# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion
### Comparison with Other Works  
To evaluate the proposed approach, its performance is compared with other models in the current state-of-the-art. The comparative results are shown in Table 5. Thesesresults demonstrate that state-of-the-art models generally exhibit excellent detection accuracy. However, it is clear that as researchers strive to improve accuracy, the complexity of the models also increases. Many studies do not consider this factor, fail to mention it, or address it only superficially. Since the primary focus of these works is on achieving the highest accuracy regardless of computational cost, discussions about implementing these algorithms on low-cost devices or simple applications are often overlooked.  
To highlight our model's ability to reduce computational cost, Table 5 includes the operational time per second of the signal, where such data were provided. For studies that did not supply this information, it is marked as \"not available\" (N/A), where in most of them the computational complexity is very high that even they ignored this issue and just focused on the accuracy. The studies [14; 24; 26] examined the algorithm's processing time, achieving excellent speed results. In this regard, our approach ranked second among the fastest, surpassed only by [14].  
These findings encourage further exploration of the model's efficiency in real-time scenarios. The remaining studies either do not provide processing speed data for their models or only briefly mention computational cost without further elaboration.  
Drawing from these findings, it becomes evident that the proposed model demonstrates proper performance compared to other methods. Further enhancements could be achieved by improving the dataset quality, such as employing appropriate feature selection methods for training or considering new feature functions. Additionally, exploring better SVM models by varying the amount of data input for training, adjusting the slack parameter \(C\), or utilizing kernel functions could lead to improvements. Despite exhibiting slightly lower performance than other reviewed models, the proposed model showcases its effectiveness with a satisfactory accuracy of 92.82% for the alpha band and 90.55% for the beta band. These findings suggest that the model could be utilized in epilepsy attack  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Operational Time (s)** & **Database** \\ \hline
1-D CNN, + Butterworth & 99.4 & N/A & Bonn \\ filter, [9] & 97.5 & N/A & Freiburg \\ CNN, [10] & 92.37 & N/A & Xinjang \\
3-D CNN + LSTM + PCA [12] & 99.3 & N/A & Not available \\ LSTM, [13] & 100.0 & N/A & Bonn \\ FC-NLSTM, [14] & 100.0 & 0.00158 & Bonn/Freiburg \\ CNN + LSTM, [15] & 100 & N/A & Bonn \\ RTS-RCVAE, [16] & 98.43 & N/A & Bonn \\ WT + ANN, [17] & 99.6 & N/A & Bonn \\ CWT + CAE + LSTM, [18] & 100 & N/A & Bonn \\ FFT + WPD + CNN, [19] & 98.33 & N/A & CHB-MIT \\ LB + DWT + LSTM, [20] & 96.1 & N/A & HCTM \\ TQWT + CNN + LSTM, [21] & 99.71 & N/A & Bonn/Freiburg \\ DWT + SVM, [22] & 95.6 & N/A & CHB-MIT \\ HHT + EWT + SVM, [23] & 100 & N/A & Bonn \\ DCT + SVM, [24] & 97 & 0.02444 & Bonn \\ CMIM + SVM, [25] & 99.83 & N/A & CHB-MIT \\ HHT + WT + KNN + SVM + SVM + LDA, [26] & 98 & 0.37 & CIREN \\ EMD + IMF + KNN, [27] & 98.67 & N/A & Bonn \\ Kurtosis-based channel + EWT, [28] & 99.88 & N/A & CHB-MIT \\ EGF + DWT + SVM, & & & \\ This work & 92.82 & 0.019444 & Helsinki \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative table of state-of-the-art approaches for epilepsy seizure detection.  
detection tasks, potentially extending its operation to real-time measurement devices, given the model's low computational complexity.