School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, Brisbane, Australia.
Physiol Meas. 2013 Feb;34(2):99-121. doi: 10.1088/0967-3334/34/2/99. Epub 2013 Jan 23.
Obstructive sleep apnea (OSA) is a serious sleep disorder with high community prevalence. More than 80% of OSA suffers remain undiagnosed. Polysomnography (PSG) is the current reference standard used for OSA diagnosis. It is expensive, inconvenient and demands the extensive involvement of a sleep technologist. At present, a low cost, unattended, convenient OSA screening technique is an urgent requirement. Snoring is always almost associated with OSA and is one of the earliest nocturnal symptoms. With the onset of sleep, the upper airway undergoes both functional and structural changes, leading to spatially and temporally distributed sites conducive to snore sound (SS) generation. The goal of this paper is to investigate the possibility of developing a snore based multi-feature class OSA screening tool by integrating snore features that capture functional, structural, and spatio-temporal dependences of SS. In this paper, we focused our attention to the features in voiced parts of a snore, where quasi-repetitive packets of energy are visible. Individual snore feature classes were then optimized using logistic regression for optimum OSA diagnostic performance. Consequently, all feature classes were integrated and optimized to obtain optimum OSA classification sensitivity and specificity. We also augmented snore features with neck circumference, which is a one-time measurement readily available at no extra cost. The performance of the proposed method was evaluated using snore recordings from 86 subjects (51 males and 35 females). Data from each subject consisted of 6-8 h long sound recordings, made concurrently with routine PSG in a clinical sleep laboratory. Clinical diagnosis supported by standard PSG was used as the reference diagnosis to compare our results against. Our proposed techniques resulted in a sensitivity of 93±9% with specificity 93±9% for females and sensitivity of 92±6% with specificity 93±7% for males at an AHI decision threshold of 15 events/h. These results indicate that our method holds the potential as a tool for population screening of OSA in an unattended environment.
阻塞性睡眠呼吸暂停（OSA）是一种具有高社区患病率的严重睡眠障碍。超过 80%的 OSA 患者未被诊断。多导睡眠图（PSG）是目前用于 OSA 诊断的参考标准。它昂贵、不方便，需要睡眠技术人员的广泛参与。目前，需要一种低成本、无人值守、方便的 OSA 筛查技术。打鼾总是几乎与 OSA 相关，是最早的夜间症状之一。随着睡眠的开始，上呼吸道经历功能和结构的变化，导致有利于打鼾声音（SS）产生的空间和时间分布的部位。本文的目的是研究通过整合捕捉 SS 的功能、结构和时空依赖性的打鼾特征，开发基于打鼾的多特征类 OSA 筛查工具的可能性。在本文中，我们将注意力集中在打鼾的有声部分的特征上，在这些特征中可以看到准重复的能量包。然后使用逻辑回归对单个打鼾特征类进行优化，以获得最佳的 OSA 诊断性能。因此，所有特征类都进行了集成和优化，以获得最佳的 OSA 分类敏感性和特异性。我们还通过颈围增加了打鼾特征，颈围是一种一次性测量值，无需额外费用即可获得。使用来自 86 名受试者（51 名男性和 35 名女性）的打鼾记录来评估所提出方法的性能。每位受试者的数据由 6-8 小时长的录音组成，在临床睡眠实验室中与常规 PSG 同时进行。使用临床诊断（由标准 PSG 支持）作为参考诊断来比较我们的结果。我们提出的技术在 AHI 决策阈值为 15 事件/小时时，女性的敏感性为 93±9%，特异性为 93±9%，男性的敏感性为 92±6%，特异性为 93±7%。这些结果表明，我们的方法有可能成为在无人值守环境中进行 OSA 人群筛查的工具。
