Comparison of short term variability indexes in cardiotocographic foetal monitoring

Early heart rate variability changes during acute fetal inflammatory response syndrome: An experimental study in a fetal sheep model

INTRODUCTION

Fetal infection during labor with fetal inflammatory response syndrome (FIRS) is associated with neurodevelopmental disabilities, cerebral palsy, neonatal sepsis, and mortality. Current methods to diagnose FIRS are inadequate. Thus, the study aim was to explore whether fetal heart rate variability (HRV) analysis can be used to detect FIRS.

MATERIAL AND METHODS

In chronically instrumented near-term fetal sheep, lipopolysaccharide (LPS) was injected intravenously to model FIRS. A control group received saline solution injection. Hemodynamic, blood gas analysis, interleukin-6 (IL-6), and 14 HRV indices were recorded for 6 h. In both groups, comparisons were made between the stability phase and the 6 h following injection (H1-H6, respectively) and between LPS and control groups.

RESULTS

Fifteen lambs were instrumented. In the LPS group (n = 8), IL-6 increased significantly after LPS injection (p < 0.001), confirming the FIRS model. Fetal heart rate increased significantly after H5 (p < 0.01). In our FIRS model without shock or cardiovascular decompensation, five HRV measures changed significantly after H2 until H4 in comparison to baseline. Moreover, significant differences between LPS and control groups were observed in HRV measures between H2 and H4. These changes appear to be mediated by an increase of global variability and a loss of signal complexity.

CONCLUSION

As significant HRV changes were detected before FHR increase, these indices may be valuable for early detection of acute FIRS.

Detection of Suspicious Cardiotocographic Recordings by Means of a Machine Learning Classifier

Cardiotocography (CTG) is one of the fundamental prenatal diagnostic methods for both antepartum and intrapartum fetal surveillance. Although it has allowed a significant reduction in intrapartum and neonatal mortality and morbidity, its diagnostic accuracy is, however, still far from being fully satisfactory. In particular, the identification of uncertain and suspicious CTG traces remains a challenging task for gynecologists. The introduction of computerized analysis systems has enabled more objective evaluations, possibly leading to more accurate diagnoses. In this work, the problem of classifying suspicious CTG recordings was addressed through a machine learning approach. A machine-based labeling was proposed, and a binary classification was carried out using a support vector machine (SVM) classifier to distinguish between suspicious and normal CTG traces. The best classification metrics showed accuracy, sensitivity, and specificity values of 92%, 92%, and 90%, respectively. The main results were compared both with results obtained by considering a more unbalanced dataset and with relevant literature studies in the field. The use of the SVM proved to be promising in the field of CTG classification. However, appropriate feature selection and dataset balancing are crucial to achieve satisfactory performance of the classifier.

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers.

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

Effects of acute tocolysis using ritodrine hydrochloride on foetal heart rate patterns in intrauterine foetal resuscitation: a retrospective, single-centre observational study

No consistent recommendations concerning the preferred tocolytic agents for intrauterine foetal resuscitation are available. We evaluated the effects of acute tocolysis (AT) using ritodrine hydrochloride on foetal heart rate (FHR) patterns and neonatal outcomes. We retrospectively analysed the data of patients undergoing emergency caesarean section because of non-reassuring foetal status indicated by foetal scalp electrodes. Patients were classified into AT (ritodrine hydrochloride approximately 500 µg/min) and control groups with 15 and 12 participants, respectively. FHR patterns, Apgar scores, umbilical arterial analysis, and neonatal admission were compared. All participants had FHR category II; decelerations disappeared in all foetuses in the AT group, with no significant difference in neonatal outcomes. The AT group had a higher baseline FHR and lower short-term FHR variability than the control group, indicating foetal autonomic responses. Further studies are needed to clarify the effects of AT on FHR patterns, neonatal outcomes, and foetal and neonatal autonomic responses.Impact statementWhat is already known on this subject? The usefulness of acute tocolysis using ritodrine hydrochloride has been well-documented in several studies; however, such an application often induces side effects, such as maternal tachycardia, palpitations, and tremors.What the results of this study add? The short-term administration of ritodrine hydrochloride eliminated decelerations, with no significant difference in neonatal outcomes in pregnant women with foetal heart rate category II. Meanwhile, there were higher foetal heart rate and lower short-term foetal heart rate variability in pregnant women administered with ritodrine hydrochloride, indicating foetal autonomic responses.What the implications are of these findings for clinical practice and/or further research? Ritodrine hydrochloride administration, even for short-term, appears to be associated with foetal autonomic responses. Further studies with stratification of patient groups based on the severity and aetiology of non-reassuring foetal status, including pregnant women with foetal category III, would elucidate the risk and benefit of acute tocolysis using ritodrine hydrochloride, based on foetal heart rate patterns, neonatal outcomes, and foetal and neonatal autonomic responses.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Classifying the type of delivery from cardiotocographic signals: A machine learning approach

