## 4 Experiments and Result Analysis
### Evaluation Index and Algorithm
## 5 Conclusions

The method effectively extracts the spatial topological relationship of EEG electrodes by applying a GCN and trains the long-term temporal features of EEG data by subsequently applying LSTM. By segmenting the experimental data and training the model accordingly, our model achieved an average accuracy of 99.39% and 98.69% in binary and ternary classification, respectively, which is highly accurate in existing studies. This shows that our proposed GCN-LSTM model has a good effect on epileptic seizure prediction. Therefore, classifying or predicting the seizure or onset region of this type of disease through deep learning technology will promote the development of AI in the direction of personalized medicine [37], which also provides a direction for our future scientific research in this field. Although our model achieved very high scores in terms of AUC, we must note that the model was trained on limited samples. Specifically, the main reason for the small sample size is that we want to capture the characteristics of longer time segments in our study, so the length of each sample is 20s. In order to ensure that each sample can contain enough information, we chose a longer time segment, which resulted in a relatively small number of usable samples. Since no independent patient data were set aside as a test set, but all data were mixed, this may have affected the objectivity and generalization ability of the model evaluation to some extent. Although the current AUC results are encouraging, we still need to be cautious and increase the sample size in future studies and use independent patient data for testing to more accurately evaluate the performance and stability of the model. In addition, since the CHB-MIT dataset used in the proposed method focuses on pediatric patients, it is necessary to conduct extensive testing on EEG data from different sources, populations, or age groups. At the same time, under the premise of maintaining ethical standards, to fully realize the potential of machine learning in healthcare, it is necessary to follow ethical frameworks, continuous monitoring, multidisciplinary governance, education, and regulatory compliance [38]. With this premise, our experimental results and comparison with previous studies show that the proposed method is effective and reliable. Figure 12: Visualization of feature importance. **Author Contributions:** Conceptualization, Z.K. ; methodology, S.L. ; software, S.L. ; validation, J.Z. ; formal analysis, Y.L. ; investigation, L.W. ; resources, J.Z. ; data curation, Z.K. ; writing--original draft preparation, S.L. ; writing--review and editing, Z.K. ; visualization, Y.L. ; supervision, Z.K. ; project administration, L.W. ; funding acquisition, Z.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Natural Science Foundation of Jilin Province (20230401092 YY), Science and Technology Research Project of Jilin Provincial Department of Education (JKH20230680 KJ) and Jilin Provincial Department of Science and Technology (YDZJ202303 CGZH010). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable, a publicly available database was used. **Data Availability Statement:** The data used in this study are available in open access on Physionet. The download link is [[https://physionet.org/content/chbmit/1.0.0/](https://physionet.org/content/chbmit/1.0.0/)]([https://physionet.org/content/chbmit/1.0.0/](https://physionet.org/content/chbmit/1.0.0/)) (accessed on 1 January 2023). **Conflicts of Interest:** The authors declare no conflicts of interest. ## References

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