Monitoring Consciousness and Spatiotemporal Dynamics During Complex Focal Seizures

Abstract

The Electroencephalogram recording system plays a very important role to identify seizure occurrence and its severity range. Thus, it requires long-term video monitoring and visual interpretation from expert. In this paper, an intelligent seizure prediction algorithm is proposed allowing the prediction and localization of ictal excessive discharges and the control of consciousness impairment. The Extended Kalman Filter is employed to predict seizure occurrence. Secondly, a Spherical Spline interpolation algorithm is used to enhance the EEG spatial resolution. The algorithm was tested on 24 epileptic patients from two different databases. The proposed Method predict occurrence of all seizures before an average of 8 minutes, correctly localize ictal epileptic discharges and control their propagation on the scalp. These findings highlight the capacity to automatically extract useful spatio-temporal features from Cranial EEG.