BACKGROUND AND OBJECTIVE

Cardiotocography (CTG) is the most employed methodology to monitor the foetus in the prenatal phase. Since the evaluation of CTG is often visual, and hence qualitative and too subjective, some automated methods have been introduced for its assessment.

METHODS

In this paper, a custom-made software is exploited to extract 17 features from the available CTG. A preliminary univariate statistical analysis is performed; then, five machine learning algorithms, exploiting ensemble learning, were implemented (J48, Random Forests (RF), Ada-boosting of decision tree (ADA-B), Gradient Boosting and Decorate) through Knime analytics platform to classify patients according to their delivery: vaginal or caesarean section. The dataset is composed by 370 signals collected between 2000 and 2009 in both public and private hospitals. The performance of the algorithms was evaluated using 10 folds cross validation with different evaluation metrics: accuracy, precision, sensitivity, specificity, area under the curve receiver operating characteristic (AUCROC).

RESULTS

While only two features were significantly different (gestation week and power expressed by the high frequency band of FHR power spectrum), from the statistical point of view, machine learning results were great. The RF obtained the best results: accuracy (91.1%), sensitivity (90.0%) and AUCROC (96.7%). The ADA-B achieved the highest precision (92.6%) and specificity (93.1%). As expected, the lowest scores were obtained by J48 that was the base classifier employed in all the others empowered implementations. Excluding the J48 results, the AUCROC of all the algorithms was greater than 94.9%.

CONCLUSION

In the light of the obtained results, that are greater than those ones found in the literature from comparable researches, it can be stated that the machine learning approach can actually help the physicians in their decision process when evaluating the foetal well-being.

New Method for Beat-to-Beat Fetal Heart Rate Measurement Using Doppler Ultrasound Signal

The most commonly used method of fetal monitoring is based on heart activity analysis. Computer-aided fetal monitoring system enables extraction of clinically important information hidden for visual interpretation—the instantaneous fetal heart rate (FHR) variability. Today’s fetal monitors are based on monitoring of mechanical activity of the fetal heart by means of Doppler ultrasound technique. The FHR is determined using autocorrelation methods, and thus it has a form of evenly spaced—every 250 ms—instantaneous measurements, where some of which are incorrect or duplicate. The parameters describing a beat-to-beat FHR variability calculated from such a signal show significant errors. The aim of our research was to develop new analysis methods that will both improve an accuracy of the FHR determination and provide FHR representation as time series of events. The study was carried out on simultaneously recorded (during labor) Doppler ultrasound signal and the reference direct fetal electrocardiogram Two subranges of Doppler bandwidths were separated to describe heart wall movements and valve motions. After reduction of signal complexity by determining the Doppler ultrasound envelope, the signal was analyzed to determine the FHR. The autocorrelation method supported by a trapezoidal prediction function was used. In the final stage, two different methods were developed to provide signal representation as time series of events: the first using correction of duplicate measurements and the second based on segmentation of instantaneous periodicity measurements. Thus, it ensured the mean heart interval measurement error of only 1.35 ms. In a case of beat-to-beat variability assessment the errors ranged from −1.9% to −10.1%. Comparing the obtained values to other published results clearly confirms that the new methods provides a higher accuracy of an interval measurement and a better reliability of the FHR variability estimation.

