## 6 Experimental Results
[38] presented a hardware-oriented multiclass SVM system, achieving 93.9% accuracy and 94.7% recall, whereas [PERSON] et al. [39] utilized logistic model trees, with a performance of 95.33% accuracy and 95% recall. [PERSON] et al. [40] proposed a symlet wavelet-based pipeline combined with gradient boosting, reaching 96.5% accuracy and 95.8% recall. While these studies show strong performance, our model exceeds all in classification accuracy, while maintaining competitive recall. Our model outperforms this by leveraging a more comprehensive hybrid feature selection process and multiple classifiers (SVM, RF, DT, KNN), ultimately enhancing predictive performance. These comparisons collectively highlight the advancements introduced by our methodology, particularly through effective feature selection, dimensionality reduction, and data balancing techniques, leading to a more reliable and accurate seizure detection system. Our findings contribute to the ongoing advancements in EEG signal analysis and seizure detection technologies. The integration of SMOTE and hybrid feature selection techniques offers a robust framework for improving seizure detection accuracy. By enhancing model performance and reliability, our approach sets a new benchmark for future research and clinical applications in this field, as shown in Table 6. The improvements in accuracy and performance metrics have significant implications for clinical practice. Enhanced \begin{table}
\begin{tabular}{c c c} \hline \hline
**Citation** & **Accuracy** & **Recall** \\ \hline [PERSON] and & & \\ [PERSON] [32] & 86\% & 68\% \\ \hline [PERSON] et al. [33] & 88\% & 91.52\% \\ \hline [PERSON] et al. [38] & 93.9\% & 94.7\% \\ \hline [PERSON] et al. [39] & 95.33\% & 95\% \\ \hline [PERSON] et al. [40] & 96.5\% & 95.8\% \\ \hline
**Proposed Model** & **97.3\%** & **93.08\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of the proposed work against existing work from the literature. seizure detection systems can lead to more reliable and timely identification of seizures, contributing to better patient management and treatment outcomes. The robustness of SVM and RF in our study suggests that these models could be effectively deployed in clinical settings to assist in real-time seizure monitoring and diagnosis. While our study demonstrates promising results, there are some limitations. First, the proposed model was evaluated on a single, publicly available dataset, which contains pre-segmented and artifact-free EEG signals recorded under controlled conditions. This limits the ecological validity of the findings, as the model may not perform equally well on raw, noisy, or multi-channel EEG data collected in clinical settings. Additionally, the use of a single-channel signal restricts the ability to capture spatial features across brain regions, which are often informative in seizure detection. These constraints may affect the model's generalizability across different patient demographics, hardware configurations, and seizure types. Also, the current model does not yet support real-time processing or adaptive learning, which are essential for integration into wearable or bedside monitoring systems. Moreover, although the hybrid feature selection method improves accuracy, it may introduce computational overhead that limits scalability in low-power or embedded environments. Future work will address these concerns by validating the model on multi-centre, real-world EEG datasets, incorporating online learning or transfer learning strategies, and optimizing the pipeline for real-time, resource-constrained applications. Exploring hybrid architectures that integrate deep temporal models with traditional classifiers may also enhance robustness and interpretability in dynamic seizure detection scenarios. Future research will also focus on incorporating stratified k-fold cross-validation to enhance the robustness of model evaluation. This will allow us to assess performance variability across different data partitions and provide statistically grounded metrics.