ABSTRACT
Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable in the intracranial EEG. A series of computer algorithms designed to detect the changes in spatiotemporal dynamics of the EEG signals and to warn of impending seizures have been developed. In this study, we evaluated the performance of a novel adaptive threshold seizure warning algorithm (ATSWA), which detects the convergence in Short-Term Maximum Lyapunov Exponent (STLmax) values among critical intracranial EEG electrode sites, as a function of different seizure warning horizons (SWHs). The ATSWA algorithm was compared to two statistical based naïve prediction algorithms (periodic and random) that do not employ EEG information. For comparison purposes, three performance indices "area above ROC curve" (AAC), "predictability power" (PP) and "fraction of time under false warnings" (FTF) were defined and the effect of SWHs on these indices was evaluated. The results demonstrate that this EEG based seizure warning method performed significantly better (P < 0.05) than both naïve prediction schemes. Our results also show that the performance indexes are dependent on the length of the SWH. These results suggest that the EEG based analysis has the potential to be a useful tool for seizure warning.
Subject(s)
Algorithms , Electroencephalography/methods , Electronic Data Processing/methods , Seizures/diagnosis , Seizures/physiopathology , Adult , Brain Mapping , Diagnosis, Computer-Assisted , Electrodes , Electroencephalography/statistics & numerical data , Female , Humans , Longitudinal Studies , Male , Middle Aged , Predictive Value of Tests , ROC Curve , Retrospective Studies , Sensitivity and Specificity , Time FactorsABSTRACT
Progressive preictal dynamical convergence and postictal divergence of dynamical EEG descriptors among brain regions has been reported in human temporal lobe epilepsy (TLE) and in a rodent model of TLE. There are also reports of anticonvulsant effects of high frequency stimulation of the hippocampus in humans. We postulate that this anticonvulsant effect is due to dynamical resetting by the electrical stimulation. The following study investigated the effects of acute hippocampal electrical stimulation on dynamical transitions in the brain of a spontaneously seizing animal model of TLE to test the hypothesis of divergence in dynamical values by electrical stimulation of the hippocampus.
