Epilepsy is a long-term neurological condition marked by recurring seizures that affect a large population across the globe. Detecting seizures in real time is particularly challenging because electroencephalogram (EEG) signals exhibit highly dynamic, non-linear, and patient-specific behavior. Conventional machine learning and deep learning approaches, although effective in certain scenarios, often demand significant computational resources and power, which limits their applicability in continuous monitoring systems.

This study proposes a framework based on neuromorphic computing to overcome these challenges, enabling real-time detection of epileptic seizures through the use of spiking neural networks (SNNs). The proposed approach adopts an event-driven processing mechanism inspired by biological neural systems, allowing efficient handling of temporal EEG data. A spike encoding method is utilized to transform continuous EEG signals into distinct spike sequences, allowing them to be compatible with neuromorphic systems.

The proposed system is designed to enhance detection performance while reducing latency and energy consumption. By combining temporal signal representation with low-power computation, the framework offers a promising approach for applications in real-time healthcare. This study demonstrates how neuromorphic computing can contribute to the development of next-generation intelligent monitoring systems for neurological disorders.