TY - JOUR
T1 - Stochastic modeling and prediction of experimental seizures in Sprague-Dawley rats
AU - Sunderam, Sridhar
AU - Osorio, Ivan
AU - Frei, Mark G.
AU - Watkins, James F.
PY - 2001
Y1 - 2001
N2 - 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 (Ts) and interictal (Ti) durations were measured from 11 rats treated with 3-mercaptopropionic acid. The duration of a seizure Ts was used to predict the time (Ti2) to the next one. Ts and Ti 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, Ti2) was modeled, and was used to predict Ti2 given Ts. An identical model predicted Ts given the duration Ti1 of the preceding interictal interval. The median prediction error was 3.0 ± 3.5 seconds for Ts (given Ti1) and 6.5 ± 2.0 seconds for Ti2 (given Ts). In comparison, ranges for observed values were 2.3 seconds < T2 < 120 seconds and 6.6 seconds < Ti < 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.
AB - 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 (Ts) and interictal (Ti) durations were measured from 11 rats treated with 3-mercaptopropionic acid. The duration of a seizure Ts was used to predict the time (Ti2) to the next one. Ts and Ti 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, Ti2) was modeled, and was used to predict Ti2 given Ts. An identical model predicted Ts given the duration Ti1 of the preceding interictal interval. The median prediction error was 3.0 ± 3.5 seconds for Ts (given Ti1) and 6.5 ± 2.0 seconds for Ti2 (given Ts). In comparison, ranges for observed values were 2.3 seconds < T2 < 120 seconds and 6.6 seconds < Ti < 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.
KW - 3-MPA
KW - Experimental epilepsy models
KW - Rat
KW - Seizure prediction
KW - Stochastic
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U2 - 10.1097/00004691-200105000-00007
DO - 10.1097/00004691-200105000-00007
M3 - Article
C2 - 11528299
AN - SCOPUS:0034828299
SN - 0736-0258
VL - 18
SP - 275
EP - 282
JO - Journal of Clinical Neurophysiology
JF - Journal of Clinical Neurophysiology
IS - 3
ER -