This paper presents a dynamical characterization of epileptic seizures in animal models. Inter-hippocampal recordings of two animal models of seizures, kindling and pilocarpine, were analyzed by nonlinear analytic tools. The aim is to assess and differentiate pathophysiological states and behavioral phases of a status epilepticus. The achieved results indicates that stage V of Racine classification could be identified as the transition of dynamical indicators exhibit a monotonic decline up to this stage and an increase after that. Furthermore, concentration of data points on a small region of state space, achieved by our analysis, promises that a local nonlinear control may cause neuromodulation. This feasibility gets more strengthen by achievements of this paper on successful tracking of drifts of unstable periodic orbits at seizure onset. Nonlinear control algorithms could afterwards be designed to find suitable instances for inserting perturbations and steer the dynamics of system toward a desired dynamical operating mode.