  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 na\"ive Bayesian inference using M\"uller 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 $6.5 \mu W$ per channel, which
when compared to the state-of-the-art NeuroPace system would increase battery
life by up to $50 \%$.
