Applying nonlinear dynamics to transcranial Doppler time-series analysis for assessing cerebral autoregulation

Fractal methods have been proved to be valuable as a tool to quantify the heterogeneity of physiological time-series and the underlying regulation processes. The aim of this study is to evaluate whether cerebrovascular autoregulation can be described with fractal analysis. It's clinical application offers an assesment of signal variability that is not available from more conventional parameters and it could become a fast noninvasive method for assessing cerebral autoregulation. Eighteen healthy volunteers TCD monitoring was performed with head-up tilt perturbation. In each of the measurements at every head position we recorded the maximal velocity of the left MCA continuously for 11 minutes. The underlying structure of the TCD time-series was examined by calculating its phase space plot. The Fractal Dimension (FD) was estimated by dispersional analysis. The resulted topology of the TCD's phase space plots revealed complexity in every case. Between the average FDs in each position there were no significant differences in all cases. Our data suggests that FD calculated from TCD time-series represents a clinically useful tool to describe the complex human autoregulation. The use of this mathematical algorithm increases the effectiveness of TCD diagnostics.