Objective: The relevance of the dimensional complexity (DC) for the analysis of sleep EEG data is investigated and compared to linear measures.
Methods: We calculated DC of artifact-free 1 min segments of all-night sleep EEG recordings of 4 healthy young subjects. Non-linearity was tested by comparing with DC values of surrogate data. Linear properties of the segments were characterized by estimating the self-similarity exponent alpha based on the detrended fluctuation analysis which quantifies the persistence of the signal and by calculating spectral power in the delta, theta, alpha and sigma bands, respectively.
Results: We found weak nonlinear signatures in all sleep stages, but most pronounced in sleep stage 2. Strong correlations between DC and linear measures were established for the self-similarity exponent alpha and delta power, respectively.
Conclusions: The dimensional complexity of the sleep EEG is influenced by both linear and nonlinear features. It cannot be directly interpreted as a nonlinear synchronization measure of brain activity, but yields valuable information when combined with the analysis of linear measures.