  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 67% 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.
