Jiří Spilka

Open access intrapartum CTG database

BackgroundCardiotocography (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.DescriptionThe 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.ConclusionA 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.

Using nonlinear features for fetal heart rate classification

Highlights • We analyzed fetal heart rate of normal and acidemic fetuses. • We used conventional and nonlinear features for the signal analysis. • Addition of nonlinear features improves accuracy of classification. • The best nonlinear features are: Lempel Ziv complexity and Sample entropy. • Combination of conventional and nonlinear features provides the best accuracy. Abstract Fetal heart rate (FHR) is used to evaluate fetal well-being and enables clinicians to detect ongoing hypoxia during delivery. Routine clinical evaluation of intrapartum FHR is based on macroscopic morphological features visible to the naked eye. In this paper we evaluated conventional features and compared them to the nonlinear ones in the task of intrapartum FHR classification. The experiments were performed using a database of 217 FHR records with objective annotations, i.e. pH measurement. We have proven that the addition of nonlinear features improves accuracy of classification. The best classification results were achieved using a combination of conventional and nonlinear features with sensitivity of 73.4%, specificity of 76.3%, and F -measure of 71.9%. The best selected nonlinear features were: Lempel Ziv complexity, Sample entropy, and fractal dimension estimated by Higuchi method. Since the results of automatic signal evaluation are easily reproducible, the process of FHR evaluation can become more objective and may enable clinicians to focus on additional non-cardiotocography parameters influencing the fetus during delivery.

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 Higuchis fractal dimension are among the top five features.

Fractal Analysis and Hurst Parameter for Intrapartum Fetal Heart Rate Variability Analysis: A Versatile Alternative to Frequency Bands and LF/HF Ratio

Background The fetal heart rate (FHR) is commonly monitored during labor to detect early fetal acidosis. FHR variability is traditionally investigated using Fourier transform, often with adult predefined frequency band powers and the corresponding LF/HF ratio. However, fetal conditions differ from adults and modify spectrum repartition along frequencies. Aims This study questions the arbitrariness definition and relevance of the frequency band splitting procedure, and thus of the calculation of the underlying LF/HF ratio, as efficient tools for characterizing intrapartum FHR variability. Study Design The last 30 minutes before delivery of the intrapartum FHR were analyzed. Subjects Case-control study. A total of 45 singletons divided into two groups based on umbilical cord arterial pH: the Index group with pH ≤ 7.05 (n = 15) and Control group with pH > 7.05 (n = 30). Outcome Measures Frequency band-based LF/HF ratio and Hurst parameter. Results This study shows that the intrapartum FHR is characterized by fractal temporal dynamics and promotes the Hurst parameter as a potential marker of fetal acidosis. This parameter preserves the intuition of a power frequency balance, while avoiding the frequency band splitting procedure and thus the arbitrary choice of a frequency separating bands. The study also shows that extending the frequency range covered by the adult-based bands to higher and lower frequencies permits the Hurst parameter to achieve better performance for identifying fetal acidosis. Conclusions The Hurst parameter provides a robust and versatile tool for quantifying FHR variability, yields better acidosis detection performance compared to the LF/HF ratio, and avoids arbitrariness in spectral band splitting and definitions.

Automatic Evaluation of FHR Recordings from CTU-UHB CTG Database

Fetal heart rate (FHR) provides information about fetal well-being during labor. The FHR is usually the sole direct information channel from the fetus – undergoing the stress of labor – to the clinician who tries to detect possible ongoing hypoxia. For this paper, new CTU-UHB CTG database was used to compute more than 50 features. Features came from different domains ranging from classical morphological features based on FIGO guidelines to frequency-domain and non-linear features. Features were selected using the RELIEF (RELevance In Estimating Features) technique, and classified after applying Synthetic Minority Oversampling Technique (SMOTE) to the pathological class of the data. Nearest mean classifier with adaboost was used to obtain the final results. In results section besides the direct outcome of classification the top ten ranked features are presented.

Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization

The most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractions-the cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natural ordering, and representation of increased severity, for obtaining the final results. The results are promising suggesting that more effort should be put into this proposed approach.

Investigating pH based evaluation of fetal heart rate (FHR) recordings

Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms.

Intrapartum Fetal Heart Rate Classification: Cross-Database Evaluation

Fetal Heart Rate (FHR) provides obstetricians with essential information about fetal reactions to stress events during delivery. Early detection of fetal acidosis, enabling timely interventions and prevention of adverse consequences of acidosis for fetuses, remains a challenging task. In particular, the use of different, proprietary and small databases in various published works hinders meaningful comparisons of achieved results. This work relies on the the use of two independent databases in order to asses relevantly acidosis detection performance and to address important issues of knowledge transfer (features, classification model) from one database to the other. Using a large set of features, supervised classification is performed with state-of-the-art sparse support vector machines. It shows that selected features and classification performance are consistent for both databases. Further it quantifies the level of generalization of the achieved results, by making use of one database for learning and the other one for testing.

Electronic Delivery Book: Structured Database Enables Analysis of Perinatal Risk Factors

The Electronic Delivery Book (EDB), an electronic information system, was developed in cooperation with obstetricians, midwives, and neonatologists from the University Hospital in Brno. The main aim was to create structured electronic documentation of selected delivery-related parameters based on the existing paper-based documentation. The system contains information from the different stages of delivery: parameters of the pregnancy, medications/interventions during the birth, outcome measures for the newborn(s), and primary attributes from neonatology. The EDB also allows creating overviews and basic statistics for everyday clinical needs and offers structured data for retrospective as well as prospective studies. One of the first results based on data collected using the EDB was the analysis aimed at identification of potential risk factors for low umbilical cord artery pH in term, singleton pregnancies. The data selected from EDB represents a basis for the retrospective case-control study. Cases were deliveries characterized by umbilical cord artery pH ≤7.05, controls were with no sign of hypoxia. In the database of 10637 deliveries, collected between 2014 and 2015 at the University Hospital in Brno, we identified 99 cases. Univariate analysis of clinical features was performed. The following risk factors were associated with low pH: the length of the first stage (odds ratio (OR) 1.40; 95% CI 1.04–1.89) and the length of the second stage of labor (OR 2.86; 1.70–4.81), primipara (OR 2.99; 1.90–4.71) and meconium stained fluid (OR 1.60; 1.07–2.38).

Automatic classification of intrapartal fetal heart-rate recordings: can it compete with experts?

Fetal heart rate (fHR) is used to evaluate the fetal well-being during the delivery. It provides information of fetal status and allows doctors to detect ongoing hypoxia. Routine clinical evaluation of intrapartal fHR is based on description of macroscopic morphological features of its baseline. In this paper we show, that by using additional features for description of the fHR recordings, we can improve the classification accuracy. Additionally since results of automatic signal evaluation are easily reproducible we can objectify the whole process, thus enabling us to focus on the underlying reasons for high expert inter-observer and intra-observer variability.