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Obstructive sleep apnea (OSA) is still an underdiagnosed common disorder. Undiagnosed OSA, in particular, increases the perioperative morbidity and mortality risks for OSA patients undergoing surgery requiring full anesthesia. OSA screening using the gold standard Polysomnography (PSG) is expensive and time-consuming. This proposal presents three research projects/points to apply advanced signal processing and machine learning techniques on breathing sounds signals for screening OSA disorder during wakefulness. This proposal will investigate the pathology of the OSA using breathing sounds analysis, correlate the signals with PSG parameters, and finally enhance the current OSA screening algorithm during wakefulness (AWakeOSA). The two main expected outcomes of this work will be a non-invasive methodology to understand the OSA disorder pathology using only breathing sounds, and enhancing the performance of the AWakeOSA algorithm as an objective, accurate, reliable, inexpensive, and quick OSA screening tool with a high classification power during wakefulness.
Engineering - biomedical
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