# Identification of Epileptic EEG Signals Using Convolutional Neural Networks
## 3 Simulation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author (Year)** & **Method** & **Performances** \\ \hline [PERSON] et al. & & \\ (2017) [54] & Spiking neural network & ACC: 92.5\% \\ \hline [PERSON] et al. & Levenberg–Marquardt backpropagation & ACC: 96.7\% \\ (2009) [55] & neural network & ACC: 99.3\% \\ (2007) [12] & \begin{tabular}{c} Principal component analysis enhanced \\ cosine radial basis function neural network \\ \end{tabular} & ACC: 99.3\% \\ \hline [PERSON] et al. (2010) [56] & Gaussian mixture model & ACC: 93.1\%; SEN: 89.7\% \\  & & SPEC: 94.8\% \\ \hline [PERSON] et al. (2010) [57] & SVM & ACC: 93.3\%; SEN: 98.3\% \\  & & SPEC: 96.7\% \\ \hline [PERSON] et al. (2011) [58] & SVM-Discrete wavelet transform & ACC: 96.3\%; SEN: 100\% \\  & & SPEC: 97.9\% \\ \hline [PERSON] et al. (2011) [59] & Genetic Programming-KNN & ACC: 93.5\% \\ \hline [PERSON] et al. (2011) [16] & SVM & ACC: 95.6\%; SEN: 98.9\% \\  & & SPEC: 97.8\% \\ \hline [PERSON] et al. (2012) [60] & Fuzzy Sugeno (Wavelet packet & ACC: 96.7\%; SEN: 95\% \\  & decomposition) & SPEC: 99\% \\ \hline [PERSON] et al. (2012) [61] & C4.5 decision tree & ACC: 95.3\%; SEN: 98\% \\  & & SPEC: 97\% \\ \hline [PERSON] et al. & & ACC: 99.4\%; SEN: 97.9\% \\ (2017) [62] & Random forest & SPEC: 99.5\% \\ \hline [PERSON] et al. & & ACC: 98.6\% \\ (2017) [63] & SVM & ACC: 98.6\% \\ \hline [PERSON] et al. (2017) [64] & LS-SVM & SEN 100\% \\  & & ACC: 99\%; SEN: 98\% \\ \hline [PERSON] et al. (2018) [47] & Orthogonal wavelet filters bank & SPEC: 99\% \\ \hline [PERSON] et al. (2018) [24] & Deep CNN & ACC: 88.7\%; SEN: 95\%; SPEC: 90\% \\ \hline [PERSON] et al. & & ACC: 98.01\%; SEN: 94.99\%; \\ (2019) [43] & Genetic algorithm & SPEC: 98.65\% \\ \hline [PERSON] et al. (2019) [44] & FBSE + WMRPE + Regression & ACC: 98.6\% \\ \hline [PERSON] et al. (2017) [46] & \
\begin{tabular}{c} Hilbert transform + Learning Vector \\ Quantization \\ \end{tabular} & ACC: 89.31\% \\ \hline [PERSON] et al. (2018) [65] & P-1D-CNN & ACC: 99.6\% \\ \hline [PERSON] et al. (2019) [66] & DNN & ACC: 97.21\%; SEN: 98.59\%; \\  & SPEC: 91.47\% \\ \hline [PERSON] et al. (2020) [67] &
\begin{tabular}{c} Complete ensemble empirical \\ mode decomposition \\ \end{tabular} & ACC: 98.67\%; SEN: 98.67\%; \\ \hline [PERSON] (2020) [68] & SEA & ACC: 97.17\%; SEN:93.11\%; \\  & & SPE: 98.18\% \\ \hline \multirow{2}{*}{The current work} & \multirow{2}{*}{Deep CNN (10-fold cross-validation)} & ACC: 98.67\%; SEN: 97.67\%; \\  & & SPEC: 98.83\% \\ \hline \hline \end{tabular}  
* Abbreviation: ACC: accuracy; SEN: sensitivity; SPEC: specificity; KNN: k-nearest neighbors algorithm; LS-SVM: least-squares support vector machine; FBSE: Fourier–Bessel series expansion; WMRPE: weighted multiscale Renyi permutation entropy; P-1D-CNN: pyramidal one-dimensional convolutional neural network; DNN: deep neural network; SEA: stacking ensemble approach.  
\end{table} 
Table 5: Comparative results of different models. (i.e., performs well on the training dataset) but does not perform well on a new or unknown dataset. Over-fitting happens when a model learns noise and detail in the training set to the extent that it negatively impacts the model's performance on new data. During the simulation, we applied some operations to solve the over-fitting problem, namely weight regularization, random shuffling of data and dropout. We performed weight regularization to improve the learning of the CNN. This allowed us to reduce over-fitting leading to faster optimization of the CNN model. We also shuffled the data before splitting it into the training and testing sets. In that way, the classes were equally distributed over the training and testing sets. Next, we applied the dropout operation. A deep neural network needs to learn a huge number of parameters, which in the case of a small dataset is likely to cause over-fitting. This issue is solved by designing dropout technology to prevent feature detectors from coadapting.