UCLA

Obstructive sleep apnea (OSA) is the most common type of sleep apnea and the most prevalent sleep disorder in general. Approximately 22 million people in the United States and more than 100 million people worldwide are affected by this serious disorder. The disorder is characterized by repeated involuntary breathing cessations (apnea) or reductions in breathing (hypopnea) during sleep, accompanied by oxygen desaturation (hypoxia). The apneas occur due to the obstruction of the upper airway as a result of the relaxing of throat tissue. The hypoxia triggers a brain activity arousal to restore the normal breathing pattern until the next apneic episode occurs. OSA patients can experience hundreds of apneas per night. The frequent sleep disruptions and the resulting fragmented sleep pattern can have serious physical and psychological consequences and can lead to premature death.

Because the apnea episodes rarely trigger a full awakening, patients are often unaware of having difficulty breathing at night. As a result of this unawareness, OSA is severely underdiagnosed. This problem is exacerbated by the intrusiveness of current diagnosis methods. Polysomnography---the traditional gold-standard diagnosis method---requires close overnight monitoring of patients' body functions, making it an uncomfortable and intrusive diagnostic test. Intrusive diagnosis methods discourage patients from getting tested for OSA until they experience its serious health effects. As a result, many patients remain untreated and OSA continues to be a major public health problem that places a significant burden on health care systems worldwide.

In an effort to increase the level of diagnosis and treatment of the disorder, this work investigates the design and implementation of effective non-intrusive OSA diagnostic methods. We explore two kinds of detection methods: nocturnal methods that rely on diagnosing OSA by detecting apneic episodes from overnight recordings, as well as daytime methods which use features and signals that can be obtained while the subject is awake. This work involves identifying the data to be collected, building the appropriate systems to obtain them, and using machine learning techniques to analyze them.

The effectiveness of our methods was proven using real data from clinical trials. Daytime methods quickly revealed their advantages over the common nocturnal methods and the few existing daytime methods.

Experimental results show that our daytime methods perform the initial diagnosis of OSA non-intrusively without significantly affecting the diagnosis accuracy compared to the current state-of-the-art nocturnal methods.