A Bio-Inspired Reservoir-Computer for Real-Time Stress Detection from ECG Signal

Sanjeev Tannirkulam Chandrasekaran, Sumukh Prashant Bhanushali, Imon Banerjee, Arindam Sanyal

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This letter presents the first on-chip bio-inspired reservoir computer (RC) prototype implemented in a 65-nm CMOS. The RC comprises 50 time-multiplexed neurons, and each neuron embeds a strong nonlinearity in a feedback loop. The RC applies a nonlinear transformation to the input and projects it to high-dimensional space, thus allowing linear separation by a simple logistic-regression (LR) layer implemented off-chip. We demonstrate real-time stress detection from electrocardiogram (ECG) signals using the RC. The RC achieves 93% classification accuracy which is 6% better than the state-of-the-art digital classifiers. Operating at 40 kHz, the prototype consumes 27.5 nJ/classification which is 7× lower than the state-of-the-art ECG processors performing similar complexity classification task.

Original languageEnglish (US)
Article number9169659
Pages (from-to)290-293
Number of pages4
JournalIEEE Solid-State Circuits Letters
Volume3
DOIs
StatePublished - 2020
Externally publishedYes

Keywords

  • Electrocardiogram (ECG) sensor
  • machine learning (ML)
  • reservoir computing
  • stress detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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