Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages.

ABSTRACT: In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviatio

SUBMITTER: Metzner C 

PROVIDER: S-EPMC8664947 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature