Developments in CTG analysis

Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring

Asphyxiation associated with metabolic acidosis is one of the common causes of fetal deaths. The paper aims to develop a feature extraction and prediction algorithm capable of identifying most of the features in the SISPORTO software package and late and variable decelerations. The resulting features were used for classification based on umbilical cord pH data. The algorithms developed here were used to predict cord pH levels. The prediction system assists the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology, which uses fetal heart rate and uterine activity, to identify acidosis. This paper introduces a forecasting model based on deep learning to predict heart rate and uterine contractions, integrated with the classification algorithm, resulting in a robust tool for predictive fetal monitoring. The hybrid algorithm resulted in a model capable of providing future conditions of the fetus, which obstetricians can use for diagnosis and planning interventions. The ensemble classification algorithm had a test accuracy of 85% (n = 24) in predicting fetal acidosis on the features extracted from the cardiotocography data. When integrated with the classification model, the results from the prediction model (long short-term memory network) can effectively identify fetal acidosis 2 or 4 min in the future.

Intrapartum cardiotocography in pregnancies with and without fetal CHD

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.

A deep learning mixed-data type approach for the classification of FHR signals

The Cardiotocography (CTG) is a widely diffused monitoring practice, used in Ob-Gyn Clinic to assess the fetal well-being through the analysis of the Fetal Heart Rate (FHR) and the Uterine contraction signals. Due to the complex dynamics regulating the Fetal Heart Rate, a reliable visual interpretation of the signal is almost impossible and results in significant subjective inter and intra-observer variability. Also, the introduction of few parameters obtained from computer analysis did not solve the problem of a robust antenatal diagnosis. Hence, during the last decade, computer aided diagnosis systems, based on artificial intelligence (AI) machine learning techniques have been developed to assist medical decisions. The present work proposes a hybrid approach based on a neural architecture that receives heterogeneous data in input (a set of quantitative parameters and images) for classifying healthy and pathological fetuses. The quantitative regressors, which are known to represent different aspects of the correct development of the fetus, and thus are related to the fetal healthy status, are combined with features implicitly extracted from various representations of the FHR signal (images), in order to improve the classification performance. This is achieved by setting a neural model with two connected branches, consisting respectively of a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN). The neural architecture was trained on a huge and balanced set of clinical data (14.000 CTG tracings, 7000 healthy and 7000 pathological) recorded during ambulatory non stress tests at the University Hospital Federico II, Napoli, Italy. After hyperparameters tuning and training, the neural network proposed has reached an overall accuracy of 80.1%, which is a promising result, as it has been obtained on a huge dataset.

An integrated approach based on advanced CTG parameters and Doppler measurements for late growth restriction management

Background

The clinical diagnosis of late Fetal Growth Restriction (FGR) involves the integration of Doppler ultrasound data and Fetal Heart Rate (FHR) monitoring through computer assisted computerized cardiotocography (cCTG). The aim of the study was to evaluate the diagnostic power of combined Doppler and cCTG parameters by contrasting late FGR –and healthy controls.

Methods

The study was conducted from January 2018 to May 2020. Only pregnant women who had the last Doppler measurement obtained within 1 week before delivery and cCTG performed within 24 h before delivery were included in the study. Two hundred forty-nine pregnant women fulfilling the inclusion criteria were enrolled in the study; 95 were confirmed as late FGR and 154 were included in the control group.

Results

Among the extracted cCTG parameters, Delta Index, Short Term Variability (STV), Long Term Variability (LTV), Acceleration and Deceleration Phase Rectified Slope (APRS, DPRS) values were lower in the late FGR participants compared to the control group. In the FGR cohort, Delta, STV, APRS, and DPRS were found different when stratifying by MCA_PI (MCA_PI <5th centile or > 5th centile). STV and DPRS were the only parameters to be found different when stratifying by (UA_PI >95th centile or UA_PI <95th centile). Additionally, we measured the predictive power of cCTG parameters toward the identification of associated Doppler measures using figures of merit extracted from ROC curves.

The AUC of ROC curves were accurate for STV (0,70), Delta (0,68), APRS (0,65) and DPRS (0,71) when UA_PI values were > 95th centile while, the accuracy attributable to the prediction of MCA_PI was 0.76, 0.77, 0.73, and 0.76 for STV, Delta, APRS, and DPRS, respectively.

