Real-time EEG parameterization for shunt decision supporting system during carotid endarterectomy

Abstract

Intraoperative EEG monitoring during carotid endarterectomy (CEA) is the common operation used to reduce the risk of brain ischemia. Beside visual assessment of the EEG, some quantitative parameters, based on spectral information, have been recently suggested as additional criteria for shunt need decision. In this paper we explore spectral power-based parameters and some non linear parameters, like zero crossing (ZC) and beta coefficient, in order to find the parameter/s that could constitute a good decision support system in shunt decision. The results, compared with those supplied by the Brain Symmetry Index, suggest that the ZC represents the best parameter in a real time analysis of EEG during CEA. © 2008 Springer-Verlag.