Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks

Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review

Background

The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others.

Objective

The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals.

Methods

The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process.

Results

The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals.

Conclusions

In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.

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.

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.

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.

Systematic Review of Intrapartum Fetal Heart Rate Spectral Analysis and an Application in the Detection of Fetal Acidemia

It is fundamental to diagnose fetal acidemia as early as possible, allowing adequate obstetrical interventions to prevent brain damage or perinatal death. The visual analysis of cardiotocography traces has been complemented by computerized methods in order to overcome some of its limitations in the screening of fetal hypoxia/acidemia. Spectral analysis has been proposed by several studies exploring fetal heart rate recordings while referring to a great variety of frequency bands for integrating the power spectrum. In this paper, the main goal was to systematically review the spectral bands reported in intrapartum fetal heart rate studies and to evaluate their performance in detecting fetal acidemia/hypoxia. A total of 176 articles were reviewed, from MEDLINE, and 26 were included for the extraction of frequency bands and other relevant methodological information. An open-access fetal heart rate database was used, with recordings of the last half an hour of labor of 246 fetuses. Four different umbilical artery pH cutoffs were considered for fetuses' classification into acidemic or non-acidemic: 7.05, 7.10, 7.15, and 7.20. The area under the receiver operating characteristic curve (AUROC) was used to quantify the frequency bands' ability to distinguish acidemic fetuses. Bands referring to low frequencies, mainly associated with neural sympathetic activity, were the best at detecting acidemic fetuses, with the more severe definition (pH ≤ 7.05) attaining the highest values for the AUROC. This study shows that the power spectrum analysis of the fetal heart rate is a simple and powerful tool that may become an adjunctive method to CTG, helping healthcare professionals to accurately identify fetuses at risk of intrapartum hypoxia and to implement timely obstetrical interventions to reduce the incidence of related adverse perinatal outcomes.

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.

Use of automated fetal heart rate analysis to identify risk factors for umbilical cord acidosis at birth

OBJECTIVE

To identify clinical parameters and intrapartum fetal heart rate parameters associated with a risk of umbilical cord acidosis at birth, using an automated analysis method based on empirical mode decomposition.

METHODS

Our single-center study included 381 cases (arterial cord blood pH at birth pHa ≤7.15) and 1860 controls (pHa ≥7.25) extracted from a database comprising 8,383 full datasets for over-18 mothers after vaginal or caesarean non-twin, non-breech deliveries at term (>37 weeks of amenorrhea). The analysis of a 120-min period of the FHR recording (before maternal pushing or the decision to perform a caesarean section during labor) led to the extraction of morphological, frequency-related, and long- and short-term heart rate variability variables. After univariate analyses, sparse partial least square selection and logistic regression were applied.

RESULTS

Several clinical factors were predictive of fetal acidosis in a multivariate analysis: nulliparity (odds ratio (OR) 95% confidence interval (CI)]: 1.769 [1.362-2.300]), a male fetus (1.408 [1.097-1.811]), and the term of the pregnancy (1.333 [1.189-1.497]). The risk of acidosis increased with the time interval between the end of the FHR recording and the delivery (OR [95%CI] for a 1-min increment: 1.022 [1.012-1.031]). The risk factors related to the FHR signal were mainly the difference between the mean baseline and the mean FHR (OR [95%CI]: 1.292 [1.174-1.424]), the baseline range (1.027 [1.014-1.040]), fetal bradycardia (1.038 [1.003-1.075]) and the late deceleration area (1.002 [1.000-1.005]). The area under the curve for the multivariate model was 0.79 [0.76; 0.81].

CONCLUSION

In addition to clinical predictors, the automated FHR analysis highlighted other significant predictors, such as the baseline range, the instability of the FHR signal and the late deceleration area. This study further extends the routine application of automated FHR analysis during labor and, ultimately, contributes to the development of predictive scores for fetal acidosis.

On the prediction of foetal acidaemia: A spectral analysis-based approach

A computational analysis of physiological systems has been used to support the understanding of how these systems work, and in the case of foetal heart rate, many different approaches have been developed in the last decades. Our objective was to apply a new method of classification, which is based on spectral analysis, in foetal heart rate (FHR) traces to predict foetal acidosis diagnosed with umbilical arterial blood pH ≤ 7.05. Fast Fourier transform was applied to a real database for the classification approach. To evaluate the models, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were used. Sensitivity equal to 1, specificity equal to 0.85 and an area under the ROC curve of 0.94 were found. In addition, when the definition of metabolic acidosis of umbilical arterial blood pH ≤ 7.05 and base excess ≤ -10 mmol/L was used, the proposed methodology obtained sensitivity = 1, specificity = 0.97 and area under the ROC curve = 0.98. The proposed methodology relies exclusively on the spectral frequency decomposition of the FHR signal. After further successful validation in more datasets, this approach can be incorporated easily in clinical practice due to its simple implementation. Likewise, the incorporation of this novel technique in an intrapartum monitoring station should be straightforward, thus enabling the assistance of labour professionals in the anticipated detection of acidaemia.

