Using simulated neuronal cell models for detection of epileptic seizures in foramen ovale and scalp EEG

Objective: To demonstrate a novel approach for real-time and automatic dete ction of epileptic seizures in EEG recorded with foramen ovale (Fov) or sca lp electrodes. Methods: Our seizure detection method is based on simulated leaky integrate and fire units (LIFU), which are classical simple neuronal cell models. Th e LIFUs are connected to a signal preprocessing stage and increase their sp iking rates in response to rhythmic and synchronous EEG signals as typicall y occur at the onset and during seizures. Results: We analyzed 22 short-term(10 +/- 3 min) and 4 long-term(18 +/- 7 h ) Fov or scalp EEGs of 10 patients with drug resistant partial epilepsy. Se izures (n = 36) were marked by increases of the LIFUs spiking rates above a preset threshold. The durations of increased spiking rates due to seizures were always longer than 10 s (36 +/- 21 s) and allowed separation from art ifacts, which caused only short durations (1.2 +/- 0.6 s) of high spiking r ates. The LIFUs correctly detected all the seizures and produced no false a larms. In the long term Fov EEGs seizure detection occurred before the onse t of clinical signs (41 +/- 22 s). Conclusions: By using simulated neuronal cell models it is possible to auto matically detect epileptic seizures in scalp and Fov EEG with high sensitiv ity and specificity. (C) 2001 Elsevier Science Ireland Ltd. All rights rese rved.