Seizure detection using dynamic memristor-based reservoir computing and leaky integrate-and-fire neuron for post-processing

Abstract

Epilepsy is a prevalent neurological disorder, rendering the development of automated seizure detection systems imperative. While complex machine learning models are powerful, their training and hardware deployment remain challenging. The reservoir computing system offers a low-cost solution in terms of both hardware requirements and training. In this paper, we introduce a compact reservoir computing system for seizure detection, based on the α-In2Se3 dynamic memristors. Leaky integrate-and-fire neurons are used for post-processing the output of the system, and experimental results indicate their effectiveness in suppressing erroneous outputs, where both accuracy and specificity are enhanced by over 2.5%. The optimized compact reservoir system achieves 96.40% accuracy, 86.34% sensitivity, and 96.56% specificity in seizure detection tasks. This work demonstrates the feasibility of using reservoir computing for seizure detection and shows its potential for future application in extreme edge devices.