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Session: Respiration & Pregnancy
Session starts: Thursday 26 January, 10:30
Presentation starts: 11:45
Room: Room 531

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Ivar de Vries (Máxima Medisch Centrum, Eindhoven University of Technology)
Judith van Laar (Máxima Medisch Centrum, Eindhoven University of Technology)
Beatrijs van der Hout-van der Jagt (Máxima Medisch Centrum, Eindhoven University of Technology)
Rik Vullings (Eindhoven University of Technology)

Abstract:
Preterm labor (i.e. labor before 37 weeks of gestation) is a major cause of neonatal morbidity and mortality. Early detection of preterm birth allows for adequate treatment and could therefore improve neonatal outcomes. Such treatment might consist of the admission of medication to suppress labor and accelerate fetal lung maturation. Since these treatments are dependent on gestational age and have sensitive timing, clinical practice might benefit from a method for accurate detection of time to labor. Using time windows of the uterine electrical activity (electrohysterogram) during pregnancy, this work aims at developing a neural-network based method for the prediction of time until (spontaneous) labor. For this purpose, a mean-variance estimator is trained on features extracted from the electrohysterogram. Kernel-density estimation is used to recombine the predictions and generate a single probability distribution. Given the high incidence of induced labor (>50%), effort is made to include these cases with contaminated labels during training. The presented method will be evaluated as a screening tool for birth at different relevant gestational ages. We aim to apply this tool towards the end of the second trimester of pregnancy. Evaluated on a combination of the open-access Term-Preterm EHG and Icelandic 16-lead Electrohysterogram databases, a mean absolute error of 12.1 days, with an estimated standard deviation (i.e. uncertainty) of 12.3 days was found. This uncertainty in the model predictions is lowered to 10.4 days by using a custom loss, which allows us to include samples with induced labor during training.