Fetal heart rate variability analysis for neonatal acidosis prediction

Fetal well-being during labor is usually assessed by visual analysis of a fetal heart rate (FHR) tracing. Our primary objective was to evaluate the ability of automated heart rate variability (HRV) analysis methods, including our new fetal stress index (FSI), to predict neonatal acidosis. 552 intrapartum recordings were analyzed. The analysis occurred in the last 90 min before birth and was conducted during two 5-min intervals: (i) a stable period of FHR and (ii) the period corresponding to the maximum FSI value. For each period, we computed the mean FHR, FSI, short-term variability (STV), and long-term variability (LTV). Visual FHR interpretation was performed using the FIGO classification. The population was separated into two groups: (i) an acidotic group with an arterial pH at birth ≤ 7.10 and a control group. Prediction of a neonatal pH ≤ 7.10 was assessed by computing the receiver-operating characteristic area under the curve (AUC). FHR, FSI, STV, and LTV did not differ significantly between groups during the stable period. During the FSI max peak period, LTV and STV correlated significantly in the acidotic group (- 5.85 ± 2.19, p = 0.010 and - 0.62 ± 0.29, p = 0.037, respectively). The AUC values were 0.569 for FIGO classification, 0.595 for STV, and 0.622 for LTV. The multivariate model (FIGO, FSI, FC, STV, LTV) had the greatest accuracy for predicting acidosis (AUC = 0.719). FSI was not predictive of neonatal acidosis probably because of the low quality of the FHR signal in cardiotocography. When used separately, HRV indexes and visual FHR analysis were poor predictors of neonatal acidosis. Including all indexes in a multivariate model increased the predictive ability.

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

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

The fetal electrocardiogram to detect the effects of betamethasone on fetal heart rate variability

BACKGROUND

Betamethasone is widely used to enhance fetal lung maturation in case of threatened preterm birth. Antenatal corticosteroids are known to reduce fetal heart rate variability (fHRV) in the days following administration. Since decreased fHRV is a marker for fetal distress, this transient decrease of fHRV can cause unnecessary medical intervention.

AIM

To describe the effect of betamethasone on fHRV, by applying spectral analysis on non-invasive fetal electrocardiogram (fECG) recordings.

STUDY DESIGN

Secondary analysis of a prospective cohort study.

SUBJECTS

Women with a singleton pregnancy, at risk for preterm delivery and receiving betamethasone, admitted to the obstetric high care unit in the period from March 2013 until July 2016.

OUTCOME MEASURES

The primary outcome measure was fHRV in both time- and frequency-domain. Secondary outcome measures included basal fetal heart rate (fHR) and fHR variance. FHRV parameters were then calculated separately for the quiet and active state.

RESULTS

Following 68 inclusions, 22 patients remained with complete series of measurements and sufficient data quality. FHRV parameters and fHR showed a decrease on day 2 compared to day 1, significant for short-term variability and high-frequency power. Similar results were found when analyzing for separate behavioral states. The number of segments in quiet state increased during days 1 and 2. Normalized values showed no difference for all behavioral states.

CONCLUSION

FHRV decreases on day 2 after betamethasone administration, while periods of fetal quiescence increase. No changes were found in the normalized values, indicating that the influence of autonomic modulation is minor. Clinical trial registration number NL43294.015.13.

An open source autocorrelation-based method for fetal heart rate estimation from one-dimensional Doppler ultrasound

OBJECTIVE

Open research on fetal heart rate (FHR) estimation is relatively rare, and evidence for the utility of metrics derived from Doppler ultrasound devices has historically remained hidden in the proprietary documentation of commercial entities, thereby inhibiting its assessment and improvement. Nevertheless, recent studies have attempted to improve FHR estimation; however, these methods were developed and tested using datasets composed of few subjects and are therefore unlikely to be generalizable on a population level. The work presented here introduces a reproducible and generalizable autocorrelation (AC)-based method for FHR estimation from one-dimensional Doppler ultrasound (1D-DUS) signals.

APPROACH

Simultaneous fetal electrocardiogram (fECG) and 1D-DUS signals generated by a hand-held Doppler transducer in a fixed position were captured by trained healthcare workers in a European hospital. The fECG QRS complexes were identified using a previously published fECG extraction algorithm and were then over-read to ensure accuracy. An AC-based method to estimate FHR was then developed on this data, using a total of 721 1D-DUS segments, each 3.75 s long, and parameters were tuned with Bayesian optimization. The trained FHR estimator was tested on two additional (independent) hand-annotated Doppler-only datasets recorded with the same device but on different populations: one composed of 3938 segments (from 99 fetuses) acquired in rural Guatemala, and another composed of 894 segments (from 17 fetuses) recorded in a hospital in the UK.

