A continuous mapping of sleep states through association of EEG with a mesoscale cortical model

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time

Sleep oscillations and aging

Human sleep can be broadly categorized as rapid eye movement (REM) and non-REM (NREM) sleep according to the electrophysiological features and oscillations that characterize these distinct states. The most dramatic changes that occur to sleep are observed over the course of the life span. With aging, the neural oscillations of sleep may provide insight into the physiological changes that accompany aging, may signal poor health and pathology, and explain functional changes in daytime performance, learning, and memory. This review will discuss the age-related changes that occur to the neural oscillations that characterize sleep states, such as sleep spindles, k-complexes, slow waves, and REM-related neural activity. The physiological processes that underlie these changes and the functional significance of the age-related changes in sleep are discussed