An association of UA_PI>95th centile and MCA_PI<5th centile with higher risk for NICU admission, was observed, while CPR < 5th centile resulted not associated with any perinatal outcome. Values of STV, Delta, APRS, DPRS were significantly lower for FGR neonates admitted to NICU, compared with the uncomplicated FGR cohort.

Conclusions

The results of this study show the contribution of advanced cCTG parameters and fetal Doppler to the identification of late FGR and the association of those parameters with the risk for NICU admission.

Trial registration

Retrospectively registered.

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

Background

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

Methods

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

Results

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

Conclusions

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

Detection of fetal kicks using body-worn accelerometers during pregnancy: Trade-offs between sensors number and positioning

Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometer-based systems have been developed in the past few years, to tackle common issues in ultrasound measurement and enable remote, self-administrated monitoring of fetal movement during pregnancy. However, many questions remain unanswered to date on the optimal setup in terms of body-worn accelerometers as well as signal processing and machine learning techniques used to detect fetal movement. In this paper, we systematically analyze the trade-offs between sensor number and positioning, the presence of reference accelerometers outside of the abdominal area and provide guidelines on dealing with class imbalance. Using a dataset of 15 measurements collected employing 6 three-axial accelerometers we show that including a reference accelerometer on the back of the participant consistently improves fetal movement detection performance regardless of the number of sensors utilized. We also show that two accelerometers plus a reference accelerometer are sufficient for optimal results.

Monitoring fetal maturation-objectives, techniques and indices of autonomic function

Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of 'fetal programming', also known as 'developmental origins of adult disease hypothesis', e.g. applies for cardiovascular, metabolic, hyperkinetic, cognitive disorders. Since the autonomic nervous system is involved in all of those systems, cardiac autonomic control may provide relevant functional diagnostic and prognostic information. The fetal heart rate patterns (HRP) are one of the few functional signals in the prenatal period that relate to autonomic control and, therefore, is predestinated for its evaluation. The development of sensitive markers of fetal maturation and its disturbances requires the consideration of physiological fundamentals, recording technology and HRP parameters of autonomic control. Based on the ESGCO2016 special session on monitoring the fetal maturation we herein report the most recent results on: (i) functional fetal autonomic brain age score (fABAS), Recurrence Quantitative Analysis and Binary Symbolic Dynamics of complex HRP resolve specific maturation periods, (ii) magnetocardiography (MCG) based fABAS was validated for cardiotocography (CTG), (iii) 30 min recordings are sufficient for obtaining episodes of high variability, important for intrauterine growth restriction (IUGR) detection in handheld Doppler, (iv) novel parameters from PRSA to identify Intra IUGR fetuses, (v) evaluation of fetal electrocardiographic (ECG) recordings, (vi) correlation between maternal and fetal HRV is disturbed in pre-eclampsia. The reported novel developments significantly extend the possibilities for the established CTG methodology. Novel HRP indices improve the accuracy of assessment due to their more appropriate consideration of complex autonomic processes across the recording technologies (CTG, handheld Doppler, MCG, ECG). The ultimate objective is their dissemination into routine practice and studies of fetal developmental disturbances with implications for programming of adult diseases.

Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses

The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying data mining techniques to FHR parameters, obtained from recordings in a population of 122 fetuses (61 healthy and 61 IUGRs), through standard CTG non-stress test. We computed N=12 indices (N=4 related to time domain FHR analysis, N=4 to frequency domain and N=4 to non-linear analysis) and normalized them with respect to the gestational week. We compared, through a 10-fold crossvalidation procedure, 15 data mining techniques in order to select the more reliable approach for identifying IUGR fetuses. The results of this comparison highlight that two techniques (Random Forest and Logistic Regression) show the best classification accuracy and that both outperform the best single parameter in terms of mean AUROC on the test sets.

