TY - JOUR
T1 - Improved sleep-wake and behavior discrimination using MEMS accelerometers
AU - Sunderam, Sridhar
AU - Chernyy, Nick
AU - Peixoto, Nathalia
AU - Mason, Jonathan P.
AU - Weinstein, Steven L.
AU - Schiff, Steven J.
AU - Gluckman, Bruce J.
N1 - Funding Information:
This work was funded by National Institutes of Health grants R01EB001507, K02MH01493 and R01MH50006.
PY - 2007/7/30
Y1 - 2007/7/30
N2 - 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.
AB - 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.
KW - Accelerometer
KW - Classification
KW - EEG
KW - MEMS
KW - REM
KW - Slow wave
KW - State
KW - Vigilance
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U2 - 10.1016/j.jneumeth.2007.03.007
DO - 10.1016/j.jneumeth.2007.03.007
M3 - Article
C2 - 17481736
AN - SCOPUS:34249887360
SN - 0165-0270
VL - 163
SP - 373
EP - 383
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 2
ER -