Using uterine activity to improve fetal heart rate variability analysis for detection of asphyxia during labor

A comparative study of CTG monitoring one hour before labor in infants born with and without asphyxia

BACKGROUND AND AIM

Asphyxia is a condition arising when the infant is deprived of oxygen, causing Fetal brain damage or death, which is associated with hypoxia and hypercapnia. Although fetal Cardiotocography (CTG) can show the Fetal health status during labor, some studies have reported cases of fetal asphyxia despite reassuring CTGs. This study hence aimed to compare FHR Monitoring and uterine contractions in the last hour before delivered between two groups of infants born with and without asphyxia.

METHODOLOGY

The study was conducted on 70 pregnant women who delivered Taleghani and Al-Zahra academic teaching hospitals of Tabriz for labor in 2020-2021.

RESULTS

The study data showed no significant difference between mothers of infants with and without asphyxia in terms of demographics (p > 0.05). The prevalence of asphyxia was significantly higher only in mothers with the gravidity of 3 and 4 (p = 0.003). In terms of the methods for labor induction, the use of oxytocin was more common among mothers of infants with asphyxia (74.3%) than in those of infants without asphyxia (p = 0.015). The results also revealed a significant difference between infants with and without asphyxia in the Apgar score (first, fifth, and tenth minutes), need for neonatal resuscitation, umbilical cord artery Acidosis (pH, bicarbonate, and BE), and severity of HIE between two groups of infants with asphyxia and without asphyxia (p < 0.0001). The comparison of fetal CTG 0 to 20 min before the delivery indicated that normal variability was observed in 71.4% of infants born with asphyxia, whereas this figure for infants born without asphyxia was 91.4% (p = 0.031). However, the results showed no significant difference between the two groups of infants in any of the tstudied indicators at 20 and 40 min before the labor(p > 0.05). There was a significant difference between the two groups of infants in terms of deceleration at 40 and 60 min before the labor, as it was observed in 53.6% of infants born with asphyxia and only 11.1% of those born without asphyxia. The results also demonstrated a significant difference between the two groups in the type of deceleration (p = 0.025). Pearson and Spearman correlation coefficients showed a significant and direct relationship between interpretation the CTG of the three Perinatologists(p < 0.0001, r > 0.8).

CONCLUSION

The study results demonstrated a significant difference between infants born with asphyxia and those born without asphyxia in variability at 0 to 20 min before the labor and deceleration at 40 to 60 min before the labor.

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) &gt;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).

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.

A Novel, Cardiac-Derived Algorithm for Uterine Activity Monitoring in a Wearable Remote Device

Background: Uterine activity (UA) monitoring is an essential element of pregnancy management. The gold-standard intrauterine pressure catheter (IUPC) is invasive and requires ruptured membranes, while the standard-of-care, external tocodynamometry (TOCO)’s accuracy is hampered by obesity, maternal movements, and belt positioning. There is an urgent need to develop telehealth tools enabling patients to remotely access care. Here, we describe and demonstrate a novel algorithm enabling remote, non-invasive detection and monitoring of UA by analyzing the modulation of the maternal electrocardiographic and phonocardiographic signals. The algorithm was designed and implemented as part of a wireless, FDA-cleared device designed for remote pregnancy monitoring. Two separate prospective, comparative, open-label, multi-center studies were conducted to test this algorithm.

Methods: In the intrapartum study, 41 laboring women were simultaneously monitored with IUPC and the remote pregnancy monitoring device. Ten patients were also monitored with TOCO. In the antepartum study, 147 pregnant women were simultaneously monitored with TOCO and the remote pregnancy monitoring device.

Results: In the intrapartum study, the remote pregnancy monitoring device and TOCO had sensitivities of 89.8 and 38.5%, respectively, and false discovery rates (FDRs) of 8.6 and 1.9%, respectively. In the antepartum study, a direct comparison of the remote pregnancy monitoring device to TOCO yielded a sensitivity of 94% and FDR of 31.1%. This high FDR is likely related to the low sensitivity of TOCO.

Conclusion: UA monitoring via the new algorithm embedded in the remote pregnancy monitoring device is accurate and reliable and more precise than TOCO standard of care. Together with the previously reported remote fetal heart rate monitoring capabilities, this novel method for UA detection expands the remote pregnancy monitoring device’s capabilities to include surveillance, such as non-stress tests, greatly benefiting women and providers seeking telehealth solutions for pregnancy care.

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.