MAIN RESULTS

The proposed AC-based method was able to estimate FHR within 10% of the reference FHR values 96% of the time, with an accuracy of 97% for manually identified good quality segments in both of the independent test sets.

SIGNIFICANCE

This is the first work to publish open source code for FHR estimation from 1D-DUS data. The method was shown to satisfy estimations within 10% of the reference FHR values and it therefore defines a minimum accuracy for the field to match or surpass. Our work establishes a basis from which future methods can be developed to more accurately estimate FHR variability for assessing fetal wellbeing from 1D-DUS signals.

The prognostic accuracy of short term variation of fetal heart rate in early-onset fetal growth restriction: A systematic review

OBJECTIVE

Cardiotocography (CTG) is an important tool for fetal surveillance in severe early-onset fetal growth restriction (FGR). Assessment of the CTG is usually performed visually (vCTG). However, it is suggested that computerized analysis of the CTG (cCTG) including short term variability (STV) could more accurately detect fetal compromise. The objective of this study was to systematically review the literature on the association between cCTG and perinatal outcome and the comparison of cCTG with vCTG.

STUDY DESIGN

A systematic search was performed in MEDLINE, EMBASE and Google Scholar. Studies were included that assessed prognostic accuracy of STV or compared STV to vCTG in patients with FGR. Risk of bias and concerns about applicability were assessed with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) instrument.

RESULTS

Of the 885 records identified in the search, five cohort studies (387 patients) were included. We found no randomized studies comparing STV with visual CTG in patients with FGR. The risk of bias of all studies was generally judged as 'low'. One small study found an association of low STV with neonatal acidosis. One study observed no association of STV with long-term outcome. Composite analysis of all five studies showed a non-significant relative risk for acidosis after a low STV of 1.4 (95% CI 0.6-3.2, N = 387). Further meta-analysis was hampered due to heterogeneity in outcome reporting and use of different thresholds.

CONCLUSION

The evidence from the included studies did not support an association of STV and short or long term outcome. However, available data are limited and heterogeneous, and influenced by management based on STV. Solid evidence from a randomized controlled trial comparing STV with vCTG including long term infant outcome is needed before STV can be used clinically for timing of delivery in patients with FGR.

Use and experiences with external fetal monitoring devices among obstetrical providers

Introduction: Fetal heart rate monitoring presents one of the few available methods for evaluating the fetus prior to birth. However, current devices on the market have significant shortcomings. We sought to describe the use and experiences with external fetal monitoring (EFM) devices among obstetrical providers.Materials and methods: We performed a cross-sectional survey in an academic medical center between April and July 2017 including nurse, midwife, and physician obstetrical providers (n = 217) who were invited to participate in this study regarding their experiences with the external fetal monitoring (EFM) device utilized by their hospital system in the outpatient, inpatient, and labor and delivery (L&D) settings. Associations between provider characteristics, device use, perception of challenging patients, and potential usefulness of an improved system were assessed by Fisher's exact test.Results: The 137 respondents (63.1%) reported difficulties monitoring obese women (98.5%), multiple gestation pregnancies (90.5%), and early gestational ages (71.5%). Over half (59.5%) of L&D nurses reported interacting with EFM devices for greater than 1-hour during a typical 12-hour shift and fewer than half (42.3%) reported being satisfied with current EFM devices. There were no statistically significant associations between provider age, experience, or time spent utilizing the devices with perception of challenging patient types.Conclusions: In conclusion, obstetrical providers perceive shortcomings of current EFM devices across all levels of provider experience and time utilizing these devices. Nurses reported significant time operating the devices, representing an opportunity to reduce time and costs with an improved device.

