Adult rat vigilance states discrimination by artificial neural networks using a single EEG channel
Adult rat vigilance states discrimination by artificial neural networks using a single EEG channel
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DOI：
10.1016/0031-9384(95)02214-7
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发表时间：
1996-06-01
影响因子：
2.9
通讯作者：
Limoge, A
中科院分区：
文献类型：
--
作者：
Robert, C;Karasinski, P;Limoge, A
两个多层神经网络的设计，以区分警觉状态（清醒，异相睡眠，非REM睡眠）在大鼠使用一个单一的顶枕脑电图推导。在滤波（带宽3.18-25 Hz）和512 Hz下的数字化之后，EEG信号被分割成8秒的时期。从每个时期提取五个变量（三个统计，两个时间）。第一个网络通过epoch分类计算epoch，而第二个网络也利用来自连续epoch的上下文信息。一个特定的后处理程序的开发，以提高警觉状态歧视的神经网络设计，特别是矛盾的睡眠状态估计。将网络对六只大鼠进行的分类（有或没有后处理程序）与两位人类专家使用63，000个时期的EMG和EEG信息进行的分类进行了比较。人类和神经网络分类之间的一致率很高（> 90%）。鉴于其发展的可能性及其对其他信号的适用性，这种方法可以证明在生物医学研究中的价值。
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.