## 4 Results and Discussion  
Experiments were conducted, which involved training classifiers for each of the discussed features separately using the training data. Then, classification was performed on the training data. The quality of classification was evaluated using the measures: Accuracy, precision, sensitivity, and specificity. In the classification task, an SVM classifier with an RBF kernel was used. The results of classification quality (accuracy, precision, sensitivity, and specificity) for each feature are presented in Table 5. The obtained results indicate that features such as autocorrelation, spectrum, and features related to signal energy and variance allow for achieving very good classification accuracy. For the spectrum and autocorrelation feature, the results of accuracy, precision, sensitivity, and specificity at levels of 0.97, 0.96, 0.98, and 0.96, respectively, are very promising in the context of epileptic seizure detection. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** \\ \hline energy (signal) & 0.92 & 0.87 & 0.99 & 0.85 \\ energy (delta) & 0.85 & 0.82 & 0.89 & 0.80 \\ energy (theta) & 0.92 & 0.92 & 0.92 & 0.92 \\ energy (alpha) & 0.92 & 0.91 & 0.93 & 0.91 \\ energy (beta) & 0.95 & 0.95 & 0.96 & 0.95 \\ energy (gamma) & 0.89 & 0.89 & 0.88 & 0.89 \\ var (cd1) & 0.91 & 0.92 & 0.90 & 0.92 \\ var (cd2) & 0.95 & 0.91 & 0.99 & 0.90 \\ var (cd3) & 0.96 & 0.95 & 0.97 & 0.95 \\ var (cd4) & 0.92 & 0.92 & 0.93 & 0.92 \\ skewness (cd1) & 0.50 & 0.50 & 0.58 & 0.43 \\ skewness (cd2) & 0.45 & 0.46 & 0.53 & 0.37 \\ skewness (cd3) & 0.58 & 0.60 & 0.51 & 0.65 \\ skewness (cd4) & 0.51 & 0.51 & 0.38 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of classification quality for the test data calculated for each feature individually. An accuracy result of 0.97 means that the detection algorithm correctly classified 97% of all samples, including epileptic seizures and other EEG signals. This indicates a high degree of overall classification correctness. A precision of 0.96 means that 96% of the cases classified as epileptic seizures were correct. A high precision score indicates a low percentage of false positive results. A sensitivity of 0.98 means that the algorithm correctly identified 98% of all actual epileptic seizure cases. A higher sensitivity means fewer epileptic seizures will be missed. A specificity of 0.96 means that the algorithm correctly identified 96% of cases that were not epileptic seizures. A higher specificity means fewer cases other than epileptic seizures will be incorrectly classified as positive. Features with accuracy results below 0.6 (below 60%) are lyapExp (0.49), dim (0.51), and skewness (cd1) (0.50). These three features did not fulfill their purpose and showed low accuracy in epileptic seizure classification. In subsequent experiments, the features were grouped into those related to signal energy in frequency bands, variance, skewness, kurtosis, entropy calculated for the details of wavelet transform, features related to measures of chaos such as variance, skewness, kurtosis, entropy calculated for IMF, spectrum, and autocorrelation. Apart from the spectrum and autocorrelation (0.97), the best results were obtained for variance and measures related to chaos (Table 6). In the next stage, a CNN network was trained using all the training data (iEEG signals). Then, the trained network was used to classify the data from the test set. Multiple runs of the CNN and LSTM networks confirmed that the network effectively learned and achieved excellent results. To confirm this observation, we conducted ten runs of the CNN and LSTM networks, paying attention to accuracy, sensitivity, precision, and specificity values. The results of these runs were highly consistent. For the training and testing process of the CNN network with the training data, we obtained very similar values. The most frequently