Real-time prediction of disordered breathing events in people with obstructive sleep apnea

Purpose Conventional therapies for obstructive sleep apnea (OSA) are effective but suffer from poor patient adherence and may not fully alleviate major OSA-associated cardiovascular risk factors or improve certain aspects of quality of life. Predicting the onset of disordered breathing events in OSA patients may lead to improved strategies for treating OSA and inform our understanding of underlying disease mechanisms. In this work, we describe a deployable system capable of performing real-time predictions of sleep disordered breathing events in patients diagnosed with OSA, providing a novel approach for gaining insight into OSA pathophysiology, discovering population subgroups, and improving therapies.