Uterine activity modifies the response of the fetal autonomic nervous system at preterm active labor

BACKGROUND

The autonomic nervous system of preterm fetuses has a different level of maturity than term fetuses. Thus, their autonomic response to transient hypoxemia caused by uterine contractions in labor may differ. This study aims to compare the behavior of the fetal autonomic response to uterine contractions between preterm and term active labor using a novel time-frequency analysis of fetal heart rate variability (FHRV).

METHODS

We performed a case-control study using fetal R-R and uterine activity time series obtained by abdominal electrical recordings from 18 women in active preterm labor (32-36 weeks of gestation) and 19 in active term labor (39-40 weeks of gestation). We analyzed 20 minutes of the fetal R-R time series by applying a Continuous Wavelet Transform (CWT) to obtain frequency (HF, 0.2-1 Hz; LF, 0.05-0.2 Hz) and time-frequency (Flux0, Flux90, and Flux45) domain features. Time domain FHRV features (SDNN, RMSSD, meanNN) were also calculated. In addition, ultra-short FHRV analysis was performed by segmenting the fetal R-R time series according to episodes of the uterine contraction and quiescent periods.

RESULTS

No significant differences between preterm and term labor were found for FHRV features when calculated over 20 minutes. However, we found significant differences when segmenting between uterine contraction and quiescent periods. In the preterm group, the LF, Flux0, and Flux45 were higher during the average contraction episode compared with the average quiescent period (p<0.01), while in term fetuses, vagally mediated FHRV features (HF and RMSSD) were higher during the average contraction episode (p<0.05). The meanNN was lower during the strongest contraction in preterm fetuses compared to their consecutive quiescent period (p=0.008).

CONCLUSION

The average autonomic response to contractions in preterm fetuses shows sympathetic predominance, while term fetuses respond through parasympathetic activity. Comparison between groups during the strongest contraction showed a diminished fetal autonomic response in the preterm group. Thus, separating contraction and quiescent periods during labor allows for identifying differences in the autonomic nervous system cardiac regulation between preterm and term fetuses.

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

Fetal Heart Rate Extraction Based on Wavelet Transform to Prevent Fetal Distress In Utero

In order to improve the effective extraction of fetal heart rate and prevent fetal distress in utero, a study of fetal heart rate feature extraction based on wavelet transform to prevent fetal distress in utero was proposed. This paper adopts a fetal heart rate detection method based on the maximum value of the binary wavelet transform modulus. The method is simulated by the Doppler fetal heart signal obtained from the clinic. Compared with the original curve, the transformed curve can roughly see the change rule of the original signal and identify the peak point of the signal, but due to the large disturbance of the peak point, the influence on the computer processing is also great. The periodicity of the transformed signal is greatly enhanced, making it easier to deal with the computation. A total of 300 pregnant women with full-term fetal heart monitoring from January 2018 to January 2020 were selected as the research subjects and divided into the observation group and the control group. The observation group consisted of 100 patients with abnormal fetal heart monitoring, and the control group consisted of 200 patients with normal fetal heart monitoring. The uterine contractions and fetal heart rate were recorded, and the incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid and fecal contamination were observed. The incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid fecal stain in the observation group were significantly higher than those in the control group. Fetal heart monitoring can accurately judge the situation of the fetus in pregnant women and timely diagnose the abnormal fetal heart rate, which has a better effect on the prognosis of perinatal infants and can reduce their mortality. It can effectively solve the problems existing in the autocorrelation algorithm and extract the fetal heart rate more accurately. It is an effective improved scheme of fetal heart rate extraction. It is very helpful in preventing fetal distress in utero.

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby’s overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby’s stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant’s HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant’s clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients’ health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

Feasibility of non-invasive Foetal electrocardiography in a twin pregnancy

Background

Twin pregnancy is associated with increased perinatal mortality. Close foetal monitoring is therefore warranted. Doppler Ultrasound cardiotocography is currently the only available method to monitor both individual foetuses. Unfortunately, the performance measures of this method are poor and erroneous monitoring of the same twin with both transducers may occur, leaving the second twin unmonitored. In this study we aimed to determine the feasibility of monitoring both foetuses simultaneously in twin gestation by means of non-invasive foetal electrocardiography (NI-fECG), using an electrode patch on the maternal abdomen.

Methods

A NI-fECG recording was performed at 25 + 3 weeks of gestation on a multiparous woman pregnant with dichorionic diamniotic twins. An electrode patch consisting of eight adhesive electrodes was applied on the maternal abdomen, yielding six channels of bipolar electrophysiological measurements. The output was digitized and stored for offline processing. The recorded signals were preprocessed by suppression of high-frequency noise, baseline wander, and powerline interference. Secondly, the maternal ECG was subtracted and segmentation into individual ECG complexes was performed. Finally, ensemble averaging of these individual ECG complexes was performed to suppress interferences.

