  Traditional sleep staging categorizes sleep and wakefulness into five
coarse-grained classes, overlooking subtle variations within each stage. It
provides limited information about the probability of arousal and may hinder
the diagnosis of sleep disorders, such as insomnia. To address this issue, we
propose a deep-learning method for automatic and scalable annotation of sleep
depth index using existing sleep staging labels. Our approach is validated
using polysomnography from over ten thousand recordings across four large-scale
cohorts. The results show a strong correlation between the decrease in sleep
depth index and the increase in arousal likelihood. Several case studies
indicate that the sleep depth index captures more nuanced sleep structures than
conventional sleep staging. Sleep biomarkers extracted from the whole-night
sleep depth index exhibit statistically significant differences with
medium-to-large effect sizes across groups of varied subjective sleep quality
and insomnia symptoms. These sleep biomarkers also promise utility in
predicting the severity of obstructive sleep apnea, particularly in severe
cases. Our study underscores the utility of the proposed method for continuous
sleep depth annotation, which could reveal more detailed structures and
dynamics within whole-night sleep and yield novel digital biomarkers beneficial
for sleep health.
