Muthuswamy J, Sharma A
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.
J Clin Monit. 1996 Sep;12(5):353-64. doi: 10.1007/BF02077633.
To study the usefulness of three electro-encephalographic descriptors, the average median frequency, the average 90% spectral edge frequency, and a bispectral variable were used with the anesthetic concentrations in estimating the depth of anesthesia.
Four channels of raw EEG data were collected from seven mongrel dogs in nine separate experiments under different levels of halothane anesthesia and nitrous oxide in oxygen. A tail clamp was used as the stimulus and the dog was labeled as a non-responder or responder based on its response. A bispectral variable of the EEG (just before a tail clamp) and the estimated MAC level of halothane and nitrous oxide combined were the two features used to characterize a single data point. A neural network analysis was done on 48 such data points. A second neural network analysis was done on 47 data points using average 90% spectral edge frequency and the estimated MAC level. The average median frequency of EEG was also evaluated, although a neural network analysis was not done.
The first neural network needed nine weights in order to train and correctly classify all of the 12 points in the training set under a training tolerance of 0.2. It could correctly classify all of the remaining 36 data points as either belonging to responders or non-responders. A cross-validation procedure, which estimated the overall performance of the network against future data points, showed that the network misclassified two out of the 48 data points. The second neural network needed 25 weights in order to train and classify correctly all of the 26 points in the training set under a tolerance of 0.2. It was later able to classify all of the 21 points of the test group correctly.
The bispectral variable seems to reduce the non-linearity in the boundary separating the class of non-responders from the class of responders. Consequently, the neural network based on the bispectral variable is less complex than the neural network that uses a power spectral variable as one of its inputs.
研究三种脑电图描述指标，即平均中位频率、平均90%频谱边缘频率和一个双谱变量，与麻醉浓度一起用于评估麻醉深度的效用。
在九个不同的实验中，从七只杂种狗身上采集四通道原始脑电图数据，这些实验处于不同水平的氟烷麻醉以及氧化亚氮与氧气混合的环境下。使用尾部夹钳作为刺激物，并根据狗的反应将其标记为无反应者或有反应者。脑电图的一个双谱变量（就在施加尾部夹钳之前）以及氟烷和氧化亚氮联合的估计MAC水平是用于表征单个数据点的两个特征。对48个这样的数据点进行了神经网络分析。使用平均90%频谱边缘频率和估计的MAC水平对47个数据点进行了第二次神经网络分析。还评估了脑电图的平均中位频率，尽管未进行神经网络分析。
第一个神经网络需要九个权重才能在0.2的训练容差下训练并正确分类训练集中的所有12个点。它可以将其余所有36个数据点正确分类为属于有反应者或无反应者。一个交叉验证程序，该程序估计网络针对未来数据点的整体性能，表明该网络在48个数据点中误分类了两个。第二个神经网络需要25个权重才能在0.2的容差下训练并正确分类训练集中的所有26个点。后来它能够正确分类测试组的所有21个点。
双谱变量似乎减少了将无反应者类别与有反应者类别区分开的边界中的非线性。因此，基于双谱变量的神经网络比使用功率谱变量作为其输入之一的神经网络更简单。
