Theoretical Advancements in Seizure Prediction for Epilepsy Patients Using DTN and OFL

Millions of people worldwide suffer from epilepsy, a neurological condition that carries serious risks due to its unpredictable seizures (World Health Organization, 2023). Accurate seizure prediction can enhance patient safety by facilitating swift interventions. However, current models rely heavily on centralized processing and constant connectivity, which limits their practical efficiency, particularly in remote environments (Kuhlmann et al., 2021). This study proposes a theoretical framework that integrates Oblique Federated Learning (OFL) and Delay Tolerant Networking (DTN) to improve seizure prediction techniques. DTN uses a store-and-forward mechanism to prevent data loss and ensure smooth information transmission in low-connectivity environments (Farrell & Cahill, 2019). OFL allows distributed model updates without sharing private EEG data, enabling personalized learning on-device while protecting user privacy (Li et al., 2020). The framework ensures a scalable, energy-efficient, and privacy-preserving approach to wearable seizure prediction. Future research directions include clinical validation, optimization for energy efficiency, and mitigation of false positives.