Computational intelligence methods for predicting fetal outcomes from heart rate patterns

In this thesis, methods for evaluating the fetal state are compared to make predictions based on Cardiotocography (CTG) data. The first part of this research is the development of an algorithm to extract features from the CTG data. A feature extraction algorithm is presented that is capable of extracting most of the features in the SISPORTO software package as well as late and variable decelerations. The resulting features are used for classification based on both U.S. National Institutes of Health (NIH) categories and umbilical cord pH data. The first experiment uses the features to classify the results into three different categories suggested by the NIH and commonly being used in practice in hospitals across the United States. In addition, the algorithms developed here were used to predict cord pH levels, the actual condition that the three NIH categories are used to attempt to measure. This thesis demonstrates the importance of machine learning in Maternal and Fetal Medicine. It provides assistance for 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 to achieve a more accurate prediction of fetal outcomes using Fetal Heartrate and Uterine Activity with accuracies of greater than 99.5% for predicting categories and greater than 70% for fetal acidosis based on pH values --Abstract, page iii

Predicting complex system behavior using hybrid modeling and computational intelligence

“Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to predict water level rise from river gauge height. Combined with existing infrastructure models, these techniques provide a 15-minute interval for making closure decisions rather than the current 6-hour interval. The second system explored is fetal monitoring, which is essential to diagnose severe fetal conditions such as acidosis. Support Vector Machine and Random Forest were compared to identify the best model for classification of fetal state. This model provided a more accurate classification than existing research on the CTG. A deep learning forecasting model was developed to predict the future values for fetal heart rate and uterine contractions. The forecasting and classification algorithms are then integrated to evaluate the future condition of the fetus. The final model can predict the fetal state 4 minutes ahead to help the obstetricians to plan necessary interventions for preventing acidosis and asphyxiation. In both cases, time series predictions using hybrid modeling provided superior results to existing methods to predict complex behaviors”--Abstract, page iv

Classification of Cardiotocography Data with WEKA

Cardiotocography (CTG) records fetal heart rate (FHR) and uterine contractions (UC) simultaneously. Cardiotocography trace patterns help doctors to understand the state of the fetus. Even after the introduction of cardiotocograph, the capacity to predict is still inaccurate. This paper evaluates some commonly used classification methods using WEKA. Precision,Recall, F-Measrue and ROC curve have been used as the metric to evaluate the performance of classifiers. As opposed to some of the earlier research works that were unable to identify Suspicious and Pathologic patterns, the results obtained from the study in this paper could precisely identify pathologic and Suspicious cases. Best results were obtained from J48, Random Forest and Classification via Regression

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

Zastosowanie rozmytych reguł wnioskowania do automatycznej klasyfikacji zapisów częstości uderzeń serca płodu w odniesieniu do stanu urodzeniowego