Multivariate analysis based on linear and non-linear FHR parameters for the identification of IUGR fetuses

Fetal Heart Rate (FHR) monitoring represents a powerful tool for checking the arousal of pathological fetal conditions during pregnancy. This paper proposes a multivariate approach for the discrimination of Normal and Intra Uterine Growth Restricted (IUGR) fetuses based on a small set of parameters computed on the FHR signal. We collected FHR recordings in a population of 120 fetuses (60 normals and 60 IUGRs) at approximately the same gestational week through a standard CTG non-stress test. A set of 8 linear and non-linear indices were selected and computed on each recording, on the basis of their "stand-alone" discriminative properties, demonstrated in previous studies. By using the Orange® data mining suite we checked various multivariate discrimination models. The results show that a Logistic Regression performed on a limited set of only 4 parameters can reach 92.5% accuracy in the correct identification of fetuses, with 93% sensitivity and 91.5% specificity.

Accelerometer-based fetal movement detection

Monitoring fetal wellbeing is a compelling problem in modern obstetrics. Clinicians have become increasingly aware of the link between fetal activity (movement), well-being, and later developmental outcome. We have recently developed an ambulatory accelerometer-based fetal activity monitor (AFAM) to record 24-hour fetal movement. Using this system, we aim at developing signal processing methods to automatically detect and quantitatively characterize fetal movements. The first step in this direction is to test the performance of the accelerometer in detecting fetal movement against real-time ultrasound imaging (taken as the gold standard). This paper reports first results of this performance analysis.

FETAL HEART RATE VARIABILITY ANALYSIS FROM PHONOCARDIOGRAPHIC RECORDINGS

This paper presents an algorithm for classification of fetal health status using fetal heart rate variability (fHRV) analysis through phonocardiography. First, the fetal heart sound signals are acquired from the maternal abdominal surface using a specially developed Bluetooth-based wireless data recording system. Then, fetal heart rate (FHR) traces are derived from these signals. Ten numbers of linear and nonlinear features are extracted from each FHR trace. Finally, the multilayer perceptron (MLP) neural network is used to classify the health status of the fetus. Results show very promising performance toward the prediction of fetal wellbeing on the set of collected fetal heart sound signals. Finally, this work is likely to lead to an automatic screening device with additional potential of predicting fetal wellbeing.

Fetal vibroacoustic stimulation in computerized cardiotocographic analysis: the role of short-term variability and approximate entropy

The aim of this study was to evaluate the impact of vibroacoustic stimulation (VAS) on computerized cardiotocography short-term variability (STV) and approximate entropy (ApEn) in both low- and high-risk pregnancies. VAS was performed on 121 high- and 95 low-risk pregnancies after 10 minutes of continuous quiet, while their FHR parameters were monitored and recorded by cCTG analysis. Fetal heart rate was recorded using a computer-assisted equipment. Baseline FHR, accelerations, decelerations, STV, long-term irregularity (LTI), ApEn, and fetal movements (FMs) were calculated for defined observational periods before VAS and after 10 minutes. Data were also investigated in relationship with the perinatal outcome. In each group of patients, FHR after VAS remained almost unmodified. Fetal movements significantly increased after VAS in both groups. Results show that only in the high-risk pregnancies, the increase of STV and the decrease of ApEn after VAS were significantly associated with favorable perinatal outcomes.

FEATURE EXTRACTION AND CLASSIFICATION OF FETAL HEART RATE USING WAVELET ANALYSIS AND SUPPORT VECTOR MACHINES

Since the fetus is not available for direct observations, only indirect information can guide the obstetrician in charge. Electronic Fetal Monitoring (EFM) is widely used for assessing fetal well being. EFM involves detection of the Fetal Heart Rate (FHR) signal and the Uterine Activity (UA) signal. The most serious fetal incident is the hypoxic injury leading to cerebral palsy or even death, which is a condition that must be predicted and avoided. This research work proposes a new integrated method for feature extraction and classification of the FHR signal able to associate FHR with umbilical artery pH values at delivery. The proposed method introduces the use of the Discrete Wavelet Transform (DWT) to extract time-scale dependent features of the FHR signal and the use of Support Vector Machines (SVMs) for the categorization. The proposed methodology is tested on a data set of intrapartum recordings were the FHR categories are associated with umbilical artery pH values, This proposed approach achieved high overall classification performance proving its merits.

Intrapartum cardiotocography (CTG) and ST-analysis of labor in diabetic patients

AIM

To determine the prevalence and types of intrapartum cardiotocography (CTG) patterns and investigate their relationship to moderate acidemia in term fetuses of diabetic mothers. Also, to assess if the combination of fetal electrocardiogram (FECG) and those CTG patterns strengthens the association with moderate acidemia.

