Prediction of seizure onset in an in-vitro hippocampal slice model of epilepsy using Gaussian-based and wavelet-based artificial neural networks
Prediction of seizure onset in an in-vitro hippocampal slice model of epilepsy using Gaussian-based and wavelet-based artificial neural networks
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DOI：
10.1007/s10439-005-2346-1
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发表时间：
2005-06-01
影响因子：
3.8
通讯作者：
Bardakjian, BL
中科院分区：
文献类型：
--
作者：
Chiu, AWL;Daniel, S;Bardakjian, BL
我们建议人工神经网络（ANN）可用于在体外海马切片模型中预测癫痫发作，该模型能够在其细胞外场记录中产生自发性癫痫样事件（SLE）。本文评估了两种 ANN 预测方案的有效性：基于高斯的人工神经网络（GANN）和基于小波的人工神经网络（WANN）。 GANN 预测系统由具有高斯径向基函数 (RBF) 非线性的循环网络组成，能够提取系统的估计流形。它能够将自发体外活动的潜在动力学分类为发作间期、发作前和发作模式。它还能够早在 60 秒前成功预测 SLE 的发作。通过结合时变频率信息可以改进癫痫发作预测器的整体设计。因此，考虑了WANN的想法。 WANN 设计需要假设细胞外场记录中的频率变化可用于计算未来最有可能发生 SLE 的时间。在初始小波变换之后，人工神经网络可以通过修剪使用适当的频带调整来捕获不同频率分量的进展。在由 14 个体外大鼠海马切片生成的 102 个自发 SLE 组成的离线处理中，其中一半用于训练，另一半用于测试，WANN 能够早在 SLE 之前 2 分钟预测即将到来的发作发作，在 30 秒的精度窗口内，准确率超过 75%。
We propose that artificial neural networks (ANNs) can be used to predict seizure onsets in an in-vitro hippocampal slice model capable of generating spontaneous seizure-like events (SLEs) in their extracellular field recordings. This paper assesses the effectiveness of two ANN prediction schemes: Gaussian-based artificial neural network (GANN) and wavelet-based artificial neural network (WANN). The GANN prediction system consists of a recurrent network having Gaussian radial basis function (RBF) nonlinearities capable of extracting the estimated manifold of the system. It is able to classify the underlying dynamics of spontaneous in-vitro activities into interictal, preictal and ictal modes. It is also able to successfully predict the onsets of SLEs as early as 60 s before. Improvements can be made to the overall seizure predictor design by incorporating time-varying frequency information. Consequently, the idea of WANN is considered. The WANN design entails the assumption that frequency variations in the extracellular field recordings can be used to compute the times at which onsets of SLEs are most likely to occur in the future. Progressions of different frequency components can be captured by the ANN using appropriate frequency band adjustments via pruning, after the initial wavelet transforms. In the off-line processing comprised of 102 spontaneous SLEs generated from 14 in-vitro rat hippocampal slices, with half of them used for training and the other half for testing, the WANN is able to predict the forecoming ictal onsets as early as 2 min prior to SLEs with over 75% accuracy within a 30 s precision window.