Neuro-Fuzzy Reinforcement Learning for Personalized Seizure Warning System on Edge Devices

Predicting epileptic seizures accurately and in real-time remains a major challenge, especially when the solution needs to work on resource-limited, wearable devices. In this work, we introduce a new approach called Neuro-Fuzzy Reinforcement Learning (NFRL), which combines three powerful techniques to make seizure alerts more intelligent and personalized. A lightweight CNN model first estimates the likelihood of a seizure from EEG data. Then, a fuzzy logic module interprets this output to assess the confidence level in a more humanunderstandable way. Finally, a reinforcement learning agent adjusts when and how to trigger alerts based on ongoing user feedback and individual seizure history. This layered design not only improves accuracy but also helps reduce unnecessary alerts. Designed to run efficiently on small devices like Raspberry Pi 4 and Coral Edge TPU, the system was tested on the CHB-MIT EEG dataset and showed strong results 95.2% accuracy and false alarm at a very low rate of 0.62 per hour made it a practical real-time option for device seizure prediction.