MATERIAL AND METHODS

The material for this study is obtained from the Swedish randomized control trial and the European Union ST-analysis trial. We developed an analytical model for CTG patterns based on the progress in CTG changes, in a longitudinal periodic manner. The model was then combined with information regarding changes in ST interval that indicate threatening asphyxia, and the findings were analyzed to determine correlation with the presence of moderate acidemia at birth.

RESULTS

This study involved data of 413 diabetic mothers. A preterminal CTG was more common in the diabetes mellitus (DM) group (6/70, 8.6%) than in the gestational diabetes (GD) group (3/307, 1.0%; P=0.003). For diabetic mothers (i.e., DM+GD) with a normal CTG at the start of monitoring, the presence of FECG data indicating asphyxia significantly increased the likelihood of an umbilical artery pH<7.15 at birth [odds ratio (OR)=3.65, 95% confidence interval (CI)=1.33-10.05]. Among labors where the CTG was non-reassuring at the start of monitoring, no significant association was found between pH<7.15 and indication to intervene according to FECG information (OR=1.51, 95% CI=0.33-7.0).

CONCLUSIONS

A preterminal CTG is more common in the fetuses of DM than GD mothers during labor. When CTG was normal at the start of recording, the addition of FECG information gave a significant add on information to predict moderate acidemia.

A Review of Fetal ECG Signal Processing; Issues and Promising Directions

The field of electrocardiography has been in existence for over a century, yet despite significant advances in adult clinical electrocardiography, signal processing techniques and fast digital processors, the analysis of fetal ECGs is still in its infancy. This is, partly due to a lack of availability of gold standard databases, partly due to the relatively low signal-to-noise ratio of the fetal ECG compared to the maternal ECG (caused by the various media between the fetal heart and the measuring electrodes, and the fact that the fetal heart is simply smaller), and in part, due to the less complete clinical knowledge concerning fetal cardiac function and development. In this paper we review a range of promising recording and signal processing techniques for fetal ECG analysis that have been developed over the last forty years, and discuss both their shortcomings and advantages. Before doing so, however, we review fetal cardiac development, and the etiology of the fetal ECG. A selection of relevant models for the fetal/maternal ECG mixture is also discussed. In light of current understanding of the fetal ECG, we then attempt to justify recommendations for promising future directions in signal processing, and database creation.

Comparison of short term variability indexes in cardiotocographic foetal monitoring

Concise indexes related to variability of foetal heart rate (FHR) are usually utilised for foetal monitoring; they enrich information provided by cardiotocography (CTG). Most attention is paid to the short term variability (STV), which relates to activity and reaction of autonomic nervous control of foetal heart. There is not a unique method to compute short term variability of the FHR but different formulas have been proposed and are employed in clinical and scientific environments: this leads to different evaluations and makes difficult comparative studies. Nine short term variability indexes: Arduini, Dalton, Organ, Sonicaid 8000, Van Geijn, Yeh, Zugaib a modified version of Arduini index and Standard Deviation were considered and compared to test their robustness in CTG applications. A large set of synthetic foetal heart rate series with known features were used to compare indexes performances. Different amounts of variability, mean foetal heart rate, storage rates, baseline variations were considered. The different indexes were in particular tested for their capability to recognise short term heart rate variability variation, their dependence on heart rate signal storage rate (as those provided by commercial cardiotocographic devices), on mean value of the foetal heart rate and on modifications of the floatingline, such in case of accelerations or decelerations. Concise statistical parameters relative to indexes scores were presented in comparative tables. Results indicate that although the indexes are able to recognise STV variation, they show substantial differences in magnitude and some in sensibility. Results depend on the frequency used to acquire and store FHR data (depending on devices); in general, the lower is data rate the more degraded are the results. Furthermore, results differently depend on FHR mean, some for their intrinsic definition; differences arise also in correspondences of accelerations and decelerations. Our results demonstrate that only indexes which refer directly to differences in FHR values, such as Organ and SD indexes, not show dependence on FHR mean. The use of the Standard Deviation index may provide efficient information while showing independence from the considered variables. Indexes performance in case of real cardiotocographic signals were also presented as examples.

