  In this work, we leverage machine learning techniques to identify potential
biomarkers of oxygen desaturation during sleep exclusively from
electroencephalogram (EEG) signals in pediatric patients with sleep apnea.
Development of a machine learning technique which can successfully identify EEG
signals from patients with sleep apnea as well as identify latent EEG signals
which come from subjects who experience oxygen desaturations but do not
themselves occur during oxygen desaturation events would provide a strong step
towards developing a brain-based biomarker for sleep apnea in order to aid with
easier diagnosis of this disease. We leverage a large corpus of data, and show
that machine learning enables us to classify EEG signals as occurring during
oxygen desaturations or not occurring during oxygen desaturations with an
average 66.8% balanced accuracy. We furthermore investigate the ability of
machine learning models to identify subjects who experience oxygen
desaturations from EEG data that does not occur during oxygen desaturations. We
conclude that there is a potential biomarker for oxygen desaturation in EEG
data.
