用于癫痫脑电动态建模的埃尔曼神经网络。
Elman neural networks for dynamic modeling of epileptic EEG.
作者信息
Kannathal N, Puthusserypady Sadasivan K, Min Lim Choo
机构信息
Dept. of ECE, Nat. Univ. of Singapore, Singapore.
出版信息
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:6145-8. doi: 10.1109/IEMBS.2006.259990.
In this paper, autoregressive modeling technique and neural network based modeling techniques are used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The normal, background and epileptic EEG signals are modeled and the dynamical properties of the actual and modeled signals are compared. Chaotic invariants like correlation dimension (D(2)), largest Lyapunov exponent (lambda(1), Hurst exponent (H) and Kolmogorov entropy (K) are used to characterize the dynamical properties of the actual and modeled signals. Our study showed that the dynamical properties of the EEG signal modeled using neural network (NN) techniques are very similar to that of the signal.
在本文中，自回归建模技术和基于神经网络的建模技术被用于对脑电图（EEG）信号进行建模和模拟。EEG信号建模被用作一种工具，以识别在临床诊断中可能有用的病理生理EEG变化。对正常、背景和癫痫性EEG信号进行建模，并比较实际信号和建模信号的动力学特性。使用混沌不变量，如关联维数（D(2)）、最大Lyapunov指数（lambda(1)）、赫斯特指数（H）和柯尔莫哥洛夫熵（K）来表征实际信号和建模信号的动力学特性。我们的研究表明，使用神经网络（NN）技术建模的EEG信号的动力学特性与该信号非常相似。
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