Jandó G, Siegel R M, Horváth Z, Buzsáki G
Center for Molecular and Behavioral Neuroscience, Rutgers, State University of New Jersey, Newark 07102.
Electroencephalogr Clin Neurophysiol. 1993 Feb;86(2):100-9. doi: 10.1016/0013-4694(93)90082-7.
A back-propagation network was trained to recognize high voltage spike-and-wave spindle (HVS) patterns in the rat, a rodent model of human petit mal epilepsy. The spontaneously occurring HVSs were examined in 137 rats of the Fisher 344 and Brown Norway strains and their F1, F2 and backcross hybrids. Neocortical EEG and movement of the rat were recorded for 12 night hours in each animal and analog data were filtered (low cut: 1 Hz; high cut: 50 Hz) and sampled at 100 Hz with 12 bit precision. A training data set was generated by manually marking durations of HVS epochs in 16 representative animals selected from each group. Training data were presented to back-propagation networks with variable numbers of input, hidden and output cells. The performance of different types of networks was first examined with the training samples and then the best configuration was tested on novel sets of the EEG data. FFT transformation of EEG significantly improved the pattern recognition ability of the network. With the most effective configuration (16 input; 19 hidden; 1 output cells) the summed squared error dropped by 80% as compared with that of the initial random weights. When testing the network with new patterns the manual and automatic evaluations were compared quantitatively. HVSs which were detected properly by the network reached 93-99% of the manually marked HVS patterns, while falsely detected events (non-HVS, artifacts) varied between 18% and 40%. These findings demonstrate the utility of back-propagation networks in automatic recognition of EEG patterns.
训练了一个反向传播网络来识别大鼠中的高压棘波-慢波纺锤波（HVS）模式，大鼠是人类失神癫痫的啮齿动物模型。在137只Fisher 344和Brown Norway品系及其F1、F2和回交杂种大鼠中检查了自发出现的HVS。对每只动物记录12个夜间小时的新皮质脑电图和大鼠运动情况，模拟数据经过滤波（低截止频率：1赫兹；高截止频率：50赫兹）并以100赫兹的采样频率、12位精度进行采样。通过手动标记从每组中选出的16只代表性动物的HVS发作持续时间来生成训练数据集。将训练数据呈现给具有不同数量输入、隐藏和输出单元的反向传播网络。首先用训练样本检查不同类型网络的性能，然后在新的脑电图数据集上测试最佳配置。脑电图的快速傅里叶变换显著提高了网络的模式识别能力。采用最有效的配置（16个输入；19个隐藏；1个输出单元）时，与初始随机权重相比，均方误差下降了80%。在用新模式测试网络时，对人工评估和自动评估进行了定量比较。网络正确检测到的HVS达到了人工标记的HVS模式的93%-99%，而错误检测到的事件（非HVS、伪迹）在18%至40%之间变化。这些发现证明了反向传播网络在自动识别脑电图模式方面的实用性。
