# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 5 Discussion  
In this paper, a novel graph-based method is proposed for the automated detection of neonatal seizures. Compared with existing graph-based methods, the emphasis of this work is on the graph embedding and the graph aggregation of different frequency bands. Specifically, the GAT layer and the DIFFPOOL layer are serially stacked as the front end of the MBGNN model for the graph embedding. To visualize the effectiveness of the graph embedding, the input and the output data of the front end of MBGNN are extracted and transformed into 2D maps using the t-distributed stochastic neighbor embedding(t-SNE) method [35], respectively, as shown in Figure 4. The red and blue scattered points stand  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **AUC(\%)** & **PPV(\%)** & **NPV(\%)** \\ \hline
1 & 99.10 \(\pm\) 0.13 & 94.90 \(\pm\)  
for the data of seizure and non-seizure samples, respectively. The distribution of scattered points in 2D maps visually reveals the separability of seizure and non-seizure samples before and after the graph embedding in different frequency bands. As we can see, the seizure samples and the non-seizure samples of ID 21 are highly overlapped in the input maps of all sub-bands. After the graph embedding of the front end in MBGNN, the distribution of the output data has changed and varies across different sub-bands. The seizure samples and the non-seizure samples are significantly separated in sub-band 1, sub-band 2, and sub-band 3. However, there is still a wide overlapping range in the output maps of sub-band 4, sub-band 5, and sub-band 6. This indicates that the epileptic neuron disorder could be detected in particular frequency bands. Additionally, the frequency bands with abnormalities may differ among patients. Therefore, it is crucial to utilize the information from various frequency bands in a comprehensive and adaptive manner. In the proposed MBGNN model, the band-wise attention layer can innovatively handle the graph representations from different frequency bands. The output data of the front end are adaptively aggregated by the aggregation coefficients so that the data with better classification potential can be emphasized. The aggregation coefficients of 39 cases are given in Figure 5. As shown in the figure, the aggregation coefficients of six sub-bands vary across 39 cases, exhibiting varying degrees of contribution to the final classification. A higher aggregation coefficient, represented by a bigger proportion of the bar, indicates a stronger impact of the corresponding sub-band on the final decision. For ID 21, the aggregation coefficients of sub-band 1, sub-band 2, and sub-band 3 are significantly higher than those of the other sub-bands, which is consistent with the visualization result  
Figure 4: Two-dimensional map of graph embeddings in MBGNN for ID 21.  
in Figure 4, where the distribution of seizure and non-seizure samples shows higher separability in these three sub-bands. Seizure detection metrics in different frequency bands are calculated based on the single-channel version of MBGNN, which only analyzes the EEG graphs from a single band and removes the band-wise attention layer. The mean AUC, PPV, and NPV of 10-fold cross validation on 39 cases are presented as a box plot in Figure 6. As shown in the figure, the mean AUC, PPV, and NPV of 39 cases are relatively high in sub-band 5 (20-26 Hz) and sub-band 6 (26-32 Hz), with a smaller variance across different cases. The comparison results show that the high half range of the Beta band are most indicative of epileptic EEG in neonates. However, the accuracy and reliability of seizure detection using a single frequency band are significantly inferior to that of multiple frequency bands, which verifies the necessity of adaptive feature fusion of different frequency bands.