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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 with discharges 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.
ApEn of EEG with epileptiform discharges was significantly different from that of EEG without epileptiform discharges, (p = 0.002). Meanwhile, dynamic ApEn shows consistent change of EEG signal with discharges 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.