Predicting Pathophysiological Events: Sleep Disordered Breathing, Vigilance Lapses, Driving Errors

Disrupted or insuficient sleep is a major cause of motor vehicle accidents, workplace injuries, and industrial disasters. Responding to this epidemic is a public health imperative. Two major causes of disrupted sleep and consequent impairment of daytime performance are obstructive sleep apnea (OSA) and sleep restriction or deprivation. OSA is a highly prevalent disorder in which patients experience repetitive total or partial collapses of the airway, respectively referred to as apneas and hypopneas, during sleep. These episodes of sleep disordered breathing (SDB) are commonly associated with arousal from sleep. Consequently, OSA patients experience disrupted sleep and are exposed to chronic intermittent hypoxemia, leading to impaired daytime performance and excessive daytime sleepiness. People with OSA are also at risk for heart disease, stroke, metabolic disorders, and cognitive dysfunction. Many of the adverse out- comes associated with OSA are also associated with sleep deprivation, even in the absence of a concomitant sleep disorder.;Therefore, there is a pressing need to develop a deeper understanding of the mechanisms that underlie OSA and the effects of restricted sleep. Furthermore, public safety will be enhanced by improved and more efficacious treatment of sleep disorders as well as accurate and reliable methodologies to monitor the behavioral state of at-risk individuals and respond to imminent decrements or lapses in performance. At the same time, the complexity of the processes that underlie SDB, sleep restriction, and their consequences, as well as significant inter-individual variability, necessitates individualized systems-based approaches. Such methodologies may provide important tools to aid in the identification and disentanglement of the differential impact of numerous pathophysiological mechanisms in heterogeneous populations and reveal subgroups or empirical stratifications among groups of individuals.;This dissertation aimed to take a first step toward responding to these needs by establishing the feasibility of individualized approaches to predict the onset of pathophysiological events in people with OSA and in acutely sleep deprived but otherwise healthy individuals. Our methods were also designed to expose precisely the signals and signal features that enable accurate and reliable predictions. The analysis of important predictors of pathophysiological events provides a novel approach to investigating the mechanisms that initiate the events. In particular, methods were developed to 1) predict episodes of SDB in the next 10 to 60 seconds in OSA patients, 2) lapses in vigilance, or sustained attention, within the next 30 seconds in OSA patients and in healthy individuals acutely deprived of sleep for 24 hours, and 3) mistakes during simulated driving within the next 30 seconds in OSA patients and acutely sleep deprived healthy subjects. (Abstract shortened by UMI.).