# Identification of Epileptic EEG Signals Using Convolutional Neural Networks
## 3 Simulation
The crucial concept of dropout is to drop units randomly from the neural network during training, with a predefined probability (along with their connections). Dropout methodology greatly eliminates over-fitting and provides major advantages over other forms of regularization. We introduced the dropout layer in the proposed model after the last ReLU activation function. The constructed model integrates feature extraction and classification modules, which simplifies the structure of the epilepsy identification model. By including more convolutional layers or using other non-linear functions, we can increase the performance characteristics and also the accuracy of the model. However, this will lead to complication of the structure and learning process of the model. Consequently, decreasing the number of layers leads to a decrease in the accuracy rate of CNN. The results of the simulations are given in Table 6.  
The goal of seizure detection is to segment the brain's electrical activity in real-time into seizure and non-seizure periods. This is implemented through the classification of extracted spectral and spatial features of the ECG signals. Rhythmic activity associated with the seizure is composed of multiple frequency components. It is necessary to consider multiple spectral components in order to improve the accuracy of seizure detection. Epileptic patients have substantial EEG-related variation in non-seizure and seizure states. This affirms a steep trade-off between specificity and sensitivity of the model. We estimated the performance of the CNN model by using TP (true positive), FP (false positive), FN (false negative), sensitivity and false positive rate. Sensitivity refers to the percentage of identified test seizures. TP refers to the number of abnormal EEG recordings properly classified as being abnormal. True negative (TN) refers to the number of normal EEG cases properly classified as normal. FP refers to the number of normal EEG instances that are considered to be abnormal, while FN refers to the number of abnormal EEG recordings that are wrongly predicted as being normal. From Table 6, the most efficient performance metric of our model is sensitivity. This refers to the percentage of the correctly identified test seizures. If an alarm is triggered between its onset and its end, a seizure (true positive) is seen as correctly diagnosed. The rate of false alarm (false positive) refers to the average number of times, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Patient** & **No of Seizures** & **TP** & **FP** & **FN** & **SEN** & **FPR** \\ \hline Pat1 & 7 & 47.4286 & 0 & 3 & 94 & 0.0 \\ \hline Pat2 & 3 & 17.33 & 38.33 & 33.33 & 33 & 4.381 \\ \hline Pat5 & 5 & 40.2 & 3 & 11.6 & 76.85 & 1.0286 \\ \hline Pat6 & 9 & 24.8889 & 4.33 & 26.667 & 46.92 & 1.4257 \\ \hline Pat7 & 3 & 1 & 97.33 & 49 & 2 & 5.6974 \\ \hline Pat9 & 4 & 15.25 & 12.25 & 35.25 & 29.65 & 0.98 \\ \hline Pat23 & 5 & 46.54 & 0.8 & 4.4 & 91.4 & 0.274 \\ \hline \hline \end{tabular}  
* Abbreviation: TP: true positive; FP: false positive; FN: false negative; SEN: sensitivity; FPR: false positive rate.  
\end{table} 
Table 6: Simulation results. per 24 h, the model erroneously predicts the onset of a seizure. Alarms that occur outside the interval between the beginning of a seizure and the end of the same seizure are considered false alarms. The seizures differed from one another in the training dataset. The lack of sufficiently comparable cases of onset events from seizures in the training set led to the fairly low identification for each given test seizure. However, not all instances involving several distinct forms of seizures produce bad results. For instance, patient 6, for whom the clinical test identified a few types of seizure due to inadequate clinical symptoms, was increasingly achieving good results.