Objectives: Fetal monitoring based on the analysis of the fetal heart rate (FHR) signal is the most common method of biophysical assessment of fetal condition during pregnancy and labor. Visual analysis of FHR signals presents a challenge due to a complex shape of the waveforms. Therefore, computer-aided fetal monitoring systems provide a number of parameters that are the result of the quantitative analysis of the registered signals. These parameters are the basis for a qualitative assessment of the fetal condition. The guidelines for the interpretation of FHR provided by FIGO are commonly used in clinical practice. On their basis a weighted fuzzy scoring system was constructed to assess the FHR tracings using the same criteria as those applied by expert clinicians. The effectiveness of the automated classification was evaluated in relation to the fetal outcome assessed by Apgar score. Material and methods: The proposed automated system for fuzzy classification is an extension of the scoring systems used for qualitative evaluation of the FHR tracings. A single fuzzy rule of the system corresponds to a single evaluation principle of a signal parameter derived from the FIGO guidelines. The inputs of the fuzzy system are the values of quantitative parameters of the FHR signal, whereas the system output, which is calculated in the process of fuzzy inference, defines the interpretation of the FHR tracing. The fuzzy evaluation process is a kind of diagnostic test, giving a negative or a positive result that can be compared with the fetal outcome assessment. The present retrospective study included a set of 2124 one-hour antenatal FHR tracings derived from 333 patients, recorded between 24 and 44 weeks of gestation (mean gestational age: 36 weeks). Various approaches for the research data analysis, depending on the method of interpretation of the individual patient-tracing relation, were used in the investigation. The quality of the fuzzy analysis was defined by the number of correct classifications (CC) and the additional index QI – the geometric mean of the sensitivity and specificity values. Results: The effectiveness of the fetal assessment varied, depending on the assumed relation between a patient and a set of her tracings. The approach, based on a common assessment of the whole set of tracings recorded for a single patient, provided the highest quality of automated classification. The best results (CC = 70.9% and QI = 84.0%) confirmed the possibility of predicting the neonatal outcome using the proposed fuzzy system based on the FIGO guidelines. Conclusions: It is possible to enhance the process of the fetal condition assessment with classification of the FHR records through the implementation of the heuristic rules of inference in the fuzzy signal processing algorithms.Cel pracy: Monitorowanie płodu na podstawie analizy sygnału czynności serca płodu (FHR) jest najczęściej stosowaną metodą biofizycznej oceny stanu płodu w czasie ciąży i porodu. Wzrokowa analiza krzywej FHR jest trudna z uwagi na jej złożony kształt. Z tego względu, komputerowo-wspomagane systemy monitorowania stanu płodu dostarczają szeregu parametrów będących rezultatem ilościowej analizy rejestrowanego sygnału. Parametry te są podstawą dla jakościowej oceny stanu płodu. Do najczęściej stosowanych wytycznych, określających sposób interpretacji sygnału FHR należą kryteria określone przez FIGO. Na ich podstawie skonstruowano ważony rozmyty system punktowy, którego zadaniem jest określenie stanu płodu na podstawie kryteriów oceny, jakimi posługuje się ekspert kliniczny. W pracy przedstawiono badania nad zgodnością rozmytej klasyfikacji z oceną stanu płodu wyznaczaną na podstawie punktacji Apgar. Materiał i metody: Proponowany system do automatycznej, rozmytej klasyfikacji stanowi rozwinięcie idei skal punktowych wykorzystywanych do jakościowej oceny zapisów czynności serca płodu. Za pomocą jednej reguły rozmytej modelowana jest zasada oceny pojedynczego parametru opisu ilościowego sygnału FHR zgodnie z wytycznymi FIGO. Wejściami systemu rozmytego są wartości parametrów ilościowych sygnału FHR, a stan wyjścia, wyznaczany w procesie wnioskowania rozmytego, definiuje interpretację zapisu. Proces rozmytej oceny sygnału jest rodzajem testu diagnostycznego, którego wynik, negatywny lub pozytywny, można porównać z oceną stanu urodzeniowego noworodka. Badaniem retrospektywnym objęto zbiór 2124 godzinnych zapisów ciążowych pochodzących od 333 pacjentek, zarejestrowanych pomiędzy 24 a 44 tygodniem ciąży (średni wiek ciążowy to 36 tygodni). W eksperymentach zastosowano różne konstrukcje zbiorów danych, w zależności od przyjętego sposobu interpretacji zbioru sygnałów zarejestrowanych dla pojedynczej pacjentki. Jakość rozmytej analizy automatycznej oceniano na podstawie liczby poprawnych klasyfikacji CC oraz wskaźnika QI będącego średnią geometryczną czułości oraz swoistości. Wyniki: W zależności od przyjętej metody analizy zbioru danych otrzymano różną skuteczność oceny stanu płodu. Podejście, w którym określano jedną wspólną ocenę dla całego zbioru zapisów zarejestrowanych dla pojedynczej pacjentki, pozwoliło na uzyskanie najwyższej jakości automatycznej klasyfikacji. Najlepsze z uzyskanych wyników (CC = 70.9% i QI = 84.0%) potwierdzają możliwość predykcji stanu urodzeniowego płodu na podstawie rozmytego wnioskowania opartego na wytycznych FIGO. Wnioski: Istnieje możliwość wspomagania procesu diagnostyki stanu płodu przez zastosowanie systemu rozmytej klasyfikacji sygnałów FHR, opartego o heurystyczne reguły wnioskowania właściwe doświadczonemu klinicyście

Challenges of developing robust AI for intrapartum fetal heart rate monitoring

Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation. Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made. Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

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