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.

翻译：传统睡眠分期将睡眠与清醒状态划分为五个粗粒度类别，忽略了各阶段内部的细微变化。该方法提供的觉醒概率信息有限，可能阻碍失眠等睡眠障碍的诊断。为解决这一问题，我们提出一种基于现有睡眠分期标签的深度学习方法，用于自动且可扩展的睡眠深度指数标注。该方法通过四个大规模队列中超过一万份多导睡眠图记录进行验证。结果显示睡眠深度指数的降低与觉醒概率的升高呈强相关性。多项案例研究表明，与传统睡眠分期相比，睡眠深度指数能捕捉更精细的睡眠结构。从整夜睡眠深度指数中提取的睡眠生物标志物，在不同主观睡眠质量与失眠症状的组别间表现出具有中等到大效应量的统计学显著差异。这些睡眠生物标志物在预测阻塞性睡眠呼吸暂停严重程度方面也展现出应用潜力，尤其适用于重症病例。本研究证实了所提连续睡眠深度标注方法的实用性，该方法能够揭示整夜睡眠中更精细的结构与动态特征，并产生对睡眠健康有益的新型数字生物标志物。