Continuous sleep depth index annotation with deep learning yields novel digital biomarkers for sleep health

Abstract

Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. We propose a deep learning method to annotate continuous sleep depth index (SDI) with existing discrete sleep staging labels, using polysomnography from over 10,000 recordings across four large-scale cohorts. The results showcased a strong correlation between the decrease in sleep depth index and the increase in duration of arousal. Case studies indicated that SDI captured more nuanced sleep structures than conventional sleep staging. Clustering based on the digital biomarkers extracted from the SDI identified two subtypes of sleep, where participants in the disturbed subtype had a higher prevalence of several poor health conditions and were associated with a 33% increased risk of mortality and a 38% increased risk of fatal coronary heart disease. Our study underscores the utility of SDI in revealing more detailed sleep structures and yielding novel digital biomarkers for sleep medicine.