  In this paper we propose a new method for the automatic recognition of the
state of behavioral sleep (BS) and waking state (WS) in freely moving rats
using their electrocorticographic (ECoG) data. Three-channels ECoG signals were
recorded from frontal left, frontal right and occipital right cortical areas.
We employed a simple artificial neural network (ANN), in which the mean values
and standard deviations of ECoG signals from two or three channels were used as
inputs for the ANN. Results of wavelet-based recognition of BS/WS in the same
data were used to train the ANN and evaluate correctness of our classifier. We
tested different combinations of ECoG channels for detecting BS/WS.
  Our results showed that the accuracy of ANN classification did not depend on
ECoG-channel. For any ECoG-channel, networks were trained on one rat and
applied to another rat with an accuracy of at least 80~\%. Itis important that
we used a very simple network topology to achieve a relatively high accuracy of
classification. Our classifier was based on a simple linear combination of
input signals with some weights, and these weights could be replaced by the
averaged weights of all trained ANNs without decreases in classification
accuracy. In all, we introduce a new sleep recognition method that does not
require additional network training. It is enough to know the coefficients and
the equations suggested in this paper. The proposed method showed very fast
performance and simple computations, therefore it could be used in real time
experiments. It might be of high demand in preclinical studies in rodents that
require vigilance control or monitoring of sleep-wake patterns.