The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network

Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named "MKNet" and recurrent neural network named "MKRNN" are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.

Cardiotocography and beyond: a review of one-dimensional Doppler ultrasound application in fetal monitoring

One-dimensional Doppler ultrasound (1D-DUS) provides a low-cost and simple method for acquiring a rich signal for use in cardiovascular screening. However, despite the use of 1D-DUS in cardiotocography (CTG) for decades, there are still challenges that limit the effectiveness of its users in reducing fetal and neonatal morbidities and mortalities. This is partly due to the noisy, transient, complex and nonstationary nature of the 1D-DUS signals. Current challenges also include lack of efficient signal quality metrics, insufficient signal processing techniques for extraction of fetal heart rate and other vital parameters with adequate temporal resolution, and lack of appropriate clinical decision support for CTG and Doppler interpretation. Moreover, the almost complete lack of open research in both hardware and software in this field, as well as commercial pressures to market the much more expensive and difficult to use Doppler imaging devices, has hampered innovation. This paper reviews the basics of fetal cardiac function, 1D-DUS signal generation and processing, its application in fetal monitoring and assessment of fetal development and wellbeing. It also provides recommendations for future development of signal processing and modeling approaches, to improve the application of 1D-DUS in fetal monitoring, as well as the need for annotated open databases.

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

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

Evolution of linear and nonlinear fetal heart rate indices throughout pregnancy in appropriate, small for gestational age and preterm fetuses: A cohort study

BACKGROUND AND OBJECTIVES

To assess the evolution of linear and nonlinear fetal heart rate (FHR) analysis throughout pregnancy in appropriate (AGA), small for gestational age (SGA) and preterm (PTB) fetuses.

METHODS

A prospective cohort study was carried out in 171 singleton pregnancies divided in three groups: AGA (n = 147), SGA (n = 13) fetuses and spontaneous PTB (n = 11). FHR was recorded with an external sensor from the 24th to the 40th week of gestation. Linear time- and frequency-domain and nonlinear FHR indices were computed on 10-min segments. Longitudinal analysis of indices throughout pregnancy was performed with generalized estimating equations, and receiver operating characteristic (ROC) curves were calculated for the prediction of SGA and PTB fetuses.

RESULTS

Increasing gestational age significantly affected most FHR indices, with a general increase in variability and entropy indices, and a decrease in mean FHR. The PTB group exhibited a significantly lower short-term variation, and no monotonic increase in the sympatho-vagal balance as observed in the AGA group. The SGA group exhibited higher long-term irregularity and lower short-term irregularity than the AGA group throughout gestation. In prediction of SGA and PTB, the largest areas under the ROC curves obtained were 0.76 and 0.78, respectively.

CONCLUSIONS

Linear and nonlinear FHR analysis provides useful information on the evolution of fetal autonomic nervous and complexity control systems throughout pregnancy, in relation with AGA, SGA and PTB fetuses, which may be helpful in clinical practice.

Pathophysiology of Perinatal Hypoxic-Ischemic Encephalopathy – Biomarkers, Animal Models and Treatment Perspectives

Hypoxic-ischemic encephalopathy (HIE) is one of the leading pediatric neurological conditions causing long-term disabilities and socio-economical burdens. Nearly 20-50 % of asphyxiated newborns with HIE die within the newborn period and another third will develop severe health consequences and permanent handicaps. HIE is the result of severe systemic oxygen deprivation and reduced cerebral blood flow, commonly occurring in full-term infants. Hypoxic-ischemic changes trigger several molecular and cellular processes leading to cell death and inflammation. Generated reactive oxygen species attack surrounding cellular components resulting in functional deficits and mitochondrial dysfunction. The aim of the present paper is to review present knowledge about the pathophysiology of perinatal hypoxic-ischemic encephalopathy, especially with respect to novel treatment strategies and biomarkers that might enhance early detection of this disorder and thus improve the general outcome of patients.

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.

