Development and Applications of a Novel Acoustic Model for Respiratory System

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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.