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 detection of epileptic seizures in EEG recorded with foramen ovale (Fov) or scalp electrodes.
METHODS: Our seizure detection method is based on simulated leaky integrate and fire units (LIFU), which are classical simple neuronal cell models. The LIFUs are connected to a signal preprocessing stage and increase their spiking rates in response to rhythmic and synchronous EEG signals as typically 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. Seizures (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 artifacts, which caused only short durations (1.2+/-0.6 s) of high spiking rates. The LIFUs correctly detected all the seizures and produced no false alarms. In the long term Fov EEGs seizure detection occurred before the onset of clinical signs (41+/-22 s).
CONCLUSIONS: By using simulated neuronal cell models it is possible to automatically detect epileptic seizures in scalp and Fov EEG with high sensitivity and specificity.