## 4 Experiments and Result Analysis
### Evaluation Index and Algorithm
According to the position of the curve, the whole graph is divided into two parts, with the area under the curve (AUC) in the lower part representing the prediction accuracy. The higher the AUC value is, the larger the area under the curve is. The larger the area is, the larger the true positive is and the smaller the false positive is, which indicates a higher prediction accuracy. The closer the curve is to the upper left corner (with smaller X and larger Y), the higher the prediction accuracy is. The ROC of each fold of the five-fold cross validation is shown in Figure 11.

In addition, we analyze in detail the impact of different hyperparameters (such as learning rate, number of hidden layers, or graph convolution depth) on model performance. As shown in Table 3.

In medical applications, especially in epilepsy detection, understanding which factors contribute to prediction is crucial for clinicians to trust and adopt the model. Therefore, we use heatmaps to visualize feature importance, as shown in \[Acc=\frac{TP+TN}{TP+TN+FP+FN}\]0

We analyze the features in order. Lines 1-128 are \(F^{psd}\), lines 129-255 are \(F^{cut}\), lines 256, 257, 258, 259, and 260 are the time features (\(x^{avg}\), \(x^{avg}\), \(x^{std}\), \(x^{p-p}\), \(x^{cross}\)), lines 261-265 are \(F^{psdband}\), and the last five lines represent \(F^{curbband}\). The results show that power spectral density features, corrected mean, and standard deviation have a significant impact on epilepsy prediction. These findings provide an important reference for further optimizing epilepsy prediction models. On the CHB dataset, we compared the performance of various models in epilepsy binary classification, where the binary classification refers to the division of epileptic EEG into seizures and preictal periods, and the ternary classification refers to interictal, preictal, and ictal periods. In addition, we used the accuracy (Acc), specificity (Spe), and sensitivity (Sen) commonly used in machine learning as three indicators to evaluate model performance. The comparison with some models is shown in \[Acc=\frac{TP+TN}{TP+TN+FP+FN}\]1

\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Learning Rate** & **Hidden** & **GCN Layer** & **val\_Accuracy (\%)** & **val\_Sensitivity (\%)** & **val\_Specificity (\%)** \\ \hline \multirow{8}{*}{0.002} & 1 & 94.58 & 96.26 & 92.96 \\  & 800 & 2 & 94.40 & 94.72 & 94.09 \\  & 3 & 95.10 & 93.92 & 96.84 \\  & 1 & 94.58 & 96.26 & 92.96 \\  & 1000 & 2 & 95.37 & 93.55 & 97.14 \\  & 3 & 94.04 & 91.71 & 96.30 \\  & 1 & 9 As can be seen from Table 4, the accuracy of our model is significantly improved compared with other studies in the same field. The algorithm is compared with various algorithms on the CHB-MIT dataset and achieves an average accuracy of 99.39% and 98.69% in binary and ternary classification of CHB-MIT, respectively. In addition, high accuracy is achieved in most channels, which shows that our model has a high classification ability. The experiments we conducted showed that the actual running time of the model is 145 min, and the number of parameters is 3.776 M. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Work** & **Year** & **Method** & **Acc/\%** & **Spe/\%** & **Sen/\%** \\ \hline [PERSON] et al. [18] & 2018 & SAE(2) & 96.6 & - & - \\ [PERSON] et al. [33] & 2018 & Bi-LSTM(2) & 87.8 & 87 & 88.63 \\ [PERSON] et al. [21] & 2018 & AE-CDNN(2) & 91 & - & - \\ [PERSON] et al. [34] & 2019 & PGN-CNN(2) & - & 98.7 & 61 \\ [PERSON] et al. [35] & 2019 & SCNN(3) & 98.62 & - & - \\ [PERSON] et al. [36] & 2020 & E-GCN(2) & 98.35 & - & - \\ Ours(2) & - & GCN-LSTM(2) & 99.39 & - & - \\ Ours(3) & - & GCN-LSTM(3) & 98.69 & 99.12 & 95.72 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental performance of different frames on CHB-MIT dataset [32] (The numbers in parentheses represent the number of categories). Figure 11: ROC for each fold.