Publication Date
5-18-2026
Document Type
Article
Publication Title
Intelligent Systems with Applications
Volume
31
DOI
10.1016/j.iswa.2026.200675
Abstract
Epileptic seizures are one of the most common neurological disorders worldwide, yet accurate prediction remains a major challenge to date. Existing techniques largely rely on either electroencephalography (EEG) or neuroimaging and use a one-size-fits-all paradigm with limited personalization. Wearable physiological signals such as heart rate (HR), electrodermal activity (EDA), and skin temperature (TEMP) provide a non-invasive, real-time monitoring solution, whose seizure prediction potential is yet to be comprehensively explored. This work proposes a proof-of-concept patient-specific seizure risk forecasting model that integrates a Transformer encoder with a Proximal Policy Optimization (PPO) reinforcement learning agent. The system is trained from 30-second segments of wearable data, fusing clinical priors with patient-specific percentiles through an α-blended thresholding and dynamic reward mechanism. Performance is evaluated using both standard frame-level metrics (e.g., precision, recall, F1) and clinically motivated episode-level metrics (e.g., true-positive and false-alarm rates, total episodes per hour). Across ablation studies, we build upon conventional machine learning baselines that over-fit rule-based labels and fail to capture temporal structure. Occlusion-based explanation also evidences that the agent prefers clinically important feature combinations (HR+EDA, HR+TEMP). Our results indicate the promise of reinforcement learning from evolving wearable signals for proactive and individualized seizure prediction. We conclude by emphasizing the limitations of simulated data use and point toward future validation on actual patient datasets.
Keywords
Epilepsy, Explainable AI, Personalization, Physiological signals, Reinforcement learning, Seizure prediction, Transformer encoder, Wearable sensors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Applied Data Science
Recommended Citation
Mayank Kapadia and Vishnu S. Pendyala. "Personalized Seizure Risk Forecasting via Transformer-Enhanced Reinforcement Learning With Wearable Signals" Intelligent Systems with Applications (2026). https://doi.org/10.1016/j.iswa.2026.200675