Identification of fetal sufferance antepartum through a multiparametric analysis and a support vector machine

The present work is concerned with the automatic identification of fetal sufferance in intrauterine growth retarded (IUGR) fetuses, based on a multiparametric analysis of cardiotocographic recordings feeding a neural classifier. As classification tool, we propose a SVM (support vector machine), which receives the set of linear and nonlinear parameters extracted from the fetal heart rate signal (FHR) as input and gives the indication of fetal distress as output. SVM is a powerful supervised learning algorithm belonging to the statistical learning theory. It minimizes the structural risk performance in various classification problems. Three SVMs are built with different kernels. Their training set includes 70 cases: 35 normal and 35 IUGR suffering fetuses. Classification results obtained with a 2nd order polynomial kernel, on a test set of 30 unknown cases, show good values of accuracy, specificity and sensitivity. The SVM performance is very similar to that obtained with multilayer perceptron and neurofuzzy classifiers proposed in previous works. The introduction of a hybrid unsupervised/supervised learning scheme integrating independent component analysis (ICA) with SVM will be the natural development of this work with a further improvement of the diagnostic ability of the system.

New approach to quantitative description of deceleration of fetal heart rate for the patterns classification

The most common method of biophysical fetal monitoring is recording and analyzing the cardiotocographic signals. In analysis of the fetal heart rate signal special emphasis is paid to the deceleration patterns and their correlation to the uterine contractions. According to deceleration classification the most important is the distinguishing between the periodic and the episodic types. In visual analysis, this classification is based on fuzzy description of deceleration onset being "abrupt" or "gradual". Application of commonly used interpretation of these imprecise terms in computer aided monitoring systems very often leads to erroneous classifications. Therefore, the redefinition of the deceleration nadir phase, as a group of samples around the lowest point, is required. It ensures that the onset phase, which is very important in deceleration classification, will consist of only appropriate samples. For determination of nadir the new method based on three stage-analysis of samples frequency distribution was developed. To evaluate the proposed method we compared the results with reference data obtained from clinical experts.

Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines

Cardiotocography is the main method used for fetal assessment in every day clinical practice for the last 30 years. Many attempts have been made to increase the effectiveness of the evaluation of cardiotocographic recordings and minimize the variations of their interpretation utilizing technological advances. This research work proposes and focuses on an advanced method able to identify fetuses compromised and suspicious of developing metabolic acidosis. The core of the proposed method is the introduction of a support vector machine to "foresee" undesirable and risky situations for the fetus, based on features extracted from the fetal heart rate signal at the time and frequency domains along with some morphological features. This method has been tested successfully on a data set of intrapartum recordings, achieving better and balanced overall performance compared to other classification methods, constituting, therefore, a promising new automatic methodology for the prediction of metabolic acidosis.

Foetal heart rate power spectrum response to uterine contraction

Cardiotocography is the most diffused prenatal diagnostic technique in clinical routine. The simultaneous recording of foetal heart rate (FHR) and uterine contractions (UC) provides useful information about foetal well-being during pregnancy and labour. However, foetal electronic monitoring interpretation still lacks reproducibility and objectivity. New methods of interpretation and new parameters can further support physicians' decisions. Besides common time-domain analysis, study of the variability of FHR can potentially reveal autonomic nervous system activity of the foetus. In particular, it is clinically relevant to investigate foetal reactions to UC to diagnose foetal distress early. Uterine contraction being a strong stimulus for the foetus and its autonomic nervous system, it is worth exploring the FHR variability response. This study aims to analyse modifications of the power spectrum of FHR variability corresponding to UC. Cardiotocographic signal tracts corresponding to 127 UC relative to 30 healthy foetuses were analysed. Results mainly show a general, statistically significant (t test, p<0.01) power increase of the FHR variability in the LF 0.03-0.2 Hz and HF 0.2-1 in correspondence of the contraction with respect to a reference tract set before contraction onset. Time evolution of the power within these bands was computed by means of time-varying spectral estimation to concisely show the FHR response along a uterine contraction. A synchronised grand average of these responses was also computed to verify repeatability, using the contraction apex as time reference. Such modifications of the foetal HRV that follow a contraction can be a sign of ANS reaction and, therefore, additional, objective information about foetal reactivity during labour.

Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress

This paper considers the multiscale entropy (MSE) approach for estimating the regularity of time series at different scales. Sample entropy (SampEn) and approximate entropy (ApEn) are evaluated in MSE analysis on simulated data to enhance the main features of both estimators. We applied the approximate entropy and the sample entropy estimators to fetal heart rate signals on both single and multiple scales for an early identification of fetal sufferance antepartum. Our results show that the ApEn index significantly distinguishes suffering from normal fetuses between the 30th and the 35th week of gestation. Furthermore, our data shows that the MSE entropy values are reliable indicators of the fetal distress associated with the presence of a pathological condition at birth.

Self-similarity behavior characterization of fetal heart rate signal in healthy and intrauterine growth retarded fetuses

In this paper we deal with the problem of the interpretation of the fetal heart rate (FHR) signal. From literature is known that FHR contains both linear and non linear components. Starting from this consideration we analyzed FHR as a fractal time series and we evaluated its self similarity behavior using the Hurst's coefficient (H). We first evaluated the stationarity of FHR time series and then we estimated H with Detrend fluctuation analysis (DFA) method. We calculated Hurst's coefficient for healthy fetuses and for fetuses affected by Intrauterine grow retardation (IUGR). Results provided H = 0.350 +/- 0.064 (avg +/- std) for healthy patients and H = 0.461 +/- 0.059 for IUGR. It is also shown that IUGR patients exhibit a "less non-stationary" and longer-memory behavior than normals with a reduced information content of FHR signal. We propose for this phenomenon a physiological explanation connected with the abnormal autonomic nervous system development of IUGR patients.

Antepartum cardiotocography: a study of fetal reactivity in frequency domain

Cardiotocography (CTG) is the most widely used diagnostic technique in clinical practice to monitor fetal health. Cardiotocographic recording also permits to assess maturation of the fetal autonomous nervous system (ANS): fetal heart rate (FHR) modifications may reveal ANS' reactions to stimuli. To assess fetal reactivity, physicians evaluate specific clinical CTG parameters, generally, by means of visual inspection, thus depending on observer's expertise, with lack of reproducibility. Still nowadays, there is a very high intra- and inter-observer variation in the assessment of FHR patterns. More objective methods for CTG interpretation are of crucial importance. For adults, frequency analysis of heart rate variability (HRV) is a non-invasive and powerful method to investigate ANS activity. This frequency analysis can also be a valid support for a better knowledge of fetal ANS functional state and reactions. Indeed, fetal HRV is a good indicator of fetal well-being in non-stress conditions. Fetal reactivity is a very important CTG characteristic used to diagnose fetal distress, but its interpretation is still uncertain. The aim of this study is to characterise fetal reactivity proposing new fetal HRV frequency parameters to support a more exhaustive CTG analysis.

Fetal heart rate patterns and ECG ST segment changes preceding metabolic acidaemia at birth

OBJECTIVES

To compare the rates of abnormal ST segment patterns of the ECG and cardiotocographic (CTG) abnormalities in fetuses with metabolic acidaemia at birth and controls. To evaluate the inter-observer agreement in interpretation of ST analysis and CTG.

DESIGN

Case-control study.

SETTING

Three University hospitals in southern Sweden.

POPULATION

Cases and controls were selected from the Swedish randomised controlled trial on intrapartum monitoring, including 4966 fetuses monitored with a scalp electrode.

METHODS

Two obstetricians independently assessed the CTG and ST traces of 41 fetuses with metabolic acidaemia at birth and 101 controls, blinded to group, outcome and all clinical data. They classified each CTG trace and ST analysis as abnormal or not abnormal, and whether there was indication to intervene according to the CTG or to the CTG + ST guidelines. If their classification differed, assessment by a third obstetrician determined the final classification.

MAIN OUTCOME MEASURES

Rates of CTG and ST abnormalities and decisions to intervene. Rates of inter-observer agreement.

RESULTS

CTG was classified as abnormal in 50% and ST in 63% of cases with acidaemia, and in 20% and 34% of controls, respectively. CTG abnormalities were judged to be indication for intervention in 45% and CTG + ST abnormalities in 56% of cases with acidaemia, and in 15% and 8% of controls, respectively. The proportion of agreement between the two initial observers was significantly higher for ST abnormalities (94%) than for CTG abnormalities (73%), and for indication to intervene according to CTG + ST (89%) than according to CTG alone (76%).

CONCLUSIONS

The inter-observer agreement rate was higher for a decision to intervene based on CTG + ST than on CTG alone.

