Nonlinear system identification and analysis of complex dynamical behaviorin reflected light measurements of vasomotion

Nonlinear system identification and analysis methods are employed to study the low-frequency oscillations present in time-series data obtained from re flectance imagery of microvasculature. Using the method of surrogate data t esting the analysis reveals the deterministic nature of these oscillations believed by many to be chaotic. Further investigations by means of nonlinea r system identification techniques indicate however that the underlying dyn amics can described by a periodically driven nonlinear dynamical model exhi biting quasiperiodic behavior.