A Hybrid EMD-Kurtosis Method for Estimating Fetal Heart Rate from Continuous Doppler Signals

Monitoring of fetal heart rate (FHR) is an important measure of fetal wellbeing during the months of pregnancy. Previous works on estimating FHR variability from Doppler ultrasound (DUS) signal mainly through autocorrelation analysis showed low accuracy when compared with heart rate variability (HRV) computed from fetal electrocardiography (fECG). In this work, we proposed a method based on empirical mode decomposition (EMD) and the kurtosis statistics to estimate FHR and its variability from DUS. Comparison between estimated beat-to-beat intervals using the proposed method and the autocorrelation function (AF) with respect to RR intervals computed from fECG as the ground truth was done on DUS signals from 44 pregnant mothers in the early (20 cases) and late (24 cases) gestational weeks. The new EMD-kurtosis method showed significant lower error in estimating the number of beats in the early group (EMD-kurtosis: 2.2% vs. AF: 8.5%, p < 0.01, root mean squared error) and the late group (EMD-kurtosis: 2.9% vs. AF: 6.2%). The EMD-kurtosis method was also found to be better in estimating mean beat-to-beat with an average difference of 1.6 ms from true mean RR compared to 19.3 ms by using the AF method. However, the EMD-kurtosis performed worse than AF in estimating SNDD and RMSSD. The proposed EMD-kurtosis method is more robust than AF in low signal-to-noise ratio cases and can be used in a hybrid system to estimate beat-to-beat intervals from DUS. Further analysis to reduce the estimated beat-to-beat variability from the EMD-kurtosis method is needed.

Is Abdominal Fetal Electrocardiography an Alternative to Doppler Ultrasound for FHR Variability Evaluation?

Great expectations are connected with application of indirect fetal electrocardiography (FECG), especially for home telemonitoring of pregnancy. Evaluation of fetal heart rate (FHR) variability, when determined from FECG, uses the same criteria as for FHR signal acquired classically—through ultrasound Doppler method (US). Therefore, the equivalence of those two methods has to be confirmed, both in terms of recognizing classical FHR patterns: baseline, accelerations/decelerations (A/D), long-term variability (LTV), as well as evaluating the FHR variability with beat-to-beat accuracy—short-term variability (STV). The research material consisted of recordings collected from 60 patients in physiological and complicated pregnancy. The FHR signals of at least 30 min duration were acquired dually, using two systems for fetal and maternal monitoring, based on US and FECG methods. Recordings were retrospectively divided into normal (41) and abnormal (19) fetal outcome. The complex process of data synchronization and validation was performed. Obtained low level of the signal loss (4.5% for US and 1.8% for FECG method) enabled to perform both direct comparison of FHR signals, as well as indirect one—by using clinically relevant parameters. Direct comparison showed that there is no measurement bias between the acquisition methods, whereas the mean absolute difference, important for both visual and computer-aided signal analysis, was equal to 1.2 bpm. Such low differences do not affect the visual assessment of the FHR signal. However, in the indirect comparison the inconsistencies of several percent were noted. This mainly affects the acceleration (7.8%) and particularly deceleration (54%) patterns. In the signals acquired using the electrocardiography the obtained STV and LTV indices have shown significant overestimation by 10 and 50% respectively. It also turned out, that ability of clinical parameters to distinguish between normal and abnormal groups do not depend on the acquisition method. The obtained results prove that the abdominal FECG, considered as an alternative to the ultrasound approach, does not change the interpretation of the FHR signal, which was confirmed during both visual assessment and automated analysis.

Frequency and Time Domain Analysis of Foetal Heart Rate Variability with Traditional Indexes: A Critical Survey

Monitoring of foetal heart rate and its variability (FHRV) covers an important role in assessing health of foetus. Many analysis methods have been used to get quantitative measures of FHRV. FHRV has been studied in time and in frequency domain and interesting clinical results have been obtained. Nevertheless, a standardized definition of FHRV and a precise methodology to be used for its evaluation are lacking. We carried out a literature overview about both frequency domain analysis (FDA) and time domain analysis (TDA). Then, by using simulated FHR signals, we defined the methodology for FDA. Further, employing more than 400 real FHR signals, we analysed some of the most common indexes, Short Term Variability for TDA and power content of the spectrum bands and sympathovagal balance for FDA, and evaluated their ranges of values, which in many cases are a novelty. Finally, we verified the relationship between these indexes and two important parameters: week of gestation, indicator of foetal growth, and foetal state, classified as active or at rest. Our results indicate that, according to literature, it is necessary to standardize the procedure for FHRV evaluation and to consider week of gestation and foetal state before FHR analysis.