Scattering transform for intrapartum fetal heart rate variability fractal analysis: a case-control study

Intrapartum fetal heart rate monitoring, aiming at early acidosis detection, constitutes an important public health stake. Scattering transform is proposed here as a new tool to analyze intrapartum fetal heart rate (FHR) variability. It consists of a nonlinear extension of the underlying wavelet transform, that thus preserves its multiscale nature. Applied to an FHR signal database constructed in a French academic hospital, the scattering transform is shown to permit to efficiently measure scaling exponents characterizing the fractal properties of intrapartum FHR temporal dynamics, that relate not only to the sole covariance (correlation scaling exponent), but also to the full dependence structure of data (intermittency scaling exponent). Such exponents are found to satisfactorily discriminate temporal dynamics of healthy subjects (from that of nonhealthy ones) and to emphasize the role of the highest frequencies (around and above 1 Hz) in intrapartum FHR variability. This permits us to achieve satisfactory classification performance that improves on those obtained from the analysis of International Federation of Gynecology and Obstetrics (FIGO) criteria, notably by classifying as healthy a number of subjects that were incorrectly classified as nonhealthy by classical clinically used FIGO criteria. Combined to obstetrician annotations, these scaling exponents enable us to sketch a typology of these FIGO-false positive subjects. Also, they permit us to monitor the evolution along time of the intrapartum health status of the fetuses and to estimate an optimal detection time-frame.

Open access intrapartum CTG database

Background

Cardiotocography (CTG) is a monitoring of fetal heart rate and uterine contractions. Since 1960 it is routinely used by obstetricians to assess fetal well-being. Many attempts to introduce methods of automatic signal processing and evaluation have appeared during the last 20 years, however still no significant progress similar to that in the domain of adult heart rate variability, where open access databases are available (e.g. MIT-BIH), is visible. Based on a thorough review of the relevant publications, presented in this paper, the shortcomings of the current state are obvious. A lack of common ground for clinicians and technicians in the field hinders clinically usable progress. Our open access database of digital intrapartum cardiotocographic recordings aims to change that.

Description

The intrapartum CTG database consists in total of 552 intrapartum recordings, which were acquired between April 2010 and August 2012 at the obstetrics ward of the University Hospital in Brno, Czech Republic. All recordings were stored in electronic form in the OB TraceVue®;system. The recordings were selected from 9164 intrapartum recordings with clinical as well as technical considerations in mind. All recordings are at most 90 minutes long and start a maximum of 90 minutes before delivery. The time relation of CTG to delivery is known as well as the length of the second stage of labor which does not exceed 30 minutes. The majority of recordings (all but 46 cesarean sections) is – on purpose – from vaginal deliveries. All recordings have available biochemical markers as well as some more general clinical features. Full description of the database and reasoning behind selection of the parameters is presented in the paper.

Conclusion

A new open-access CTG database is introduced which should give the research community common ground for comparison of results on reasonably large database. We anticipate that after reading the paper, the reader will understand the context of the field from clinical and technical perspectives which will enable him/her to use the database and also understand its limitations.

Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features

Perinatal hypoxia is a cause of cerebral injury in foetuses and neonates. Detection of foetal hypoxia during labour based on the pattern recognition of heart rate signals suffers from high observer variability and low specificity. We describe a new automated hypoxia detection method using time-frequency analysis of heart rate variability (HRV) signals. This approach uses features extracted from the instantaneous frequency and instantaneous amplitude of HRV signal components as well as features based on matrix decomposition of the signals' time-frequency distributions using singular value decomposition and non-negative matrix factorization. The classification between hypoxia and non-hypoxia data is performed using a support vector machine classifier. The proposed method is tested on a dataset obtained from a newborn piglet model with a controlled hypoxic insult. The chosen HRV features show strong performance compared to conventional spectral features and other existing methods of hypoxia detection with a sensitivity 93.3 %, specificity 98.3 % and accuracy 95.8 %. The high predictive value of this approach to detecting hypoxia is a substantial step towards developing a more accurate and reliable hypoxia detection method for use in human foetal monitoring.

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.

Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features

Cardiotocography is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO), used routinely since the 1960s by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. We assess the features on a large data set (552 records) and unlike in other published papers we use three-class expert evaluation of the records instead of the pH values. We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. The number of accelerations and decelerations, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

Background

Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'.

Methods

The FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples.

Results

For the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684.

Conclusions

Based on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals.

Fetal pulse oximetry: correlation with intrapartum fetal heart rate patterns and neonatal outcome

AIM

To determine how fetal pulse oximetry behaves in various cardiotocographic (CTG) tracings and correlates with neonatal outcome.

PATIENTS AND METHODS

Pregnant women undergoing active labor with singleton pregnancies of 32-42 weeks were enrolled. CTG recordings were reassuring or nonreassuring (namely variable or persisting late decelerations). Pulse oximetry values during labor and changing throughout deceleration and recovery phases, duration and frequency of pulse oximetry recordings <30%, and neonatal outcome were determined. One-way anova, Tukey test, chi(2)-test and multiple logistic regression model were used for statistical analysis where appropriate.

