Stochastic modeling and prediction of experimental seizures in Sprague-Dawley rats

Most seizure prediction methods are based on nonlinear dynamic techniques, which are highly computationally expensive, thus limiting their clinical usefulness. The authors propose a different approach for prediction that uses a stochastic Markov chain model. Seizure (T_s) and interictal (T_i) durations were measured from 11 rats treated with 3-mercaptopropionic acid. The duration of a seizure T_s was used to predict the time (T_i2) to the next one. T_s and T_i were distributed bimodally into short (S) and long (L), generating four probable transitions: S → S, S → L, L → S, and L → L. The joint probability density f (T₂, T_i2) was modeled, and was used to predict T_i2 given T_s. An identical model predicted T_s given the duration T_i1 of the preceding interictal interval. The median prediction error was 3.0 ± 3.5 seconds for T_s (given T_i1) and 6.5 ± 2.0 seconds for T_i2 (given T_s). In comparison, ranges for observed values were 2.3 seconds < T₂ < 120 seconds and 6.6 seconds < T_i < 782 seconds. These results suggest that stochastic models are potentially useful tools for the prediction of seizures. Further investigation of the probable temporal interdependence between the ictal and interictal states may provide valuable insight into the dynamics of the epileptic brain.