Development and Applications of a Novel Acoustic Model for Respiratory System

呼吸是由一个高度复杂和动态的系统控制的。然而，目前还没有一种方便且无创的技术来评估呼吸系统的动态变化。呼吸声是由空气通过咽和其他气道产生的，但没有简单的、动态的咽声学模型来研究呼吸声的产生机制，以及如何利用呼吸声分析来监测咽解剖和生理的变化。此外，呼吸相关的数据，如呼吸频率、强度和呼吸声音的频谱特征可以代表呼吸系统的特征。然而，目前可用的呼吸信号分析算法在很大程度上局限于检测呼吸速率，这并没有纳入呼吸信号产生的动态变化。因此，有必要开发生理驱动的数据挖掘算法来分析呼吸信号，以检查呼吸系统及其随时间的动态变化。***这个发现基金项目的重点将首先开发新的声学算法来评估呼吸系统动力学，并训练HQP具有信号处理、物理学、流体动力学和生理学的特定知识。Aim 1本身就是一种新颖的方法——它将产生第一个使用呼吸音来估计咽狭窄、咽塌陷的潜在部位以及咽组织组成和硬度的模型。通过该方法获得的知识可用于对睡眠呼吸暂停患者进行表型分析，预测每个患者的最佳治疗方案，并制定客观和针对患者的措施来评估不同治疗方法对睡眠呼吸暂停的疗效。目标2将开发第一个生理驱动的数据挖掘算法来监测、预测和潜在地预防呼吸系统的长期异常。呼吸系统异常会对个人健康、社会/行为表现和生活质量产生短期和长期的影响。例如，睡眠期间的呼吸障碍与车祸和与工作有关的事故的风险增加、抑郁、白天过度嗜睡和学校表现不佳有关。虽然本提案的具体目标是基于睡眠呼吸暂停，但这些算法可用于呼吸信号的广泛应用。这类应用可能包括监测呼吸能力以改善运动员的表现，防止工作场所因疲劳或困倦造成的风险和伤害，防止因疲劳造成的机动车碰撞，以及模拟唱歌过程中咽喉的动态变化，作为生物反馈来训练歌手并避免声带损伤。**

Breathing is controlled by a highly complex and dynamic system. However, there is no convenient and non-invasive technology to assess the dynamic changes in the respiratory system. Respiratory sounds are generated by the passage of air through pharynx and the rest of airways, but there is no simple and dynamic acoustic model of the pharynx in order to investigate the mechanisms of respiratory sound generation and how respiratory sounds analysis can be used to monitor variations in the pharyngeal anatomy and physiology. Furthermore, respiratory-related data such as breathing rate, intensity and spectral features of breathing sounds can represent characteristics of the respiratory system. However, currently available algorithms for analyzing respiratory signals are largely limited to examining respiratory rate, which does not incorporate the dynamic changes in the generation of respiratory signals. Therefore, there is a need to develop physiology-driven data mining algorithms for analysing respiratory signals to examine the respiratory system and its dynamic changes over time.***The focus of this Discovery Grant project will be first to develop novel acoustic algorithms to assess respiratory system dynamics and to train HQP with a specific knowledge of signal processing, physics, fluid dynamics, and physiology. Aim 1 is a novel approach in itself- it will yield the first model that uses respiratory sounds to estimate pharyngeal narrowing, potential sites of collapse in the pharynx, and pharyngeal tissue composition and stiffness. The knowledge gained by this method can be used to phenotype patients with sleep apnea, to predict the optimum treatment for every patient, and to develop objective and patient-specific measures to assess the efficacy of different treatments for sleep apnea. Aim 2 will develop the first physiology-driven data mining algorithm to monitor, predict, and potentially prevent long-term abnormalities of the respiratory system. Abnormalities in the respiratory system will have both short-term and long-term effects on the individual's health, social/behavioral performance, and quality of life. For example, breathing disorders during sleep are associated with increased risk of car and work-related accidents, depression, excessive daytime sleepiness, and poor performance at school. ***While the specific aims of this proposal are based on sleep apnea, these algorithms can be used in a wide range of applications for respiratory signals. Such application may include monitoring respiratory capacity to modify performance of athletes, preventing work place risks and injuries due to fatigue or sleepiness, preventing motor-vehicle collisions due to fatigue, and modeling the dynamic changes in the pharynx and larynx during singing as a biofeedback to train singers and to avoid vocal fold damage.**