## 4 Experiments and Result Analysis  
### Evaluation Index and Algorithm  
The experiment was evaluated by a number of indicators, including accuracy, specificity, sensitivity, the receiver operating characteristic curve (ROC curve), and area under the ROC curve (AUC). The training process of GCN-LSTM is shown in Algorithm 1:  
```
0: A labeled training data set \((X,Y)=\{x_{i},y_{i}\}_{i=1}^{N}\), the maximum number of training epochs \(T\), the initial adjacency Matrix \(A\), regularization coefficient \(\alpha\)
0: The learned adjacency matrix A, the model parameter for GCN-LSTM and the predicted label y\({}^{\prime}\)
1: Initialize the model parameters in GCN-LSTM model. Set iteration number iter = 1
2:while iter \(\leq\) T do
3:for k = 1 do
4: Calculate the \(k\)-th graph convolutional layer \(H^{(k)}\) and calculate the \(k\)-th sum pooling layer Pool( H \({}^{(k)}\) )
5: Obtain the extracted features \(F_{g}\)
6:endfor
7:for k = 1 do
8: Calculate the k-th LSTM-based convolution layer \(C_{k}\)
9: Obtain the extracted features \(Fl\)
10:endfor
11: Concatenate the different layers of features \(F\), Namely \(Fg\) and \(Fl\), etc. 12: Calculate the prediction label y\({}^{\prime}\)
13: Update the adjacency matrix \(A\) and the model parameters via optimizer according entropy loss
14:\(i\)ter = iter + 1
15:endwhile \begin{table}
\begin{tabular}{c c c} \hline
**Layer** & **Shape** & **Parameter** \\ \hline Input & \(20\times[512]\) & / \\ Long- and short-term memory cells & \(20\times[512]\) & Number of features = 512 \\ Long- and short-term memory cells & \(20\times[512]\) & Number of features = 512 \\ Long- and short-term memory cells & \(20\times[512]\) & Number of features = 512 \\ Draw & [512] & Take the last moment feature \\ Full connection & [Number of categories] & Number of features = 2 \\ Output & [Number of categories] & Softmax \\ \hline \end{tabular}
\end{table} 
Table 2: Structure of long- and short-term memory neural networks. In this section, the effectiveness and superiority of the proposed model are evaluated on the dataset described in the experiments. Based on the epilepsy EEG dataset from previous studies, a large number of comparative experiments are conducted and the three evaluation indicators of accuracy (ACC), specificity (Spe), and sensitivity (Sen) are used to evaluate the model to verify our theory and the proposed method. The specific calculation method is as follows:

\[Acc=\frac{TP+TN}{TP+TN+FP+FN}\] \[Spe=\frac{TN+FP}{TN+FP}\] \[Sen=\frac{TP}{TP+FN}\]

where TP denotes positive samples predicted as positive; TN denotes negative samples predicted as negative; FP denotes negative samples predicted as positive; and FN denotes positive samples predicted as negative. ### Experimental Result and Comparison

In this experiment, we used binary classification and ternary classification methods, divided the dataset into a training set and a test set, and then used five-fold cross validation to divide the training set into k parts, one of which was used for validation each time, and the remaining k \(-\) 1 parts were used for training. The results of each fold are shown in Figure 9. The loss function on the test set was less than 0.051 after 175 iterations, as shown in Figure 10. The accuracy rates reached 99.39% and 98.69%, respectively. The ROC curve, also known as \"Recipient Operating Characteristic Curve\" or sensitivity curve, is mainly used to evaluate the accuracy of X in predicting Y. It was initially used in the military field and is now more commonly used in the medical field. The ROC curve reflects the relationship between sensitivity and specificity. The horizontal X-axis

Figure 10: The loss of each fold of the test set. Figure 9: Five-fold cross validation results for each fold. represents 1--specificity, also known as false positive rate (FPR). The closer it is to zero, the higher the accuracy is. The vertical Y-axis represents sensitivity, also known as true positive rate (TPR), and the larger it is, the better the accuracy is.