Quality of intrapartum cardiotocography in twin deliveries

OBJECTIVE

Intrapartum fetal heart rate (FHR) recordings in twins were compared for fetal signal loss during both stages of labor to assess the quality of these recordings by the method that had been used: external ultrasound or directly via a scalp electrode.

STUDY DESIGN

Analysis of recordings collected between January 1, 1994, and January 1, 2002, from consecutive twin deliveries at the Vrije Universiteit Medical Center in Amsterdam. One hundred seventy-two twins that delivered via the vaginal route were included in the study. FHR recordings had a duration of at least 1 hour before the birth of the second twin. Subdivision took place on the basis of the recording technique, ie, ultrasound or scalp electrode. FHR data was obtained with HP-M1350 cardiotocographs. The status (pen on, pen off, maternal signal) and the mode of the signals were acquired. The duration of pen lifts and maternal signals was divided by the total duration of the recording. Statistical analyses were performed with the Mann-Whitney U test and the Wilcoxon signed ranks test.

RESULTS

Recordings obtained via ultrasound demonstrated significantly more fetal signal loss than those obtained via the direct mode, particularly in the second stage. Approximately 26% to 33% of first stage and 41% to 63% of second stage ultrasound intrapartum FHR recordings in twins exceeded the International Federation of Gynecology and Obstetrics (FIGO) criteria for fetal signal loss.

CONCLUSION

Intrapartum FHR monitoring via ultrasound provides far poorer quality FHR signals than the direct mode. The direct mode deserves a more prominent position in fetal surveillance than it currently has.

Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and simple tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless, very poor indications on fetal pathologies can be inferred from the even automatic CTG analysis methods, which are actually employed. The feeling is that fetal heart rate (FHR) signals and uterine contractions carry much more information on fetal state than is usually extracted by classical analysis methods. In particular, FHR signal contains indications about the neural development of the fetus. However, the methods actually adopted for judging a CTG trace as "abnormal" give weak predictive indications about fetal dangers. We propose a new methodological approach for the CTG monitoring, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy). This preliminary study considers 14 normal fetuses, eight cases of gestational (maternal) diabetes, and 13 intrauterine growth retarded fetuses. A comparison with the traditional time domain analysis is also included. This paper shows that the proposed new parameters are able to separate normal from pathological fetuses. Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies.

Prenatal telemedicine: clinical experience with conventional and computerised antepartum telecardiotocography

OBJECTIVE

To describe the clinical results of the first working year of a telemedicine project based on computerised telecardiotocography.

STUDY DESIGN

The project is based on the "TOCOMAT" system, which remotely recorded and processed cardiotocograms performed at five peripheral units from high and low risk patients, then transferred them to a University Operative Centre, where they were displayed, stored and analysed by the 2CTG system and by two expert observers.

RESULTS

457 traces were analysed. The perinatal outcome was good, except for two high-risk fetuses. Both patients and carers had favourable reactions. The management of patients with pregnancies at risk was improved by the interaction of the physicians involved with the experts at the Operative Centre.

CONCLUSIONS

Telemedicine could enable the decentralization of perinatal surveillance, improving quality of life for pregnant and newborn and rationalizing costs for prenatal care.

Calcium channel blockers in the management of preterm labor and hypertension in pregnancy

Calcium channel blockers (CCBs) have the ability to inhibit contractility in smooth muscle cells. CCBs have an already established role in the treatment of non-pregnant hypertension and angina pectoris. Some epidemiological studies found an association between the use of CCBs and an increase in cardiovascular mortality, malignancy, and gastrointestinal bleeding. More recent studies with many more patients and a longer follow-up did not find these associations. In obstetrics CCBs have become increasingly popular for the management of preterm labor and pregnancy-induced hypertensive disorders. Meta-analysis shows that use of nifedipine in comparison with betamimetics is associated with a more frequent successful prolongation of pregnancy in case of preterm labor, resulting in significantly fewer admissions of newborns to the neonatal intensive care unit (NICU), and is associated with a lower incidence of respiratory distress syndrome. No adverse fetal side effects in humans have been reported with the use of nifedipine for obstetric indications. Nifedipine is an effective and safe drug to use when tocolytic therapy is indicated for preterm labor. In preeclampsia nifedipine effectively lowers blood pressure and can be a good alternative for (di) hydralazine.

