## 4 Result  
We utilized the CHB-MIT Scalp EEG database, which contains recordings from 24 pediatric patients. From this dataset, a total of 827 usable EEG signal segments were extracted following initial preprocessing, which included  
* Removing segments with missing or corrupted values. * Normalizing signal amplitudes using z-score normalization to ensure a standardized range. * Truncating or zero-padding signals to a fixed length to maintain uniform input dimensions. To simulate a realistic prediction scenario, the dataset was split chronologically based on recording times. This strategy ensures that the test data reflects unseen future samples, reducing data leakage and offering a more accurate measure of generalization. The data was divided as follows:  
* **289 signals** were assigned to the test set. * **539 signals** were used for training and validation. This results in a test set comprising approximately 35% of the entire dataset. To enhance the robustness of the evaluation, the model was trained and assessed over 5 independent runs, each with different random seeds and data shuffling. Table 1 presents a comparison of several deep learning models applied to EEG signal classification, each utilizing distinct preprocessing pipelines. **AUC-ROC (area under the receiver operating characteristic curve)** quantifies the model's ability to distinguish between preictal (pre-seizure) and interictal (non-seizure) states across all possible classification thresholds. An AUC of 1.0 indicates perfect discrimination, whereas an AUC of 0.5 suggests a performance equivalent to random guessing. This metric is particularly important in medical diagnosis tasks such as epilepsy prediction, where both false positives and false negatives can have significant consequences. A high AUC signifies that the model is effective at ranking true preictal events above interictal ones, which is vital for providing timely and accurate seizure warnings. **FPR (false positive rate)** refers to the proportion of interictal (non-seizure) segments that are incorrectly classified as preictal. In the context of epilepsy prediction, maintaining a low FPR is crucial, as frequent false alarms can cause undue stress, trigger unnecessary interventions, and negatively impact the patient's quality of life. This is especially important in real-time or wearable monitoring systems, where reliability and user trust are paramount. As shown in Figure 4, the plot illustrates the classification accuracy of various seizure prediction and detection models. The x axis represents different models along with their corresponding preprocessing techniques, while the y axis indicates accuracy values ranging from 93% to 100%. This visualization enables a direct comparison of model performance in terms of classification accuracy. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Preprocessing** & **Deep Learning** & **Predict Time** & **Accuracy** & **AUC-ROC** & **FPR** \\ \hline Raw Data & CapsNet & 30 & 95/7 & – & 0/127 \\ \hline DWT & SVM & 25/1 & 96/38 & – & 0/19 \\ \hline DWT & Hybrid AE+CNN & – & 94/54 & 0/9215 & – \\ \hline STFT & Transformer based & 10 & 94/6 & 0/989 & – \\ \hline DWT & FNN & 5 & 98/96 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of EEG classification models with different preprocessing and deep learning techniques. Figure 5 presents a line graph comparing the AUC-ROC scores of several seizure prediction or detection models, providing insight into their discriminative power. Higher AUC values (closer to 1.0) indicate better performance. The y axis ranges from 0.88 to 1.02, focusing on high-performing models, while the x axis lists the models along with their EEG preprocessing methods: CapsNet (Raw Data), SVM (DWT), Hybrid AE + CNN (DWT), Transformer based (STFT), and FNN (DWT). The curve begins at approximately 0.92 for the Hybrid AE + CNN model, rises to about 0.98 for the Transformer-based model, and peaks near 1.0 for the FNN model, indicating the highest classification performance.