Biomechanics based analysis of sleep

The fact that a third of a human life is spent in a bed indicates the essential character of sleep. While some people might opt voluntarily for sleep deprivation, others don?t get to choose. Their healthy pattern of sleep is disrupted due to sleep disorders such as sleep apnea, insomnia and restless legs syndrome. Most clinical diagnoses revolve around complaints of excessive daytime sleepiness. People usually wait quite long however before contacting professional help, and might only do so when complaints have gone from minor to serious. It can be argued that people with minor complaints will have negligible compliance to rather obtrusive therapies, and should not be treated with pharmaceuticals. However, cognitive and behavioral therapy has proven its effectiveness for clinically diagnosed patients in different domains, and might thus also enhance the quality of life for people with minor complaints. Contrary to the rather invasive therapies, cognitive and behavioral therapies can be quite low-cost (and thus cost-effective). Current methods for objective diagnosis of sleep disorders are however too costly, impractical and intrusive, or lack sufficient information and/or accuracy, to be used for long-term screening or follow-up after diagnosis. This PhD work hypothesizes that automated cardiac, respiratory and movement-based analysis could be able to bridge this gap, especially when all signals are monitored o?-body in a mechanical way. The first part of this work investigates the ability to use cardiac, respiratory and movement activity for sleep monitoring in healthy subjects. Using a dataset of 85 nights, several classification models were built to distinguish between Wake, REM, light sleep (N1-N2) and deep sleep (N3). The models were trained with and validated against gold standard polysomnography annotations, derived by sleep experts. Relevant characteristics of the cardiac, respiratory and movement activity are outlined, and limitations and prerequisites of the model discussed. The large amount of variability in cardiac and respiratory functioning among different subjects led to difficult-to-avoid misclassifications. Still, agreement values around 80% and kappa values around 0.60 confirm the potential of the method