Department of Electronics, Gazi University, Teknikokullar, Ankara, Turkey.
J Med Syst. 2011 Aug;35(4):489-98. doi: 10.1007/s10916-009-9385-3. Epub 2009 Oct 21.
In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.
在这项研究中，将快速傅里叶变换（FFT）分析应用于正常和患者受试者的脑电图（EEG）信号，将获得的 FFT 系数用作人工神经网络（ANN）的输入。通过使用人工神经网络，在计算机支持的条件下，评估了诸如 EEG 信号等非平稳随机信号在健康和疾病（癫痫）情况下的差异，并尝试进行分析。多层感知器（MLP）架构使用 Levenberg-Marquardt（LM）、Quickprop（QP）、Delta-bar delta（DBD）、动量和共轭梯度（CG）学习算法，并通过使用遗传算法优化权重、学习率、隐藏层神经元数量，从而在训练过程中达到最佳性能。本研究表明，人工神经网络通过遗传算法提高了分类性能。
