Automated Identification and Quantification of Sleep Disordered Breathing Using Non-Invasive Cardiopulmonary Signals

Sleep disordered breathing, including sleep apnea, is a common condition with significant impacts on quality of life and societal costs. The NYU Sleep Research Laboratory is focusing on improving the diagnosis of mild sleep disordered breathing, also known as upper airway resistance syndrome. They've developed a non-invasive method to detect increased airway resistance from the airflow signal, which shows a characteristic change in the inspiratory flow contour, indicating flow limitation. This research aims to enhance the diagnosis and treatment of this condition, potentially improving care and addressing current challenges. Currently, all respiratory events are manually identified and counted, which is time-consuming and prone to variability. This project aims to improve this process by creating an artificially intelligent system to identify and quantify sleep disordered breathing using non-invasive cardiopulmonary signals collected during a routine sleep study. The system will also evaluate the usefulness of other reported indices of sleep disordered breathing. The successful development of this automated system could simplify, standardize, and improve the diagnosis of sleep disordered breathing, while also aiding research and clinical work in this area.