Results

Six different recordings were obtained from each of the six recording channels. Depending on the orientation and distance of the fetal heart with respect to each electrode, a distinction could be made between each fetus based on the morphology of the signals. Yielding of the fetal ECGs was performed manually based on the QRS complexes of each fetus.

Conclusion

NI-fECG with multiple electrodes allows for monitoring of the fetal heart rate and ECG of both individual fetuses in twin pregnancies.

Doppler Ultrasound Technology for Fetal Heart Rate Monitoring: A Review

Fetal well-being is commonly assessed by monitoring the fetal heart rate (fHR). In clinical practice, the de facto standard technology for fHR monitoring is based on the Doppler ultrasound (US). Continuous monitoring of the fHR before and during labor is performed using a US transducer fixed on the maternal abdomen. The continuous fHR monitoring, together with simultaneous monitoring of the uterine activity, is referred to as cardiotocography (CTG). In contrast, for intermittent measurements of the fHR, a handheld Doppler US transducer is typically used. In this article, the technology of Doppler US for continuous fHR monitoring and intermittent fHR measurements is described, with emphasis on fHR monitoring for CTG. Special attention is dedicated to the measurement environment, which includes the clinical setting in which fHR monitoring is commonly performed. In addition, to understand the signal content of acquired Doppler US signals, the anatomy and physiology of the fetal heart and the surrounding maternal abdomen are described. The challenges encountered in these measurements have led to different technological strategies, which are presented and critically discussed, with a focus on the US transducer geometry, Doppler signal processing, and fHR extraction methods.

Autonomic response to fetal acidosis using an experimental sheep model

BACKGROUND

The autonomic nervous system has a major role in fetal adaptation to hypoxia. Its activity might be assessed using heart rate variability and heart rate deceleration analyses.

OBJECTIVE

To evaluate the ability of different heart rate variability and morphological deceleration analyses to predict fetal acidosis during labor in an experimental fetal sheep model.

STUDY DESIGN

Repeated 1-minute total umbilical cord occlusions were performed at mild (1minute every 5 min), moderate (1 min every 3 min), and severe (1 min every 2 min) umbilical cord occlusion periodicities until arterial pH reached 7.10. Hemodynamic,blood gas analysis, morphological analysis of decelerations (magnitude, slope, and area ofdecelerations), and heart rate variability parameters were recorded throughout the experiment.Heart rate variability analysis included temporal analysis (root mean square of successivedifferences between adjacent RR intervals, standard deviation of normal to normal RR intervals, short term variability), spectral analysis (low frequencies, high frequencies,normalized high frequencies), and a new index developed by our team, the Fetal Stress Index.We defined and compared three pH groups: >7.20, 7.10-7.20, and <7.10.

RESULTS

Eleven experiments were performed. Repetitive umbilical cord occlusions resulted in progressive fetal acidosis. Fetal Stress Index was correlated with pH and lactate (p < 0.05) and increased with acidosis. There were no significant correlations between pH, lactate, and other indices (spectral analysis, temporal analysis, or morphological analysis of decelerations).

CONCLUSION

This protocol allowed us to identify the progressive onset of fetal acidosis in an experimental model close to labor. Fetal Stress Index is a heart rate variability method that varies with acidosis and indicates an increase in parasympathetic nervous system activity in response to fetal acidosis.

Fetal Heart Rate Monitoring Implemented by Dynamic Adaptation of Transmission Power of a Flexible Ultrasound Transducer Array

Fetal heart rate (fHR) monitoring using Doppler Ultrasound (US) is a standard method to assess fetal health before and during labor. Typically, an US transducer is positioned on the maternal abdomen and directed towards the fetal heart. Due to fetal movement or displacement of the transducer, the relative fetal heart location (fHL) with respect to the US transducer can change, leading to frequent periods of signal loss. Consequently, frequent repositioning of the US transducer is required, which is a cumbersome task affecting clinical workflow. In this research, a new flexible US transducer array is proposed which allows for measuring the fHR independently of the fHL. In addition, a method for dynamic adaptation of the transmission power of this array is introduced with the aim of reducing the total acoustic dose transmitted to the fetus and the associated power consumption, which is an important requirement for application in an ambulatory setting. The method is evaluated using an in-vitro setup of a beating chicken heart. We demonstrate that the signal quality of the Doppler signal acquired with the proposed method is comparable to that of a standard, clinical US transducer. At the same time, our transducer array is able to measure the fHR for varying fHL while only using 50% of the total transmission power of standard, clinical US transducers.

