Detecting epileptic electroencephalography (EEG) signals, both automatically and accurately, is significant in ambulatory long-term monitoring patients with epilepsy. In this study, it is presented a novel epileptic-like event detection algorithm based on a mixture of amplitude, frequency and spatial analysis with rule-based decision. In this work, EEG signals from 6 different subjects were searched for epileptic-like and normal data segments. The herein proposed algorithm detects putative epileptic EEG channels by comparing the RMS values of EEG activity with a hysteresis threshold, on a channel basis. The raw EEG signals are filtered with an artefact attenuation technique. The threshold is calculated on a reviewer-visually-selected baseline epoch, free of artefacts. Generalized epileptic activity detection is based on a spatial decision rule. Experimental results have shown detection rates as high as 95% with a false-negative rate as low as 1%. The algorithm seems to show a promising detection performance, even on artifact contaminated datasets. The proposed algorithm is intended to be used in real-time ambulatory monitoring of epileptic patients, with subject personalization, small size window analysis, good artefact immunity and no need for classifier training.