RESULTS

A total of 156 pregnant subjects were divided into three groups: reassuring fetal heart rate (FHR) patterns (group 1, n=78 [50%]), late decelerations (group 2, n=16 [10.3%]) and variable decelerations (group 3, n=62 [39.7%]). The initial and final pulse oximetry readings, pulse values in first stage of labor, the duration and the frequency of pulse oximetry recordings <30% were significantly different between groups (P<0.001, P<0.001, P<0.001, P=0.001, P<0.001). Fetal acidosis was significantly more frequent with late decelerations (23.1%, P=0.004). A multiple logistic regression model demonstrated that the initial pulse oximetry value during active labor was the most predictive variable of neonatal well-being (P<0.001).

CONCLUSION

Decreased fetal pulse oximetry values, especially prolonged and recurrent recordings <30% are well-correlated with abnormal FHR patterns, indicating an association with fetal compromise and metabolic acidosis. Going through active labor with a lower initial value of FSpO(2) more frequently leads to an altered FHR pattern and subsequent adverse fetal outcome.

Computerized analysis of fetal heart rate variability using the matching pursuit technique as an indicator of fetal hypoxia during labor

OBJECTIVE

To determine whether the computerized analysis of fetal heart rate variability with the new matching pursuit technique can indicate fetal distress during labor.

STUDY DESIGN

Eighty women were studied during the intrapartum period with external cardiotocography. In all cases, cord arterial pH and 5-min Apgar Scores were evaluated. Six cases that presented large segments of missing data were excluded from the study. The remaining 74 women were divided into two groups; 32 women with normal (Group A) and 42 women with non-reassuring FHR tracings (group B). Group B was divided in subgroup BI, including 24 women with pH > 7.20, and BII, including 18 women with pH < 7.20. In order to evaluate the FHR fluctuations, in different frequency ranges, we applied an adaptive time-frequency method, called Matching Pursuit. We estimated the power of the FHR signal in four frequency ranges.

RESULTS

The 5-min Apgar Scores were significantly lower in both subgroup BI and subgroup BII (p = 0.003 and p = 0.003 respectively). The Low Low Frequency (LLF) parameter appears to recognize better the cases with lower pH (sensitivity 78.5%, specificity 52.3%) than the cases with non-reassuring FHR (66.6%, 56.2). The sensitivity and specificity of the Very Low Frequency (VLF) parameter were 72.2% and 59% respectively in recognizing the cases with lower pH and 64.2% and 53.1% in recognizing non-reassuring FHR.

CONCLUSION

Fetal hypoxia during labor can be recognized using the MP technique for the analysis of FHR signal power in the VLF and LLF frequency ranges. Since the analysis is feasible in real-time, it can be a useful tool for the intrapartum evaluation of fetal well-being.

Linear and nonlinear fetal heart rate analysis of normal and acidemic fetuses in the minutes preceding delivery

Linear and nonlinear fetal heart rate (FHR) indices, namely mean FHR, interval index (II), very low, low and high frequencies, approximate (ApEn) and sample entropy (SampEn), were computed, immediately before delivery, in the initial and final FHR tracing segments, from 48 normal, 10 mildly acidemic and 10 moderate-to-severely acidemic fetuses. Progression of labor was associated with a significant increase in linear frequency domain indices whereas nonlinear indices were significantly decreased. Moderate-to-severe fetal acidemia was associated with a significant decrease in nonlinear indices. The best discrimination between moderate-to-severe acidemic fetuses and the remaining cases was obtained combining II and ApEn(2,0.15), with a specificity of 71% and a sensitivity of 80%. These findings support the hypothesis of increased autonomic nervous system activity in the final minutes of labor and of decreased central nervous system activity, both in the final minutes of labor and in moderate-to-severe acidemic fetuses.

On the Creation of a New Diagnostic Model for Fetal Well-Being on the Base of Wavelet Analysis of Cardiotocograms

The article is devoted to the description of the results of wavelet analysis of fetal heart rate detecting by cardiotocography method. A number of conclusions are made on the base of such an analysis. It is a part of the research program of creation of a new diagnostic model estimating fetal conditions in antepartum period.

Fetal pulse oximetry and wavelet analysis of the fetal heart rate in the evaluation of abnormal cardiotocography tracings

AIM

Previous studies indicate that the addition of wavelet analysis of the fetal pulse oximetry tracings (FSPO2) and fetal heart rate (FHR) variability to cardiotocography (CTG), for intrapartum fetal monitoring, provides useful information on the fetal response to hypoxia. We applied the new procedure in non-reassuring CTG patterns, in which cesarean section was performed, and tested its accuracy in the diagnosis of the intrapartum fetal compromise.

