Sleep-stage scoring in the rat using a support vector machine

Abstract

Analysis and classification of sleep stages is a fundamental part of basic sleep research. Rat sleep stages are scored based on electrocorticographic (ECoG) signals recorded from electrodes implanted epidurally and electromyographic (EMG) signals from the temporalis or nuchal muscle. An automated sleep scoring system was developed using a support vector machine (SVM) to discriminate among waking, nonrapid eye movement sleep, and paradoxical sleep. Two experts scored retrospective data obtained from six Sprague-Dawley rodents to provide the training sets and subsequent comparison data used to assess the effectiveness of the SVM. Numerous time-domain and frequency-domain features were extracted for each epoch and selectively reduced using statistical analyses. The SVM kernel function was chosen to be a Gaussian radial basis function and kernel parameters were varied to examine the effectiveness of optimization methods. Tests indicated that a common set of features could be chosen resulted in an overall agreement between the automated scores and the expert consensus of greater than 96%.