Detection rate of fetal distress using contraction-dependent fetal heart rate variability analysis

OBJECTIVE

Monitoring of the fetal condition during labor is currently performed by cardiotocograpy (CTG). Despite the use of CTG in clinical practice, CTG interpretation suffers from a high inter- and intra-observer variability and a low specificity. In addition to CTG, analysis of fetal heart rate variability (HRV) has been shown to provide information on fetal distress. However, fetal HRV can be strongly influenced by uterine contractions, particularly during the second stage of labor. Therefore, the aim of this study is to examine if distinguishing contractions from rest periods can improve the detection rate of HRV features for fetal distress during the second stage of labor.

APPROACH

We used a dataset of 100 recordings, containing 20 cases of fetuses with adverse outcome. The most informative HRV features were selected by a genetic algorithm and classification performance was evaluated using support vector machines.

MAIN RESULTS

Classification performance of fetal heart rate segments closest to birth improved from a geometric mean of 70% to 79%. If the classifier was used to indicate fetal distress over time, the geometric mean at 15 minutes before birth improved from 60% to 72%.

SIGNIFICANCE

Our results show that combining contraction-dependent HRV features with HRV features calculated over the entire fetal heart rate signal improves the detection rate of fetal distress.

Improved ultrasound transducer positioning by fetal heart location estimation during Doppler based heart rate measurements

OBJECTIVE

Doppler ultrasound (US) is the most commonly applied method to measure the fetal heart rate (fHR). When the fetal heart is not properly located within the ultrasonic beam, fHR measurements often fail. As a consequence, clinical staff need to reposition the US transducer on the maternal abdomen, which can be a time consuming and tedious task.

APPROACH

In this article, a method is presented to aid clinicians with the positioning of the US transducer to produce robust fHR measurements. A maximum likelihood estimation (MLE) algorithm is developed, which provides information on fetal heart location using the power of the Doppler signals received in the individual elements of a standard US transducer for fHR recordings. The performance of the algorithm is evaluated with simulations and in vitro experiments performed on a beating-heart setup.

MAIN RESULTS

Both the experiments and the simulations show that the heart location can be accurately determined with an error of less than 7 mm within the measurement volume of the employed US transducer.

SIGNIFICANCE

The results show that the developed algorithm can be used to provide accurate feedback on fetal heart location for improved positioning of the US transducer, which may lead to improved measurements of the fHR.

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.

Selective heart rate variability analysis to account for uterine activity during labor and improve classification of fetal distress

Cardiotocography (CTG) is currently the most often used technique for detection of fetal distress. Unfortunately, CTG has a poor specificity. Recent studies suggest that, in addition to CTG, information on fetal distress can be obtained from analysis of fetal heart rate variability (HRV). However, uterine contractions can strongly influence fetal HRV. The aim of this study is therefore to investigate whether HRV analysis for detection of fetal distress can be improved by distinguishing contractions from rest periods. Our results from feature selection indicate that HRV features calculated separately during contractions or during rest periods are more informative on fetal distress than HRV features that are calculated over the entire fetal heart rate. Furthermore, classification performance improved from a geometric mean of 69.0% to 79.6% when including the contraction-dependent HRV features, in addition to HRV features calculated over the entire fetal heart rate.

Simulation of fetal heart rate variability with a mathematical model

In the clinic, the cardiotocogram (CTG), the combined registration of fetal heart rate (FHR) and uterine contractions, is used to predict fetal well-being. Amongst others, fetal heart rate variability (FHRV) is an important indicator of fetal distress. In this study we add FHRV to our previously developed CTG simulation model, in order to improve its use as a research and educational tool. We implemented three sources of variability by applying either 1/f or white noise to the peripheral vascular resistance, baroreceptor output, or efferent vagal signal. Simulated FHR tracings were evaluated by visual inspection and spectral analysis. All power spectra showed a 1/f character, irrespective of noise type and source. The clinically observed peak near 0.1 Hz was only obtained by applying white noise to the different sources of variability. Similar power spectra were found when peripheral vascular resistance or baroreceptor output was used as source of variability. Sympathetic control predominantly influenced the low frequency power, while vagal control influenced both low and high frequency power. In contrast to clinical data, model results did not show an increase of FHRV during FHR decelerations. Still, addition of FHRV improves the applicability of the model as an educational and research tool.