Commentary on 'Antenatal cardiotocogram quality and interpretation using computers'

Cardiotocography analysis by empirical dynamic modeling and Gaussian processes

Introduction: During labor, fetal heart rate (FHR) and uterine activity (UA) can be continuously monitored using Cardiotocography (CTG). This is the most widely adopted approach for electronic fetal monitoring in hospitals. Both FHR and UA recordings are evaluated by obstetricians for assessing fetal well-being. Due to the complex and noisy nature of these recordings, the evaluation by obstetricians suffers from high interobserver and intraobserver variability. Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. Methods: Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. In this paper, we propose to model intrapartum CTG recordings from a dynamical system perspective using empirical dynamic modeling with Gaussian processes, which is a Bayesian nonparametric approach for estimation of functions. Results and Discussion: In the context of our paper, Gaussian processes are capable for simultaneous estimation of the dimensionality of attractor manifolds and reconstructing of attractor manifolds from time series data. This capacity of Gaussian processes allows for revealing causal relationships between the studied time series. Experimental results on real CTG recordings show that FHR and UA signals are causally related. More importantly, this causal relationship and estimated attractor manifolds can be exploited for several important applications in computerized analysis of CTG recordings including estimating missing FHR samples, recovering burst errors in FHR tracings and characterizing the interactions between FHR and UA signals.

EXTRACTING INTERPRETABLE FEATURES FOR FETAL HEART RATE RECORDINGS WITH GAUSSIAN PROCESSES

During labor, fetal heart rate (FHR) and uterine activity (UA) are continuously monitored with Cardiotocography (CTG). The FHR and UA signals are visually inspected by obstetricians to assess the fetal well-being. However, due to the subjectivity of the visual inspection, the evaluations of CTG recordings performed by obstetricians have high inter- and intra-variability. The computerized analysis of FHR relies on features either hand-crafted by experts or automatically learned by machine learning methods. However, the popular interpretable FHR features, in general, have low correlation with the pH value of the umbilical cord blood at birth, which is the current gold standard for labeling FHRs in the computerized analysis of FHRs. The features found by machine learning methods, by contrast, usually have limited interpretability. In this paper, in a follow up of our previous work on FHR analysis using Gaussian processes (GPs), we explore the possibility of using the hyperparameters of GPs as interpretable features. Our results indicate that some GP features achieve high correlation with the pH values, while at the same time they are not highly correlated with other popular features.

INFERENCE ABOUT CAUSALITY FROM CARDIOTOCOGRAPHY SIGNALS USING GAUSSIAN PROCESSES

In this paper, we propose a novel and simple method for discovery of Granger causality from noisy time series using Gaussian processes. More specifically, we adopt the concept of Granger causality, but instead of using autoregressive models for establishing it, we work with Gaussian processes. We show that information about the Granger causality is encoded in the hyper-parameters of the used Gaussian processes. The proposed approach is first validated on simulated data, and then used for understanding the interaction between fetal heart rate and uterine activity in the last two hours before delivery and of interest in obstetrics. Our results indicate that uterine activity affects fetal heart rate, which agrees with recent clinical studies.

Fetal heart rate monitoring of short term variation (STV): a methodological observational study

BACKGROUND

Cardiotocography (CTG) has high sensitivity, but less specificity in detection of fetal hypoxia. There is need for adjunctive methods easy to apply during labor. Low fetal heart rate short term variation (STV) is predictive for hypoxia during the antenatal period. The objectives of our study were to methodologically evaluate monitoring of STV during labor and to compare two different monitors (Sonicaid™ and EDAN™) for antenatal use.

METHODS

A prospective observational study at the obstetric department, Karolinska University hospital, Stockholm (between September 2011 and April 2015). In 100 women of ≥ 36 weeks gestation, STV values were calculated during active labor. In a subset of 20 women we compared STV values between internal and external signal acquisition. Additionally we compared antenatal monitoring with two different monitors in another 20 women.

RESULTS

Median STV in 100 fetuses monitored with scalp electrode during labor (EDAN™) was 7.1 msec (range 1.3-25.9) with no difference between early (3-6 cm) and late (7-10 cm) labor (7.1 vs 6.8 msec; p = 0.80). STV calculated from scalp electrode signals were positively correlated with delta-STV (STV internal -external) (R = 0.70; p < 0.01). No significant differences were found between Sonicaid™ and EDAN™ in antenatal external monitoring of STV (median difference 0.9 msec, Spearman Rank Correlation Sonicaid vs delta-STV; R = 0.35; p = 0.14).

CONCLUSIONS

Median intrapartum STV was 7.1 msec. Significant differences were found between internal and external signal acquisition, a finding that suggests further intrapartum studies to be analysed separately depending upon type of signal acquisition. Antenatal external monitoring with Sonicaid™ and EDAN™ indicates that the devices perform equally well in the identification of acidemic fetuses. Further studies are needed to assess the clinical value of intrapartum STV.