## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** \\ \hline kurtosis (cd1) & 0.73 & 0.70 & 0.82 & 0.65 \\ kurtosis (cd2) & 0.58 & 0.56 & 0.69 & 0.46 \\ kurtosis (cd3) & 0.53 & 0.52 & 0.66 & 0.39 \\ kurtosis (cd4) & 0.54 & 0.55 & 0.45 & 0.63 \\ entropy (cd1) & 0.75 & 0.76 & 0.71 & 0.78 \\ entropy (cd2) & 0.81 & 0.81 & 0.83 & 0.80 \\ entropy (cd3) & 0.74 & 0.68 & 0.90 & 0.58 \\ entropy (cd4) & 0.67 & 0.62 & 0.86 & 0.47 \\ entropy (signal) & 0.64 & 0.61 & 0.77 & 0.51 \\ lyapExp (signal) & 0.49 & 0.49 & 0.56 & 0.42 \\ Lag (signal) & 0.65 & 0.67 & 0.60 & 0.70 \\ Dim (signal) & 0.51 & 0.50 & 0.99 & 0.03 \\ corDim (signal) & 0.90 & 0.86 & 0.96 & 0.85 \\ fractalDimension & 0.65 & 0.65 & 0.65 & 0.65 \\ (signal) & 0.94 & 0.92 & 0.96 & 0.92 \\ var (imf2) & 0.90 & 0.89 & 0.91 & 0.89 \\ var (imf3) & 0.79 & 0.80 & 0.78 & 0.80 \\ skewness (imf1) & 0.55 & 0.55 & 0.50 & 0.59 \\ skewness (imf2) & 0.56 & 0.58 & 0.44 & 0.68 \\ skewness (imf3) & 0.49 & 0.49 & 0.60 & 0.38 \\ kurtosis (imf1) & 0.69 & 0.71 & 0.64 & 0.75 \\ kurtosis (imf2) & 0.62 & 0.63 & 0.60 & 0.65 \\ kurtosis (imf3) & 0.54 & 0.54 & 0.50 & 0.58 \\ entropy (imf1) & 0.89 & 0.94 & 0.83 & 0.95 \\ entropy (imf2) & 0.86 & 0.93 & 0.79 & 0.94 \\ entropy (imf3) & 0.75 & 0.79 & 0.70 & 0.81 \\ Spectrum (signal) & 0.97 & 0.96 & 0.98 & 0.96 \\ Autocorrelation (signal) & 0.97 & 0.96 & 0.98 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
CNN correctly identified 98% of cases that were not epileptic seizures. On the other hand, the LSTM network achieved an accuracy of 0.98, with a precision of 0.96. The sensitivity achieved a value of 1.00, while the specificity is 0.98. In summary, the results of epileptic seizure detection for the CNN and LSTM networks are very promising. The obtained accuracy detection results can be compared with other works on seizure detection. A comprehensive comparison and summary of seizure detection accuracy results were presented by [PERSON] et al. [126]. The accuracy results vary depending on the database used and the detection method applied, ranging from 0.905 to 1. For example, in the case of the CHB-MIT database and the CNN approach proposed by [PERSON] et al. [127], the results demonstrate that the original CNN achieves a sensitivity of 70.7% and a specificity of 92.3% for epileptic EEG classification. Conversely, a remarkable 100% accuracy in detection was achieved for the Bonn database using the GRP-DNet algorithm introduced by [PERSON] et al. [128]. The obtained results should also be compared with the application of transformer-based networks for epileptic seizure detection. The model proposed by [PERSON] et al. [37] achieved an AUROC of 92.1% when tested on Temple University's publicly available electroencephalogram (EEG) seizure corpus dataset (TUH). In their article, [PERSON] et al. [38] reported a remarkable event-based sensitivity of 97.5% for the SWEC-ETHZ iEEG dataset, while achieving an event-based sensitivity of 98.1% for the TJU-HH iEEG dataset. In the study conducted by [PERSON] et al. [39], experiments were performed on two EEG datasets, demonstrating that the model provides state-of-the-art performance. Specifically, on the CHB-MIT dataset, the model achieves an average sensitivity of 96.02% and an average specificity of 97.94%, surpassing other existing methods by significant margins. The good results of accuracy, precision, sensitivity, and specificity can be used, to some extent, to evaluate the usefulness of specific features and to compare algorithms. However, it is necessary to critically examine how the research and solutions can be practically applied in the medical field. This stage of analysis, in our opinion, is often overlooked in many scientific publications. In our assessment, high measures alone do not indicate practical utility and do not objectively present the potential for utilizing the created detection system. An important aspect is the way data are collected and selected for the experiments.