Epilepsy can be treated with medication, however, 30% of epileptic patients are still drug resistive. Devices like responsive neurostimluation systems are implanted in select patients who may not be amenable to surgical resection. However, state-of-the-art devices suffer from low accuracy and high sensitivity. We propose a novel patient-specific seizure detection system based on naıve Bayesian inference using Muller C-elements. The system improves upon the current leading neurostimulation device, NeuroPace’s RNS® by implementing analog signal processing for feature extraction, minimizing the power consumption compared to the digital counterpart. Preliminary simulations were performed in MATLAB, demonstrating
that through integrating multiple channels and features, up to 98% detection accuracy for individual patients can be achieved. Similarly, power calculations were performed, demonstrating that the system uses 6:5W per channel, which when compared to the state-of-the-art NeuroPace system would increase battery life by up to 50%.