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.

翻译：本文提出一种利用自由活动大鼠皮层脑电图（ECoG）数据自动识别行为睡眠（BS）与清醒状态（WS）的新方法。我们从左侧额叶、右侧额叶及右侧枕叶皮层区域记录了三通道ECoG信号。采用简易人工神经网络（ANN），以两通道或三通道ECoG信号的均值与标准差作为ANN输入。利用基于小波变换的BS/WS识别结果对ANN进行训练并评估分类器准确性。我们测试了不同ECoG通道组合检测BS/WS的效果。结果显示，ANN分类准确率与ECoG通道选择无关。对于任意ECoG通道，网络经单只大鼠训练后应用于另一只大鼠，准确率均不低于80%。值得关注的是，我们采用极为简单的网络拓扑结构即实现了较高分类精度。该分类器基于输入信号的加权线性组合，且这些权重可直接替换为所有训练后ANN的均值权重而不降低分类准确率。综上，我们提出一种无需额外网络训练的新型睡眠识别方法，仅需运用本文给出的系数与方程即可完成识别。本方法运算速度快、计算简单，适用于实时实验场景。在需要警觉状态监测或睡眠-觉醒模式追踪的啮齿类动物临床前研究中具有重要应用价值。