Software for computerised analysis of cardiotocographic traces

Despite the widespread use of cardiotocography in foetal monitoring, the evaluation of foetal status suffers from a considerable inter and intra-observer variability. In order to overcome the main limitations of visual cardiotocographic assessment, computerised methods to analyse cardiotocographic recordings have been recently developed. In this study, a new software for automated analysis of foetal heart rate is presented. It allows an automatic procedure for measuring the most relevant parameters derivable from cardiotocographic traces. Simulated and real cardiotocographic traces were analysed to test software reliability. In artificial traces, we simulated a set number of events (accelerations, decelerations and contractions) to be recognised. In the case of real signals, instead, results of the computerised analysis were compared with the visual assessment performed by 18 expert clinicians and three performance indexes were computed to gain information about performances of the proposed software. The software showed preliminary performance we judged satisfactory in that the results matched completely the requirements, as proved by tests on artificial signals in which all simulated events were detected from the software. Performance indexes computed in comparison with obstetricians' evaluations are, on the contrary, not so satisfactory; in fact they led to obtain the following values of the statistical parameters: sensitivity equal to 93%, positive predictive value equal to 82% and accuracy equal to 77%. Very probably this arises from the high variability of trace annotation carried out by clinicians.

Fetal heart rate feature extraction from cardiotocographic recordings through autoregressive model's power spectral- and pole-based analysis

The main objective of this work is to perform an autoregressive model (AR)-based power spectral analysis of the fetal heart rate (FHR) signal for the extraction of significant features for fetal welfare assessment. A group of features is directly computed from the AR-based spectrum while another group is computed from the poles representation. The presented method is applied to real cardiotocographic (CTG) signals and for different frequency bands, and the obtained results are very promising as they exhibit direct correlations between the extracted features and the fetal welfare in terms of umbilical pH.

A novel technique for fetal heart rate estimation from Doppler ultrasound signal

Background

The currently used fetal monitoring instrumentation that is based on Doppler ultrasound technique provides the fetal heart rate (FHR) signal with limited accuracy. It is particularly noticeable as significant decrease of clinically important feature - the variability of FHR signal. The aim of our work was to develop a novel efficient technique for processing of the ultrasound signal, which could estimate the cardiac cycle duration with accuracy comparable to a direct electrocardiography.

Methods

We have proposed a new technique which provides the true beat-to-beat values of the FHR signal through multiple measurement of a given cardiac cycle in the ultrasound signal. The method consists in three steps: the dynamic adjustment of autocorrelation window, the adaptive autocorrelation peak detection and determination of beat-to-beat intervals. The estimated fetal heart rate values and calculated indices describing variability of FHR, were compared to the reference data obtained from the direct fetal electrocardiogram, as well as to another method for FHR estimation.

Results

The results revealed that our method increases the accuracy in comparison to currently used fetal monitoring instrumentation, and thus enables to calculate reliable parameters describing the variability of FHR. Relating these results to the other method for FHR estimation we showed that in our approach a much lower number of measured cardiac cycles was rejected as being invalid.

Conclusions

The proposed method for fetal heart rate determination on a beat-to-beat basis offers a high accuracy of the heart interval measurement enabling reliable quantitative assessment of the FHR variability, at the same time reducing the number of invalid cardiac cycle measurements.

Computerized fetal heart rate analysis in labor: detection of intervals with un-assignable baseline

The fetal heart rate (FHR) is monitored during labor to assess fetal health. Both visual and computerized interpretations of the FHR depend on assigning a baseline to detect key features such as accelerations or decelerations. However, it is sometimes impossible to assign a baseline reliably, by eye or by numerical methods. To address this issue, we used the Oxford Intrapartum FHR Database to derive an algorithm based on the distribution of the FHR that detects heart rate intervals without a clear baseline. We aimed to recognize when a fetus cannot maintain its heart rate baseline and use this to assist computerized FHR analysis. Twenty-three FHR windows (15 min long) were used to develop the method. The algorithm was then validated by comparison with experts who classified 50 FHR windows into two groups: baseline assignable or un-assignable. The average agreement between experts (κ = 0.76) was comparable to the agreement between method and experts (κ = 0.67). The algorithm was used in 22 559 patients with intrapartum FHR records to retrospectively determine the incidence of intervals (defined as 15 min windows) that had un-assignable baselines. Sixty-six percent had one or more such episodes at some stage, most commonly after the onset of pushing (55%) and least commonly pre-labor (16%). These episodes are therefore relatively common. Their detection should improve the reliability of computerized analysis and allow further studies of what they signify clinically.