Detection of interictal epileptic discharges (IED) events in the EEG recordings is a critical indicator for detecting and diagnosing epileptic seizures. We propose a key technology to extract the most important features related to epileptic seizures and identifies the IED events based on the interaction between frequencies of EEG with the help of a two-level recurrent neural network. The proposed classification network is trained and validated using the largest publicly available EEG dataset from Temple University Hospital. Experimental results clarified that the interaction between β and β bands, β and γ bands, γ and γ bands, δ and δ bands, θ and α bands, and θ and β bands have a significant effect on detecting the IED discharges. Moreover, the obtained results showed that the proposed technique detects 95.36% of the IED epileptic events with a false-alarm rate of 4.52% and a precision of 87.33% by using only 25 significant features. Furthermore, the proposed system requires only 164 ms for detecting a 1-s IED event which makes it suitable for real-time applications.