Improved sleep-wake and behavior discrimination using MEMS accelerometers

Sridhar Sunderam, Nick Chernyy, Nathalia Peixoto, Jonathan P. Mason, Steven L. Weinstein, Steven J. Schiff, Bruce J. Gluckman

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

State of vigilance is determined by behavioral observations and electrophysiological activity. Here, we improve automatic state of vigilance discrimination by combining head acceleration with EEG measures. We incorporated biaxial dc-sensitive microelectromechanical system (MEMS) accelerometers into head-mounted preamplifiers in rodents. Epochs (15 s) of behavioral video and EEG data formed training sets for the following states: Slow Wave Sleep, Rapid Eye Movement Sleep, Quiet Wakefulness, Feeding or Grooming, and Exploration. Multivariate linear discriminant analysis of EEG features with and without accelerometer features was used to classify behavioral state. A broad selection of EEG feature sets based on recent literature on state discrimination in rodents was tested. In all cases, inclusion of head acceleration significantly improved the discriminative capability. Our approach offers a novel methodology for determining the behavioral context of EEG in real time, and has potential application in automatic sleep-wake staging and in neural prosthetic applications for movement disorders and epileptic seizures.

Original languageEnglish
Pages (from-to)373-383
Number of pages11
JournalJournal of Neuroscience Methods
Volume163
Issue number2
DOIs
StatePublished - Jul 30 2007

Bibliographical note

Funding Information:
This work was funded by National Institutes of Health grants R01EB001507, K02MH01493 and R01MH50006.

Keywords

  • Accelerometer
  • Classification
  • EEG
  • MEMS
  • REM
  • Slow wave
  • State
  • Vigilance

ASJC Scopus subject areas

  • Neuroscience (all)

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