By the means of computing approximate entropy (ApEn) of video-EEG from some clinical epileptic, ApEn of EEG with epileptiform discharges is found significantly different from that of EEG without epileptiform discharges, (p=0. 002). Meanwhile, dynamic ApEn shows consistent change of EEG signal withdischarges of epileptic waves inside. These results suggest that ApEn may be a useful tool for automatic recognition and detection of epileptic activity and for understanding epileptogenic mechanism.