Pediatric Sleep Scoring In-the-wild from Millions of Multi-channel EEG Signals

• URL: http://arxiv.org/abs/2207.06921v1
• Date: Thu, 30 Jun 2022 17:44:16 GMT
• Title: Pediatric Sleep Scoring In-the-wild from Millions of Multi-channel EEG Signals
• Authors: Harlin Lee, Aaqib Saeed
• Abstract summary: We present the first automated pediatric sleep scoring results on a recent large-scale sleep study dataset. 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 78% overall accuracy.
• Score: 4.505321919386738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 78% overall accuracy. Further, we conduct an in-depth analysis of the model performance based on patient demographics and EEG channels.

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