AUTHOR=Gold Nathan , Herry Christophe L. , Wang Xiaogang , Frasch Martin G. 

TITLE=Fetal Cardiovascular Decompensation During Labor Predicted From the Individual Heart Rate Tracing: A Machine Learning Approach in Near-Term Fetal Sheep Model

JOURNAL=Frontiers in Pediatrics

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2021.593889

DOI=10.3389/fped.2021.593889

ISSN=2296-2360

ABSTRACT=We present a novel computerized fetal heart rate intrapartum algorithm for early and individualized prediction of fetal cardiovascular decompensation, a key event in the causal chain leading to brain injury. This real-time machine learning algorithm performs well on noisy fetal heart rate data and requires ~2 hours to train on the individual fetal heart rate tracings in the first stage of labor; once trained, the algorithm predicts the event of fetal cardiovascular decompensation with 92% sensitivity. We show that the algorithm's performance suffers reducing sensitivity to 50% when the fetal heart rate is acquired at the sampling rate of 4 Hz used in ultrasound cardiotocographic monitors compared to the electrocardiogram(ECG)-derived signals as can be acquired from maternal abdominal ECG.