标题： 从睡眠分期到纺锤体检测：评估端到端自动睡眠分析 显示英文标题

标题： From Sleep Staging to Spindle Detection: Evaluating End-to-End Automated Sleep Analysis

摘要： 自动化睡眠分析，包括宏观结构（睡眠阶段）和微观结构（例如，睡眠纺锤体）元素，有望实现大规模的睡眠研究，并减少由于评分者之间不一致导致的方差。 虽然诸如睡眠阶段划分和纺锤体检测等单独步骤已经被分别研究过，但自动化多步骤睡眠分析的可行性仍然不清楚。 在此，我们评估是否可以使用最先进的睡眠阶段划分机器学习模型（RobustSleepNet）和随后的纺锤体检测模型（SUMOv2）进行完全自动化的分析，以复制基于专家的研究中双相障碍的研究结果。 自动化分析定性地再现了基于专家研究的关键发现，包括双相障碍患者与健康对照组之间快速纺锤体密度的显著差异，仅用几分钟就完成了以前需要几个月才能手动完成的工作。 尽管自动化分析的结果在定量上与基于专家的研究有所不同，这可能是由于专家评分者之间或评分者与模型之间的偏差，但模型在睡眠阶段划分和纺锤体检测方面各自的表现达到了或超过了评分者之间的一致性水平。 我们的结果表明，完全自动化的方法有潜力促进大规模的睡眠研究。 我们通过分享我们的代码并引入SomnoBot——一个保护隐私的睡眠分析平台，来提供对自动化分析所用工具的公开访问。 显示英文摘要

摘要： Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater incongruencies. While individual steps, such as sleep staging and spindle detection, have been studied separately, the feasibility of automating multi-step sleep analysis remains unclear. Here, we evaluate whether a fully automated analysis using state-of-the-art machine learning models for sleep staging (RobustSleepNet) and subsequent spindle detection (SUMOv2) can replicate findings from an expert-based study of bipolar disorder. The automated analysis qualitatively reproduced key findings from the expert-based study, including significant differences in fast spindle densities between bipolar patients and healthy controls, accomplishing in minutes what previously took months to complete manually. While the results of the automated analysis differed quantitatively from the expert-based study, possibly due to biases between expert raters or between raters and the models, the models individually performed at or above inter-rater agreement for both sleep staging and spindle detection. Our results demonstrate that fully automated approaches have the potential to facilitate large-scale sleep research. We are providing public access to the tools used in our automated analysis by sharing our code and introducing SomnoBot, a privacy-preserving sleep analysis platform.