Search

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.

ObjectiveTo compare the rapid bedside test for placental α microglobulin-1 with the instrumented fetal fibronectin test for prediction of imminent spontaneous preterm delivery among women with symptoms of preterm labor.MethodsWe conducted a prospective observational study on pregnant women with signs or symptoms suggestive of preterm labor between 24 and 35 weeks of gestation with intact membranes and cervical dilatation less than 3 cm. Participants were prospectively enrolled at 15 U.S. academic and community centers. Placental α microglobulin-1 samples did not require a speculum examination. Health care providers were blinded to placental α microglobulin-1 results. Fetal fibronectin samples were collected through speculum examination per manufacturer requirements and sent to a certified laboratory for testing using a cutoff of 50 ng/mL. The coprimary endpoints were positive predictive value (PPV) superiority and negative predictive value (NPV) noninferiority of placental α microglobulin-1 compared with fetal fibronectin for the prediction of spontaneous preterm birth within 7 days and within 14 days.ResultsOf 796 women included in the study cohort, 711 (89.3%) had both placental α microglobulin-1 and fetal fibronectin results and valid delivery outcomes available for analysis. The overall rate of preterm birth was 2.4% (17/711) within 7 days of testing and 4.2% (30/711) within 14 days of testing with respective rates of spontaneous preterm birth of 1.3% (9/703) and 2.9% (20/701). Fetal fibronectin was detected in 15.5% (110/711), and placental α microglobulin-1 was detected in 2.4% (17/711). The PPVs for spontaneous preterm delivery within 7 days or less among singleton gestations (n=13) for placental α microglobulin-1 and fetal fibronectin were 23.1% (3/13) and 4.3% (4/94), respectively (P