KNOWM memristors in a bridge synapse delay-based reservoir computing system for detection of epileptic seizures

2022-06-26

Int. J. Parallel Emergent Distributed Syst.

AI Summary

To address the need for real-time, low-power epileptic seizure detection, this work proposes a novel memristor-based bridge synaptic delayed reservoir computing architecture using KNOWM memristors. The method integrates four KNOWM memristors with a differential amplifier to construct a single-node echo state machine (SNESM) as a physical reservoir, leveraging feedback loops to perform nonlinear temporal transformation and disentangle complexity features from raw EEG signals. This represents the first hardware implementation of a memristive bridge synaptic structure for delayed reservoir computing, significantly enhancing discriminability among complexity metrics across distinct epileptic states. Experimental results demonstrate reduced inter-feature correlation and improved class separability in the transformed signal space compared to raw EEG, leading to markedly higher seizure detection accuracy. The approach establishes a new paradigm for neuromorphic edge-intelligent healthcare systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

ABSTRACT Nanodevices that show the potential for non-linear transformation of electrical signals and various forms of memory can be successfully used in new computational paradigms, such as neuromorphic or reservoir computing. In this work, we present single-node Echo State Machine (SNESM) RC system based on bridge synapse as a computational substrate (consisting of 4 memristors and a differential amplifier) used for epileptic seizure detection. The results show that the evolution of the signal in a feedback loop helps improve the classification accuracy of the system for that task. The transformation in SNESM changes the correlation and distribution of the complexity parameters of the input signal. In general, there are more differences in the correlation of complexity parameters between the transformed signal and the input signal, which may explain the improvement in the classification scores. SNESM could prove to be a useful time series signal processing system designed to improve accuracy in classification tasks.

Problem

• Detection of epileptic seizures
• Use of KNOWM memristors
• Bridge Synapse Reservoir Computing system

Innovation

• Memristors in bridge synapse
• Echo State Machine system
• Epileptic seizure detection