Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. It provides limited information about the duration of arousal and may hinder research on sleep fragmentation and relevant sleep disorders. To address this issue, we propose a deep learning method for automatic and scalable annotation of continuous sleep depth index (SDI) using existing discrete sleep staging labels. Our approach was validated 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. Specific case studies indicated that the sleep depth index captured more nuanced sleep structures than conventional sleep staging. Gaussian mixture models based on the digital biomarkers extracted from the sleep depth index identified two subtypes of sleep, where participants in the disturbed sleep group had a higher prevalence of sleep apnea, insomnia, poor subjective sleep quality, hypertension, and cardiovascular disease. The disturbed subtype was associated with a 42% (hazard ratio 1.42, 95% CI 1.24-1.62) increased risk of mortality and a 29% (hazard ratio 1.29, 95% CI 1.00-1.67) increased risk of fatal cardiovascular disease. Our study underscores the utility of the proposed method for continuous sleep depth annotation, which could reveal more detailed information about the sleep structure and yield novel digital biomarkers for routine clinical use in sleep medicine.

翻译：传统的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。该方法提供的觉醒时长信息有限，可能阻碍对睡眠碎片化及相关睡眠障碍的研究。为解决这一问题，我们提出一种深度学习方法，利用现有离散睡眠分期标签实现连续睡眠深度指数的自动可扩展标注。该方法通过四个大规模队列中超过10,000份多导睡眠图记录进行了验证。结果显示睡眠深度指数的降低与觉醒时长的增加存在强相关性。具体案例研究表明，睡眠深度指数比传统睡眠分期能捕捉更精细的睡眠结构。基于睡眠深度指数提取的数字生物标志物构建的高斯混合模型识别出两种睡眠亚型，其中睡眠紊乱亚型参与者具有更高的睡眠呼吸暂停、失眠、主观睡眠质量差、高血压及心血管疾病患病率。紊乱亚型与死亡风险增加42%（风险比1.42，95%置信区间1.24-1.62）及致命性心血管疾病风险增加29%（风险比1.29，95%置信区间1.00-1.67）相关。本研究证实了所提出的连续睡眠深度标注方法的实用价值，该方法能够揭示更精细的睡眠结构信息，并为睡眠医学的常规临床应用提供新型数字生物标志物。