Sinha R K
School of Biomedical Engineering, Institute of Technology, Banaras Hindu University, India.
Med Biol Eng Comput. 2003 Sep;41(5):595-600. doi: 10.1007/BF02345323.
An effective application is presented of a back-propagation artificial neural network (ANN) in differentiating electro-encephalogram (EEG) power spectra of stressed and normal rats in three sleep-wakefulness stages. The rats were divided into three groups, one subjected to acute heat stress, one subjected to chronic heat stress and one a handling control group. The polygraphic sleep recordings were performed by simultaneous recording of cortical EEG, electro-oculogram (EOG) and electromyogram (EMG) on paper and in digital form on a computer hard disk. The preprocessed EEG signals (after removal of DC components and reduction of baseline movement) were fragmented into 2 s artifact-free epochs for the calculation of power spectra. The slow-wave sleep (SWS), rapid eye movement (REM) sleep and awake (AWA) states were analysed separately. The power spectrum data for all three sleep-wake states in the three groups of rats were tested by a back-propagation ANN. The network contained 60 nodes in the input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in the hidden layer and an output node. The ANN was found effective in differentiating the EEG power spectra from stressed to normal spectral patterns following acute (92% in SWS, 85.5% in REM sleep, 91% in AWA state) as well as chronic heat exposure (95.5% in SWS, 93.8% in REM sleep, 98.5% in AWA state).
本文介绍了一种反向传播人工神经网络（ANN）在区分处于三个睡眠-觉醒阶段的应激大鼠和正常大鼠脑电图（EEG）功率谱方面的有效应用。将大鼠分为三组，一组施加急性热应激，一组施加慢性热应激，一组作为处理对照组。通过在纸上同时记录皮层脑电图、眼电图（EOG）和肌电图（EMG）以及以数字形式记录在计算机硬盘上进行多导睡眠记录。对预处理后的脑电图信号（去除直流分量并减少基线移动后）进行分段，形成2秒无伪迹的时段，用于计算功率谱。分别分析慢波睡眠（SWS）、快速眼动（REM）睡眠和清醒（AWA）状态。通过反向传播人工神经网络对三组大鼠所有三种睡眠-觉醒状态的功率谱数据进行测试。该网络在输入层包含60个节点，由0至30Hz的功率谱数据加权，在隐藏层包含18个节点和一个输出节点。结果发现，人工神经网络在区分急性热暴露（慢波睡眠中为92%，快速眼动睡眠中为85.5%，清醒状态中为91%）以及慢性热暴露（慢波睡眠中为95.5%，快速眼动睡眠中为93.8%，清醒状态中为98.5%）后应激大鼠与正常大鼠的脑电图功率谱方面是有效的。
