A linear epileptic seizure predictor based on slow waves of scalp EEGs

Seizure prediction could change treatment of epilepsy, which has attracted substantial attention over the past two decades. Nonlinear EEG analysis techniques have made some successes at it, but linear analysis techniques being with the controversial results. We put forward a linear seizure predictor, which tracks the time evolution of the slow wave energy bigger than some preset threshold from scalp EEGs. The results from four generalized epileptic patients demonstrate that pre-seizure transition phases of several minutes can be identified clearly by our linear predictor, which could help us to a deeper understanding of the mechanism of seizure and open the possibility of clinical applications.