A Molecular Communication Channel Model for Early Epileptic Seizure Detection via Synaptic Glutamate Dynamics

Molecular communication (MC) is a bio-inspired paradigm that models information transfer through molecules and provides an approach to analyze biological processes. Positioning neurological signaling within this framework enables a communication-theoretic view of disorders such as epilepsy. Epileptic seizures originate from microscopic neurotransmitter dynamics. Current prediction methods rely on EEG, which captures only macroscopic activity and often misses early preictal buildup. We propose an MC framework that models microscale synaptic glutamate signaling as a channel shaped by diffusion, clearance, and finite-receptor binding. Our Partial Differential Equations (PDEs) and Chemical Master Equation (CME) combined model yields impulse response and validates stochastic binding. Further analysis shows how synaptic parameters influence detection dynamics. Framing seizure prediction as a threshold-crossing detection problem aligns with latency and reliability metrics in communication theory, positioning MC as a promising framework for next-generation molecular-level seizure detection.