Fetal cardiovascular decompensation during labor predicted from the individual heart rate: a prospective study in fetal sheep near term and the impact of low sampling rate
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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 algorithm's performance suffers reducing sensitivity to 67 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.
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