# Identification of Epileptic EEG Signals Using Convolutional Neural Networks
## 4 Conclusions  
The design of a deep-learning structure based on CNNs for the detection of epilepsy using EEG signals has been performed. A four-level structure was used for training of the CNN to detect epilepsy. Training of CNN was implemented using a cross-validation technique. The design of the classification system was implemented using Bonn University data sets. As a result of the simulation, the average value of the accuracy rate for the test achieved was 98.67%, sensitivity was 97.67%, and specificity was 98.83%. One of the basic advantages of the model is its simple structure that combines the extraction of feature and classification stages in the body of the deep learning structure. The results obtained can be improved by increasing the number of convolutional layers, which in turn leads to complication of the deep structure. Future research is focusing on using a combination of CNNs and other feature extraction techniques to develop an identification system for the CHB-MIT dataset. Conceptualization: R.A.; investigation: M.A., B.S., J.B.I. and A.I. ; methodology: R.A., M.A. and B.S. ; software: M.A. and J.B.I. ; supervision: R.A.; validation: M.A., B.S. and A.I. ; resources: M.A., J.B.I. and A.I., Writing--Original draft: R.A., J.B.I. and M.A. ; Writing--Review and editing: R.A. and J.B.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare that there are no conflicts of interest regarding the publication of this paper. * _[PERSON] et al. (2003)_ [PERSON]; [PERSON]; [PERSON] Analysis of EEG records in an epileptic patient using wavelet transform. _J. Neurosci. Methods_**2003**, _123_, 69-87. [CrossRef]
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