The microstructure of cortical activity during sleep: Development of an objective, automatic procedure for sleep stage classification

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Sleep disorders are among the most frequently complainedsymptoms, especially in the industrial nations, with a clear upwardtrend. In addition to the direct consequences for those affected,chronic sleep disorders cause costly damage to society, e.g. throughfatigue-related errors and accidents at work, and human failures incontrol and monitoring activities. Sleep disorders are therefore asignificant and growing medical problem in terms of public health.Since the 1970s, polysomnography in sleep laboratories has been thegold standard in the diagnosis of many sleep disorders. Theevaluation of the biosignals recorded in the sleep laboratory for sleepanalysis is on the one hand extremely time-consuming and on theother hand subject to a high inter- and intra-rater reliability. Therefore,the main goal of the current project is to establish a novel, completelyobjectified and therefore automatable sleep stage analysis by meansof a global pattern analysis of the cortical activity distribution based on64-channel EEG data. This analysis will be based on objectivemethods of dimensional reduction, on a novel Bayesian method ofsuper-statistical time series analysis, on a flexible mathematicalmodel to describe complex probability distributions, and on evidencebasedBayesian model selection.Such a method could lead to a deeper understanding of the neurophysiology of sleep as a whole, and could have the clinical-practical benefit that the time-consuming visual/manual sleep stage analyses could be replaced in the future by an automated, objective method that could provide a time-resolved and dynamic image of the sleep macro- and microstructure. Furthermore, with the help of the novel methodology, the investigation of local sleep phenomena in patients with obstructive sleep apnea could lead to new insights into the pathophysiology of excessive daytime sleepiness.