal physiological development is introduced which uses the component intervals between fetal cardiac valve timings and the Q-wave of fetal electrocardiogram (fECG). These intervals were estimated automatically from one-dimensional Doppler Ultrasound and noninvasive fECG. We hypothesize that the fetal growth can be estimated by the cardiac valve intervals. This hypothesis was evaluated by modeling the fetal development using the cardiac intervals and validating against the gold standard gestational age identified by Crown-Rump Length (CRL). Among the intervals, electromechanical delay time, isovolumic contraction time, ventricular filling time and their interactions were selected in a stepwise regression process that used gestational age as the target in a cohort of 57 fetuses. Compared with the gold standard age, the newly proposed regression model resulted in a mean absolute error of 3.8 weeks for all recordings and 2.7 weeks after excluding the low quality recordings. Since Fetal Heart Rate Variability (FHRV) has been proposed in the literature for assessing the fetal development, we compared the performance of gestational age estimation by our new valve-interval based method, vs. FHRV, while assuming the CRL as the gold standard. The valve interval-based method outperformed both the model based on FHRV. Results of evaluation for 30 abnormal cases showed that the new method is less affected by arrhythmias such as tachycardia and bradycardia compared to FHRV, however certain types of heart anomalies cause large errors (more than 10 weeks) with respect to the CRL-based gold standard age. Therefore, discrepancies between the regression based estimation and CRL age estimation could indicate the abnormalities. The cardiac valve intervals have been known to reflect the autonomic function. Therefore the new method potentially provides a novel approach for assessing the development of fetal autonomic nervous system, which may be growth curve independent.

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UNLABELLED: Monitoring of fetal heart rate is one of the basic components of obstetrical care, in which the cardiotocography remains the gold standard and screening method in early diagnosis of fetal hypoxia, even after introduction of other selective methods of intrauterine monitoring of fetal well-being. The review article is divided into several parts: pathophysiology of fetal oxygenation, fetal heart rate and changes of fetal hemodynamics, and rules for fetal heart rate auscultation. The main principles of cardiotocographic monitoring and evaluation of ante- and intrapartrum recordings according to the FIGO criteria from 1986 and evaluation of intrapartum recordings according to the 2015 FIGO recommendations are mentioned. At the end a comparative table of 1986 FIGO and 2015 FIGO criteria is presented. DESIGN: Review.

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The use of continuous intrapartum electronic fetal heart rate monitoring (EFM) using a cardiotocograph (CTG) was developed to enable obstetricians and midwives to analyse the changes of fetal heart rate during labour so as to institute timely intervention to avoid intrapartum hypoxic-ischaemic injury. Although CTG was initially developed as a screening tool to predict fetal hypoxia, its positive predictive value for intrapartum fetal hypoxia is approximately only 30%. Even though different international classifications have been developed with the aim of defining combinations of features that help predict intrapartum fetal hypoxia, the false-positive rate of the CTG is high (60%). Moreover, there has not been a demonstrable improvement in the rate of cerebral palsy or perinatal deaths since the introduction of CTG into clinical practice approximately 45 years ago. However, there has been a significant increase in intrapartum caesarean section and operative vaginal delivery rates. Unfortunately, existing guidelines employ the visual interpretation of CTG based on 'pattern recognition', which is fraught with inter- and intra-observer variability. Therefore, clinicians need to understand the physiology behind fetal heart rate changes and to respond to them accordingly, instead of purely relying on guidelines for management. It is very likely that such a 'physiology-based' approach would reduce unnecessary operative interventions and improve perinatal outcomes whilst reducing the need for 'additional tests' of fetal well-being.

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