RRAM-Based Bio-Inspired Circuits for Mobile Epileptic Correlation Extraction and Seizure Prediction

TL;DR

This paper introduces a RRAM-based bio-inspired circuit system for seizure prediction from EEG data, achieving high sensitivity, low false positives, and significantly reduced energy consumption, suitable for mobile applications.

Contribution

It presents a novel RRAM-based bio-inspired circuit for seizure prediction that greatly improves energy efficiency and performance over existing methods.

Findings

Sensitivity of 91.2% on seizure detection

False positive rate of 0.11 FPR/h

81.3% reduction in energy consumption

Abstract

Non-invasive mobile electroencephalography (EEG) acquisition systems have been utilized for long-term monitoring of seizures, yet they suffer from limited battery life. Resistive random access memory (RRAM) is widely used in computing-in-memory(CIM) systems, which offers an ideal platform for reducing the computational energy consumption of seizure prediction algorithms, potentially solving the endurance issues of mobile EEG systems. To address this challenge, inspired by neuronal mechanisms, we propose a RRAM-based bio-inspired circuit system for correlation feature extraction and seizure prediction. This system achieves a high average sensitivity of 91.2% and a low false positive rate per hour (FPR/h) of 0.11 on the CHB-MIT seizure dataset. The chip under simulation demonstrates an area of approximately 0.83 mm2 and a latency of 62.2 {\mu}s. Power consumption is recorded at 24.4 mW during the feature extraction phase and 19.01 mW in the seizure prediction phase, with a cumulative energy consumption of 1.515 {\mu}J for a 3-second window data processing, predicting 29.2 minutes ahead. This method exhibits an 81.3% reduction in computational energy relative to the most efficient existing seizure prediction approaches, establishing a new benchmark for energy efficiency.