Pediatric Sleep Scoring In-the-wild from Millions of Multi-channel EEG Signals. (arXiv:2207.06921v1 [eess.SP])

Sleep is critical to the health and development of infants, children, and
adolescents, but pediatric sleep is severely under-researched compared to adult
sleep in the context of machine learning for health and well-being. Here, we
present the first automated pediatric sleep scoring results on a recent
large-scale sleep study dataset that was collected during standard clinical
care. We develop a transformer-based deep neural network model that learns to
classify five sleep stages from millions of multi-channel electroencephalogram
(EEG) signals with …

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