## 4 Experimental Analysis
### Experimental Setup
#### 4.2.2 Experimental Results  
This sub-section presents a detailed discussion of the RNN-based experiments carried out in different scenarios as well as a comparison of the results of the proposed model with traditional classification models such as ANN and SVM. The results have also been compared with other classification models available in the literature. Figure 7 illustrates the results for our classification model in scenario 1 using five frequency sub-bands. It can be noted that the lower frequency sub-bands, such as delta (0-4 Hz), theta (4-8 Hz, and alpha (8-15 Hz), contain more discriminating features for epileptic seizure classification as compared with the higher frequency sub-bands. The proposed model yielded more than 70% accuracy on all of the frequency sub-bands; however, the delta band (0-4 Hz) showed the highest accuracy of 83.63%, as tabulated in Table 5.  
Figure 6: Decomposition of RAW EEG data using DWT. (**a**) EEG data labelled “no-seizure”; (**b**) EEG data labelled “seizure”. Figure 7: RNN classification accuracy based on training/testing ratios and frequency bands (CHB-MIT dataset). The BONN dataset contains comparatively simple data, with only one EEG channel of intracranial signals; therefore, our RNN-based model converges relatively quickly on the BONN dataset even with a lower learning rate (0.000001-0.001). The confusion matrix presented in Figure 8 reveals that the RNN+DWT approach achieved an accuracy of 99.30% in classifying seizures on the BONN dataset while the false positive rate remained below 1%. The results were obtained with a learning rate of 0.001 and a training/testing ratio of 70%/30%. Overall, the RNN-based model achieved an accuracy of 99.84% and 99.77% with DWT and FFT, respectively. In Figure 9a, it can be noted that the FFT-based RNN model achieved better accuracy with a lower learning rate; however, when the learning rate was increased, the DWT-based RNN model showed better results, as illustrated in Figure 9b. A comparison of the results obtained from RNN-based classification model on the BONN dataset is presented in Table 6. The results show that the RNN-based epilepsy classification model performed better with DWT as compared with FFT. The proposed model also demonstrated better results when compared with the latest research found in the literature, as shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline
**Frequency Sub-Band** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Gamma-Dc-1 (30–60 Hz) & 72.86\% & 55.73\% & 90.22\% \\ Beta-Dc-2 (15–30 Hz) & 73.18\% & 57.09\% & 89.46\% \\ Alpha-Dc-3 (8–15 Hz) & 79.10\% & 67.21\% & 91.11\% \\ Theta-Dc-4 (4–8 Hz) & 81.09\% & 70.51\% & 91.82\% \\ Delta-Ac-4 (0–4 Hz) & 83.63\% & 79.38\% & 87.86\% \\ \hline \end{tabular}
\end{table} 
Table 5: Performance of RNN-based classification model on frequency sub-bands. Figure 8: Confusion matrix: RNN+DWT performance-based BONN dataset with 70% train ratio and 0.001 LR. The accuracy of the proposed RNN-based classification model exhibited a comparable trend on both the CHB-MIT and BONN datasets. The RNN-based model coupled with DWT in the feature extraction pre-processing phase demonstrated a gradual increase in accuracy with each increment in the LR, as depicted in Figure 9(b), while the accuracy with FFT remained steady for the initial increments in the LR. Based on the confusion matrix illustrated in Figure 11, it can be observed that the CHB-MIT dataset, with a training and testing ratio of 30-70% and a learning rate of 0.05, identifies 91% of seizure segments accurately while maintaining a false positive rate of 9.1%. Overall, the DWT-based RNN model achieved better performance on both datasets. Table 7 provides a comparison of the results obtained from the proposed RNN-based classification model on the CHB-MIT dataset. The results indicate that the proposed RNN-based model performed with an accuracy of 93.27% as compared with ANN and SVM, which achieved an accuracy of 86.10% and 90.68% respectively.