## 4 Proposed Epileptic Seizures Detection Model
### Feature Selection Techniques
#### 4.3.3 Hybrid Feature Selection Approach (PCA + DWT)  
To leverage the strengths of both PCA and DWT, we introduced a hybrid feature selection approach by merging PCA-transformed features with selected DWT coefficients. This hybrid approach aims to harness the dimensionality reduction capabilities of PCA and the detailed signal feature extraction of DWT. By combining these methods, we aim to create a robust feature set that maximizes the informative content while minimizing redundancy and noise. This dual strategy is designed to enhance the predictive accuracy and efficiency of the seizure detection model.  
The selection of PCA and DWT, both individually and in combination, is based on their complementary strengths and established effectiveness in previous research. PCA's ability to reduce dimensionality and focus on the most significant components complements DWT's capability to capture essential time-frequency features of EEG signals. This synergyis crucial for creating a model that is not only accurate but also efficient and scalable. Moreover, the hybrid approach addresses the limitations of using either technique in isolation, providing a comprehensive feature set that improves model robustness. In summary, the application of PCA and DWT in our feature selection process is a strategic choice aimed at optimizing the performance of our ML models. By systematically reducing dimensionality and extracting key signal features, we enhance the model's ability to accurately detect epileptic seizures, ultimately contributing to better clinical outcomes and improved quality of life for individuals with epilepsy. This is shown in Figure 5.  
The DWT was applied to each 1-s EEG segment (178 data points) to extract meaningful time-frequency domain features that capture transient patterns characteristic of epileptic activity. The DB4 wavelet was selected due to its effectiveness in EEG signal analysis, particularly in identifying seizure-related features. DWT was implemented using three levels of decomposition, which allowed the signal to be separated into approximation and detail coefficients at different frequency bands. The decomposition levels were chosen based on the length of the signal and the sampling frequency (173.61 Hz), ensuring that the frequency bands covered typical EEG rhythms (delta to beta); details are shown in Table 3.  
Figure 5: Pseudocode for preprocessing, dataset splitting, and feature selection techniques.  
To support the hybrid use of PCA and DWT, we emphasize that each technique captures complementary aspects of EEG signals. DWT is well-suited for analyzing non-stationary signals by decomposing them into time-frequency components, enabling the extraction of transient features associated with seizure onset and progression. However, the resulting coefficient set may contain redundancy and noise, especially when multiple decomposition levels are used. PCA, as a linear dimensionality reduction method, is then applied not to extract new features but to refine the DWT-derived features by removing collinear and low-variance components. This enhances the discriminative quality of the feature space while retaining dominant seizure-related patterns.  
Although PCA is a global transformation and does not preserve time-localized structure in raw signals, its application after DWT is beneficial because the DWT step has already captured localized features across different frequency bands. The PCA transformation then acts as a post-processing filter that emphasizes variance-rich patterns while reducing dimensionality and noise. Empirically, our results show that the hybrid approach outperforms either method alone in terms of classification accuracy, precision, and recall. This indicates that PCA and DWT are synergistic rather than conflicting in this context, where PCA acts as a feature refiner rather than a suppressor of time-frequency information.