A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system

Naveen Verma, Ali Shoeb, Jose Bohorquez, Joel Dawson, John Guttag, Anantha P. Chandrakasan

Research output: Contribution to journalArticlepeer-review

343 Scopus citations

Abstract

This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chronic treatment system. The SoC integrates an instrumentation amplifier, ADC, and digital processor that streams features-vectors to a central device where seizure detection is performed via a machine-learning classifier. The instrumentation-amplifier uses chopper-stabilization in a topology that achieves high input-impedance and rejects large electrode-offsets while operating at 1 V; the ADC employs power-gating for low energy-per-conversion while using static-biasing for comparator precision; the EEG feature extraction processor employs low-power hardware whose parameters are determined through validation via patient data. The integration of sensing and local processing lowers system power by 14x by reducing the rate of wireless EEG data transmission. Feature vectors are derived at a rate of 0.5 Hz, and the complete one-channel SoC operates from a 1 V supply, consuming 9 μ J per feature vector.

Original languageEnglish (US)
Article number5437484
Pages (from-to)804-816
Number of pages13
JournalIEEE Journal of Solid-State Circuits
Volume45
Issue number4
DOIs
StatePublished - Apr 2010

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • 1/f noise
  • Algorithm design and analysis
  • Amplifiers
  • Biomedical equipment
  • Brain
  • Choppers
  • Digital signal processing
  • Electroencephalography
  • Low-noise amplifiers
  • Low-power electronics

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