Analog Seizure Detection for Implanted Responsive Neurostimulation

TL;DR

This paper introduces a patient-specific analog seizure detection system using naive Bayesian inference with Muller C-elements, achieving high accuracy and reduced power consumption for implanted neurostimulation devices.

Contribution

It presents a novel analog signal processing approach for seizure detection that improves accuracy and power efficiency over existing digital systems.

Findings

Achieves up to 98% detection accuracy in simulations

Uses only 6.5 μW per channel, increasing battery life by 50%

Demonstrates effectiveness of multi-channel feature integration

Abstract

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 $50 \%$.