Development and Applications of a Novel Acoustic Model for Respiratory System

新型呼吸系统声学模型的开发与应用

• 批准号: RGPIN-2016-06549
• 负责人: Yadollahi, Azadeh
• 金额: $ 2.99万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616381
• 关键词: Development; Applications; Novel; Acoustic; Model

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.

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、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部和其余气道产生的，但是没有咽部的简单且动态的声学模型来研究呼吸声产生的机制以及呼吸声分析如何用于监测咽部解剖学和生理学的变化。此外，呼吸相关的数据，例如呼吸频率、呼吸声的强度和频谱特征，可以表示呼吸系统的特性。然而，当前用于分析呼吸信号的可用算法在很大程度上限于检查呼吸速率，其不包括呼吸信号的生成中的动态变化。因此，需要开发生理驱动的数据挖掘算法，用于分析呼吸信号，以检查呼吸系统及其随时间的动态变化。 这个发现补助金项目的重点将首先开发新的声学算法，以评估呼吸系统动力学，并培训HQP与信号处理，物理学，流体动力学和生理学的具体知识。目标1本身是一种新方法-它将产生第一个使用呼吸声来估计咽部狭窄、咽部塌陷的潜在部位以及咽部组织组成和硬度的模型。通过这种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，预测每位患者的最佳治疗方案，并制定客观和患者特异性的措施，以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，以监测，预测和潜在地预防呼吸系统的长期异常。呼吸系统的异常会对个体的健康、社会/行为表现和生活质量产生短期和长期影响。例如，睡眠期间的呼吸障碍与汽车和工作相关事故的风险增加，抑郁症，白天过度嗜睡和学校表现不佳有关。 虽然该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。这种应用可以包括监测呼吸能力以改变运动员的表现，防止工作场所的风险和由于疲劳或困倦而导致的伤害，防止由于疲劳而导致的机动车碰撞，以及将歌唱期间咽和喉的动态变化建模为生物反馈以训练歌唱者并避免声带损伤。