Fetal heart rate in relation to its variation in normal and growth retarded fetuses

OBJECTIVES

(1) to assess the relationship of basal fetal heart rate (FHR) with both long term (LTV) and short term (STV) FHR variation in low-risk pregnancies, longitudinally from 24 weeks gestation onwards and (2) to investigate the relationship of FHR with LTV and STV in intrauterine growth retarded (IUGR) fetuses.

STUDY DESIGN

Computerised FHR recordings were made in twenty-nine uncomplicated pregnancies (n=224) and in twenty-seven IUGR fetuses who were selected retrospectively from three databases (n=135). Nomograms of FHR variation with FHR and GA were constructed using multilevel analysis.

RESULTS AND CONCLUSIONS

There was a strong negative relationship of FHR with both LTV and STV in the control group (R2=53% and 52%, respectively). In the IUGR fetuses, FHR was generally higher than in normal fetuses whereas LTV and STV were lower. The relationship of FHR with LTV and STV in the IUGR group was less strong (for both: R2=18%). Correction of FHR variation for basal FHR in the IUGR fetuses only resulted in a slight reduction in the number of recordings with a variation below the normal range. As it does not improve the recognition of fetuses being considered at the highest risk, such a correction of FHR variation for basal FHR is therefore not necessary. Intrafetal consistency, known to be present in healthy fetuses, was also present in the IUGR fetuses with a low FHR variation.

Temporal changes in fetal cardiovascular, behavioural, metabolic and endocrine responses to maternally administered dexamethasone in the late gestation fetal sheep

OBJECTIVE

To determine the primary (0-12 h) and secondary (12-24 h) effects of dexamethasone on fetal heart rate, short term heart rate variation, blood pressure, breathing movements and electrocortical activity, blood gas exchange, metabolism and adrenocortical function in the late gestation sheep fetus.

DESIGN

Comparison of the effects of a single maternally administered intramuscular injection of dexamethasone (12 mg) with those of saline vehicle from 1 h before injection to 24 h post-injection. Fetal cardiovascular and behavioural parameters were recorded continuously. Fetal and maternal blood samples were taken at regular intervals for blood gas, glucose and lactate, cortisol and adrenocorticotrophin measurements.

SAMPLE

Sixteen chronically instrumented singleton fetal sheep at 127-133 days of gestation (term is about 147 days).

RESULTS

During the primary phase short term heart rate variation fell (P < 0.001), and this was associated with a transient fall in the incidence of fetal breathing movements, a fall in fetal heart rate and a rise in fetal blood pressure. By 12 h there was a significant increase in short term heart rate variation (P < 0.001) and a rise in fetal heart rate, but blood pressure and fetal breathing movements had returned to normal. Dexamethasone significantly reduced fetal PaO2 throughout most of the experimental period, particularly 1 h post-injection (P < 0.005). Fetal and maternal plasma cortisol and adrenocorticotrophin concentrations fell significantly from 1 h post-injection.

CONCLUSIONS

The effects of dexamethasone on fetal heart rate variation are more complex than previously described with both a fall and an increase observed depending on the time at which heart rate variation was measured after injection. Dexamethasone also caused a significant fall in fetal PaO2, and although this was not to hypoxic levels in normoxic fetuses it does raise questions about the potential impact of dexamethasone on chronically hypoxic fetuses.

Denoising of the fetal heart rate signal with non-linear filtering of the wavelet transform maxima

The fetal heart rate (FHR) signal provides valuable information for fetal development and well-being. However, the FHR traces derived from present-day ultrasound cardiotocographs are not of the desired quality. The paper applies the wavelet transform (WT) in order to denoise effectively the FHR signal. The denoising procedure analyses the evolution of the WT maxima across scales. The singularities of the signal create wavelet maxima with different properties from those of the induced noise. Since it is difficult to formulate precise rules that distinguish between the wavelet maxima of the FHR signal from those of the noise we have trained a neural network for this classification task. The neural network draws out successfully the noise induced wavelet maxima. An improved FHR signal can be obtained from the coarser wavelet approximation signal component and the filtered wavelet maxima by means of the inverse dyadic wavelet transform. Also, feature extraction and processing algorithms can be defined on the denoised wavelet coefficients (instead of on the original signal).