# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 6 Limitations and Future Work
### Future Research Directions  
Looking forward, several promising research directions emerge from our current limitations. A primary focus should be the enhancement of data integration capabilities. Future research should explore the incorporation of additional physiological signals and contextual information, including environmental factors, patient-specific metadata, and circadian rhythm patterns. This expanded data integration would provide a more comprehensive understanding of seizure precursors and potentially improve prediction accuracy.  
Model optimization represents another critical area for future work. Research efforts should focus on developing more efficient attention mechanisms specifically designed for EEG signal processing and investigating lightweight model architectures suitable for edge device deployment. These optimizations would address the current computational limitations while maintaining or improving prediction performance.  
Clinical integration presents both challenges and opportunities for future research. The development of real-time monitoring systems that seamlessly integrate our prediction model into existing healthcare workflows requires careful consideration of user interface design, alert systems, and clinical validation across diverse patient populations. This integration must balance technical capabilities with practical clinical requirements and constraints. For clinical applicability, we conducted comprehensive latency and memory analysis across different numerical precision settings. With FP32 precision, our model achieves an average inference time of 0.022 s per sample on standard hardware (NVIDIA RTX 4090), with memory utilization stable at 1.7 GB during inference. When quantized to INT8 precision, the memory utilization would reduce to approximately 0.43 GB (a 75% reduction), while INT4 quantization would further reduce the memory footprint to around 0.21 GB (an 88% reduction). These substantial memory reductions through quantization, combined with the model's efficient inference time, make the system particularly suitable for deployment in resource-constrained clinical settings, including potential edge devices for continuous monitoring.  
The extension of prediction capabilities represents a significant opportunity for advancement. Future research should focus on extending the prediction horizon while maintaining high accuracy, potentially through the development of hierarchical prediction models that combine short-term and long-term forecasting capabilities. Additionally, the incorporation of seizure severity prediction alongside occurrence prediction would enhance the clinical utility of the system.  
Privacy and security considerations will continue to be crucial as these systems evolve. Future research must address the development of privacy-preserving training methods for sensitive medical data and secure data sharing protocols for collaborative model training. These advances must maintain compliance with healthcare regulations while enabling effective model development and deployment.  
The long-term vision for this research is the development of a comprehensive epilepsy management system that provides accurate, real-time seizure prediction while operating efficiently on portable devices and integrating seamlessly with existing healthcare systems. This system should maintain patient privacy and data security while adapting to individual patient characteristics and needs. Achieving this vision will require continued collaboration between computer science researchers, medical professionals, and healthcare technology developers, working together to translate theoretical advances into practical clinical solutions.