# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 5 Results and Discussion
### Performance Analysis of Proposed Method  
Table 1 presents the comprehensive performance comparison of our proposed approach against existing state-of-the-art methods on both the CHB-MIT and Bonn datasets. Our method demonstrates exceptional performance across all metrics, achieving 98.7% accuracy on CHB-MIT and 99.2% accuracy on the Bonn dataset.  
The consistent superior performance across both datasets highlights the robustness of our dual-modality approach. Notably, our method achieves higher specificity (99.0% and 99.4% on CHB-MIT and Bonn, respectively) compared to sensitivity metrics, indicating its strong capability in reducing false positives--a crucial factor in clinical applications. The high AUC values (98.8% and 99.3%) further confirm the model's excellent discrimination ability across different operating thresholds.  
Our comprehensive performance analysis combines quantitative metrics with statistical validation and interpretability analysis. The confusion matrices in Figure 3 reveal several important patterns in our model's classification behavior. For Class 1, Class 2, and Class 3, the model achieves the highest accuracy (99.8%), demonstrating its reliability in identifying true seizure events. False-positive rates were less than 0.4% for all classes, which is particularly important for clinical applications because they minimize unnecessary interventions. Using the normal distribution approximation, the confidence interval for our 99% accuracy is calculated as [0.9961, 0.9979].  
Analysis of feature correlations (Figure 4) reveals strong complementarity between EEG and MTF representations. The diagonal patterns in the correlation matrices indicate that each modality captures unique aspects of the underlying neural activity, while the cross-correlation patterns demonstrate successful information integration through our SYNI mechanism.  
For clinical applicability, we conducted latency analysis showing that our model achieves an average inference time of 0.022 s per sample on standard hardware (NVIDIA RTX 4090), well within the requirements for real-time monitoring applications. Memory utilization remains stable at 1.7 GB during inference, making the system suitable for deployment in resource-constrained clinical settings.  
\begin{table}
\begin{tabular}{l l c c c c} \hline
**Dataset** & **Method** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUC (\%)** \\ \hline \multirow{8}{*}{CHB-MIT} & Conv1D+LSTM [51] & 91.2 & 90.8 & 91.5 & 91.3 \\  & Transformer [52] & 93.4 & 92.9 & 93.8 & 93.5 \\  & FedTransformer [53] & 94.1 & 93.7 & 94.4 & 94.2 \\  & HViT-DUL [54] & 94.0 & 93.8 & 94.0 & 93.6 \\  & TGCNN [55] & 92.8 & 92.2 & 93.7 & 93.5 \\  & EEGWaveNet [55] & 98.3 & 91.2 & 98.0 & 98.6 \\  & PCNN-RNN [56] & 96.9 & 92.2 & 97.6 & 97.1 \\  & ADML (Ours) & **98.7** & **98.3** & **99.0** & **98.8** \\ \hline \multirow{8}{*}{Bonn} & Conv1D+LSTM [51] & 92.5 & 91.8 & 93.1 & 92.7 \\  & Transformer [52] & 94.8 & 94.2 & 95.3 & 94.9 \\ \cline{1-1}  & FedTransformer [53] & 95.6 & 95.1 & 96.0 & 95.7 \\ \cline{1-1}  & HViT-DUL [54] & 94.9 & 94.1 & 94.3 & 95.1 \\ \cline{1-1}  & TGCNN [55] & 94.5 & 93.3 & 92.0 & 94.9 \\ \cline{1-1}  & EEGWaveNet [55] & 97.9 & 92.9 & 96.0 & 97.7 \\ \cline{1-1}  & PCNN-RNN [56] & 99.0 & 95.2 & 96.6 & 98.9 \\ \cline{1-1}  & ADML (Ours) & **99.7** & **98.9** & **99.4** & **99.3** \\ \hline \end{tabular} Note: All comparison models are reproduced with reference to corresponding papers or open-source code, and all models are tested using the same computing device. Bold values indicate the best performance in each metric.  
\end{table}
Table 1: Performance comparison on both datasets.  
Figure 4: _Cont_.  
Figure 3: Confusion matrix of test on Bonn.