Systemic Modeling of Chaotic EEG During Human Sleep

Abstract

One of the most challenging discussions about EEG is the chaotic nature of this biological signal. In the present study, we attempt to provide an analysis to demonstrate sleep EEG chaoticity. We model changes of sleep attractor dynamic in phase space by exponential regression. Our model demonstrates that the sleep attractor is the sleep cycle attractor whose size shrinks during successive cycles by presenting a new definition of the sleep cycle. We study the EEG dynamics of different sleep stages by presenting two new features based on phase space properties. We show that each stage has a unique chaotic attractor. We model geometric changes of these attractors during successive sleep cycles. Our model achieves an accuracy, sensitivity, and specificity of 89.15%, 82.84%, and 81.62% classifying sleep stages.