The complexity, entropy and other non-linear measures of the electroencephalogram (EEG), such as Higuchi fractal dimension (FD), have been recently proposed as the measures of anesthesia depth and sedation. We hypothesized that during unconciousness in rats induced by the general anesthetics with opposite mechanism of action, behaviorally and poligraphically controlled as appropriately achieved stable anesthesia, we can detect distinct inter-structure brain dynamic using mean FDs. We used the surrogate data test for nonlinearity in order to establish the existence of nonlinear dynamics, and to justify the use of FD as a nonlinear measure in the time series analysis. The surrogate data of predefined probability distribution and autocorrelation properties have been generated using the algorithm of statically transformed autoregressive process (STAP). FD then is applied to quantify EEG signal complexity at the cortical, hippocampal and pontine level during stable general anesthesia (ketamine/xylazine or nembutal anesthesia). Our study showed for the first time that global neuronal inhibition caused by different mechanisms of anesthetic action induced distinct brain inter-structure complexity gradient in Sprague Dawley rats. EEG signal complexities were higher at cortical and hippocampal level in ketamine/xylazine vs. nembutal anesthesia, with the dominance of hippocampal complexity. In nembutal anesthesia the complexity dominance moved to pontine level, and ponto-hippocampo-cortical decreasing complexity gradient was established. This study has proved the Higuchi fractal dimension as a valuable tool for measuring the anesthesia induced inter-structure EEG complexity.
Higuchi Fractal Dimension as a Method for Assessing Response to Sound Stimuli in Patients with Diffuse Axonal Brain Injury