METHODS

At the 'Aretaieion' University Hospital labor ward, 318 women with term fetuses in the cephalic presentation entered the trial during labor. They all were monitored with external CTG and fetal pulse oximetry. In the cases that cesarean section was applied, because of abnormal CTG tracings, we applied a method based on the multiresolution wavelet analysis and a self-organized map neural network on the first and second stage of labor. The main outcome parameter was the rate of cord metabolic acidosis at birth (pH < 7.05). Secondary outcomes included Apgar scores at 5 min, fetal transmission to neonatal intensive care unit (NICU) and neonatal encephalopathy.

RESULTS

Fifty out of 318 cases delivered operatively because of abnormal CTG patterns (rate 15.72%). In 30 cases, cord pH was >7.05, while in 11 Apgar scores at 5 min were <7, while none of those neonates were transferred to NICU. In the rest 20 cases cord pH was <7.05; in all of these cases Apgar scores at 5 min were <7, while four neonates were transferred to NICU. In one of them, neonatal encephalopathy was diagnosed. After the offline application of wavelet analysis and neural networks to the pulse oximetry and FHR variability readings of the 50 cases, statistics calculated that the system showed a sensitivity of 85% and a specificity of 93%, while false negative and false positive rates were 15% and 7%, respectively.

CONCLUSION

Computerized FHR and FSPO2 monitoring shows an excellent efficacy and reliability in interpreting non-reassuring FHR recordings.

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.

Multiresolution Analysis of Heart Rate Variability as Investigational Tool in Experimental Fetal Cardiac Surgery

Multiresolution analysis of heart rate variability derived from aortic blood pressure, acquired before and after (30 and 60 min) experimental fetal cardiac bypass performed on five ewe's fetuses, was used to investigate the physiological response to an invasive clinical approach. Tachograms were implemented and analyzed by wavelet transform in order to verify the existence of a quantitative relationship between arterial blood gases and time series in the very-low (0.021<f<0.084 Hz) and low (0.084<f<0.337 Hz) frequency band. Multiresolution analysis showed an average decreasing trend from basal condition for all the fetuses investigated in the very-low frequency band, while an opposite trend was highlighted in the low frequency band: this resulting behavior could be related to the temporal evolution of blood gas data. Finally, a slight decrease of sympatho-vagal balance was monitored 30 min after the cardiac bypass was discontinued compared to basal condition. Multiresolution analysis could give more insights on fetal hypoxemia and could also represent a minimally invasive monitoring tool to limit the damage to the fetoplacental unit during experimental fetal cardiac surgery.

Methods of clinical prediction

The ability to predict clinical outcomes is of great importance to physicians and patients alike. Accordingly, multiple different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on univariable and multivariable analysis, as well as models involving the use of neural network, nomograms, and classification and regression trees. The principles of these types of methods, as well as their advantages and disadvantages will be presented.

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.

Internal versus external intrapartum foetal heart rate monitoring: the effect on linear and nonlinear parameters

The effect of foetal heart rate (FHR) acquisition mode on linear and nonlinear parameters is still largely unknown. In 33 normal labouring women, FHR signals were acquired simultaneously by an external ultrasound sensor applied to the maternal abdomen and an internal scalp electrode, in the minutes preceding delivery. For each case, the initial and final 5, 10 and 20 min segments were analysed, considering FHR signals at a frequency of 4 Hz (the frequency at which they are transmitted by the majority of commercialized foetal monitors). Several time and frequency domain linear and nonlinear FHR indices were computed in these segments, namely mean FHR, very low frequency (VLF), low frequency (LF), high frequency (HF), approximate entropy (ApEn) and sample entropy (SampEn). Parametric confidence intervals, statistical tests and correlation coefficients were calculated in order to evaluate the effect of internal versus external FHR monitoring modes on the considered indices. The whole evaluation was repeated using FHR signals at a frequency of 2 Hz. Most time domain linear indices were similar with external and internal monitoring in the initial and final segments of the tracings. However, linear frequency domain indices were poorly correlated in the final segments and had significantly different mean values in the initial segments. Nonlinear indices were significantly different in both initial and final segments. The correlation between 4 and 2 Hz sampled parameters was high for both linear and nonlinear indices (most correlation coefficient values ranging between 0.95 and 1) but nonlinear index values were significantly higher at 2 Hz. In conclusion, the mode used to acquire FHR signals and the sampling rate employed can significantly affect most FHR indices.