  Sleep is particularly important to the health of infants, children, and
adolescents, and sleep scoring is the first step to accurate diagnosis and
treatment of potentially life-threatening conditions. But pediatric sleep is
severely under-researched compared to adult sleep in the context of machine
learning for health, and sleep scoring algorithms developed for adults usually
perform poorly on infants. Here, we present the first automated sleep scoring
results on a recent large-scale pediatric sleep study dataset that was
collected during standard clinical care. We develop a transformer-based model
that learns to classify five sleep stages from millions of multi-channel
electroencephalogram (EEG) sleep epochs with 78% overall accuracy. Further, we
conduct an in-depth analysis of the model performance based on patient
demographics and EEG channels. The results point to the growing need for
machine learning research on pediatric sleep.
