Fetal heart rate responses in chronic hypoxaemia with superimposed repeated hypoxaemia consistent with early labour: a controlled study in fetal sheep

An Update of Our Understanding of Fetal Heart Rate Patterns in Health and Disease

UNDERSTANDING FETAL HEART RATE PATTERNS THAT MAY PREDICT ANTENATAL AND INTRAPARTUM NEURAL INJURY: Christopher A. Lear, Jenny A. Westgate, Austin Ugwumadu, Jan G. Nijhuis, Peter R. Stone, Antoniya Georgieva, Tomoaki Ikeda, Guido Wassink , Laura Bennet , Alistair J. Gunn Seminars in Pediatric Neurology Volume 28, December 2018, Pages 3-16 Electronic fetal heart rate (FHR) monitoring is widely used to assess fetal well-being throughout pregnancy and labor. Both antenatal and intrapartum FHR monitoring are associated with a high negative predictive value and a very poor positive predictive value. This in part reflects the physiological resilience of the healthy fetus and the remarkable effectiveness of fetal adaptations to even severe challenges. In this way, the majority of "abnormal" FHR patterns in fact reflect a fetus' appropriate adaptive responses to adverse in utero conditions. Understanding the physiology of these adaptations, how they are reflected in the FHR trace and in what conditions they can fail is therefore critical to appreciating both the potential uses and limitations of electronic FHR monitoring.

Prediction of Fetal Blood Pressure during Labour with Deep Learning Techniques

Our objective is to develop a model for the prediction of minimum fetal blood pressure (FBP) during fetal heart rate (FHR) decelerations. Experimental data from umbilical occlusions in near-term fetal sheep (2698 occlusions from 57 near-term lambs) were used to train a convolutional neural network. This model was then used to estimate FBP for decelerations extracted from the final 90 min of 53,445 human FHR signals collected using cardiotocography. Minimum sheep FBP was predicted with a mean absolute error of 6.7 mmHg (25th, 50th, 75th percentiles of 2.3, 5.2, 9.7 mmHg), mean absolute percentage errors of 17.3% (5.5%, 12.5%, 23.9%) and a coefficient of determination R2=0.36. While the model was unable to clearly predict severe compromise at birth in humans, there is positive evidence that such a model could predict human FBP with further development. The neural network is capable of predicting FBP for many of the sheep decelerations accurately but performed far from satisfactory at identifying FHR segments that correspond to the highest or lowest minimum FBP. These results indicate that with further work and a larger, more variable training dataset, the model could achieve higher accuracy.