Determination of the site of pharyngeal collapse in Obstructive Sleep Apnea patients from snoring sounds

摘要 阻塞性睡眠呼吸暂停（OSA）是一种流行的疾病，由于缺乏良好的治疗， 持续气道正压通气（CPAP）。一些研究表明，咽部 在患有OSA的患者中，气道可在不同部位（帆，口咽侧壁，舌根， 和/或会厌）。此外，许多用于OSA的非CPAP治疗已被证明对 比其他地方更容易坍塌问题是，塌陷的位置需要昂贵的或侵入性的 药物诱导睡眠内窥镜检查（DISE）。这项建议旨在解决这个问题， 根据打鼾声音的声学分析确定塌陷部位。 在目标1中，将使用DISE开发用于预测塌陷位置的模型，同时 测量了800个人的打鼾声音。在目标2中，将在一个子集中进行自然睡眠内窥镜检查 目标1中的人确认倒塌的结构在自然睡眠中产生与 药物引起的睡眠最后，目标3将测试在家庭环境中测量打鼾声音的能力。 将在自然睡眠内窥镜检查和在家自然睡眠的打鼾之间进行比较（在 同一个人）。此外，打鼾将在不同的夜晚在家中进行测量，以测试可重复性 穿越黑夜 这项研究预计将产生一种算法，用于估计咽部的位置， 因为打鼾而倒下该算法可以改善非CPAP治疗患者的选择 并可能增加有效治疗OSA的患者数量。

ABSTRACT Obstructive Sleep Apnea (OSA) is a prevalent disorder that remains undertreated due to poor adherence with continuous positive airway pressure (CPAP). Several studies suggest that the pharyngeal airway can collapse at different sites in patients with OSA (velum, oropharyngeal lateral walls, tongue base, and/or epiglottis). Furthermore, many of the non-CPAP treatments for OSA have been shown to work better for some sites of collapse than others. The problem is, the site of collapse requires expensive or invasive procedures, such as drug-induced sleep endoscopy (DISE). This proposal seeks to solve that problem by determining the site of collapse from acoustic analysis of snoring sounds. In Aim 1, a model for predicting the site of collapse will be developed using DISE with simultaneously measured snoring sounds in 800 individuals. In Aim 2, natural sleep endoscopy will be performed in a subset of people from Aim 1 to confirm that the collapsing structure(s) produce the same sound in natural sleep as drug-induced sleep. Finally, Aim 3 will test the ability to measure snoring sounds in the home environment. Comparisons will be made between snores from natural sleep endoscopy and natural sleep at home (in the same individual). Additionally, snoring will be measured at home on separate nights to test reproducibility across nights. The studies in this grant are expected to produce an algorithm for estimating the site of pharyngeal collapse from snoring sounds. This algorithm could improve the selection of patients for non-CPAP therapies and potentially increase the number of patients effectively treated for OSA.