Obstructive Sleep Apnea (OSA) is a breathing disorder during sleep that affects millions of people worldwide. The diagnosis of OSA often occurs through an overnight polysomnogram (PSG) sleep study that generates a massive amount of physiological data. However, despite the evidence of substantial heterogeneity in the expression and symptoms of OSA, diagnosis and scientific analysis of severity typically focus on a single summary statistic, the Apnea-Hypopnea Index (AHI). We address the limitations of this approach through hierarchical Bayesian modeling of PSG data. Our approach produces interpretable random effects for each patient, which govern sleep-stage dynamics, rates of OSA events, and impacts of OSA events on subsequent sleep-stage dynamics. We propose a novel approach for using these random effects to produce a Bayes optimal clustering of patients. We use the proposed approach to analyze data from the APPLES study. Our analysis produces clinically interesting groups of patients with sleep apnea and a novel finding of an association between OSA expression and cognitive performance that is missed by an AHI-based analysis.

翻译：阻塞性睡眠呼吸暂停（OSA）是一种影响全球数百万人的睡眠呼吸障碍。OSA的诊断通常通过夜间多导睡眠图（PSG）睡眠研究进行，该研究会产生海量的生理数据。然而，尽管有证据表明OSA的表现形式和症状存在显著异质性，其严重程度的诊断与科学分析通常仅聚焦于单一汇总统计量——呼吸暂停低通气指数（AHI）。本研究通过构建PSG数据的层次贝叶斯模型来解决这一方法的局限性。我们的方法为每位患者生成可解释的随机效应，这些效应控制着睡眠阶段动态、OSA事件发生率以及OSA事件对后续睡眠阶段动态的影响。我们提出了一种利用这些随机效应生成患者贝叶斯最优聚类的新方法。我们应用所提出的方法分析了APPLES研究的数据。我们的分析产生了具有临床意义的睡眠呼吸暂停患者分组，并发现了一个基于AHI的分析所忽略的新现象：OSA表现形式与认知表现之间存在关联。