# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 5 Discussion
The performance of the proposed method is compared with previous studies based on the Helsinki database, as shown in Table 4. In this study, the seizure detection is conducted in a patient-specific manner; thus, a 10-fold cross-validation is performed separately in each  
Figure 5: Stacked bar chart of aggregation coefficients in MBGNN. Figure 6: Box plot of seizure detection metrics on 39 cases in different frequency bands. neonatal EEG case. The overall performance can be evaluated by the AUC value. Although most of the methods in the table achieve an AUC value higher than 90%, achieving an AUC value higher than 99% is quite a challenge. It should also be noted that the performance of seizure detection methods could be affected by several other factors besides the classification model. In previous studies of seizure detection, the length of EEG epochs ranges from 0.5 s to 32 s [4; 15; 19; 36; 37; 38]. A relatively short epoch (i.e., 0.5 s) [37] has difficulty providing useful EEG features of seizure states since plenty of low-frequency components are contained in scalp EEG, which may result in a lower PPV. On the other side, a long EEG epoch could provide sufficient information for classification but may result in long latency and low temporal precision in real-time EEG monitoring. In this study, the length of EEG epochs is set to 1 s, and the proposed seizure detection method achieves a relatively high PPV of 95.34%. The imbalance between seizure and non-seizure epochs is another factor that can affect the detection performance. Traditional classifiers may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. [PERSON] et al. [16] used a half-overlapping segmentation for seizure regions and a non-overlapping segmentation for non-seizure regions, as such doubling the total number of seizure epochs. They achieved a PPV of 96.7% and a NPV of 95.9%. In this study, a non-overlapping segmentation is used for both seizure and non-seizure regions. The separability of the data is enhanced by the multi-band feature extraction method and the band-wise attention mechanism, which effectively alleviates the imbalance problem. The PPV and NPV of the proposed method are 95.34% and 96.66%, respectively. The label quality also potentially influences the detection performance. The recordings in Helsinki database were manually annotated by three highly experienced clinical experts. [PERSON] et al. [16] proposed an ensemble model composed of three CNN models which were trained separately using the annotations of three experts. In the study of [PERSON] et al. [39], the EEG epoch was labelled as a seizure epoch if at least two experts agreed; otherwise, it was labelled as a non-seizure epoch. [PERSON] et al. [40] also used \"majority principle\" in the label processing and selected patients whose annotations are relatively consistent among the three experts. In this study, the EEG epoch is labelled as a seizure epoch if all the three experts agreed; otherwise, it is labelled as a non-seizure epoch. The labelling scheme in this study ensures the quality of the seizure sample set but may reduce the quality of the non-seizure sample set and decrease detection performance in a few cases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Subject** & **Method** & **Validation** & **AUC (\%)** \\ \hline \multirow{3}{*}{[PERSON] et al. [9]} & \multirow{3}{*}{79} & \multirow{3}{*}{SVM} & Leave-one-out & \multirow{3}{*}{95.7} \\  & & & cross-validation & \\ \cline{1-1}  & & & ROC analysis & - & 77.9 \\ \cline{1-1}  & & & & cross-validation & \\ \cline{1-1}  & & & cross-validation & 94.7 \\ \cline{1-1}  & & & cross-validation & 94.0 \\ \cline{1-1}  & & & cross-validation & 98.6 \\ \cline{1-1}  & & & cross-validation & 98.6 \\ \cline{1-1}  & & & cross-validation & 97.5 \\ \cline{1-1}  & & & cross-validation & 99.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different methods on the Helsinki database.