Adult Rat Vigilance States Discrimination By Artificial Neural Networks Using A Single Eeg Channel

Two multilayer neural networks were designed to discriminate vigilance states (waking, paradoxical sleep, and non-REM sleep) in the rat using a single parieto-occipital EEG derivation. After filtering (bandwidth 3.18-25 Hz) and digitization at 512 Hz, the EEG signal was segmented into eight second epochs. Five variables (three statistical, two temporal) were extracted from each epoch. The first network computed an epoch by epoch classification, while the second network also utilized contextual information from contiguous epochs. A specific postprocessing procedure was developed to enhance the vigilance state discrimination of the neural networks designed and especially paradoxical sleep state estimation. The classifications made by the networks (with or without the postprocessing procedure) for six rats were compared to these made by two human experts using EMG and EEG informations on 63,000 epochs. High rates of agreement (> 90%) between humans and neural networks classifications were obtained. In, view of its development possibilities and its applicability to other signals, this method could prove of value in biomedical research.