Noninvasive localization of electromagnetic epileptic activity. I. Method descriptions and simulations
Noninvasive localization of electromagnetic epileptic activity. I. Method descriptions and simulations
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DOI：
10.1023/a:1012944913650
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发表时间：
2001-12-01
期刊：
影响因子：
2.7
通讯作者：
Landis, T
中科院分区：
文献类型：
--
作者：
Menendez, RGD;Andino, SG;Landis, T
本文考虑了生物电磁场逆问题的解，特别强调了癫痫数据中可能出现的焦点致密源。提出了两种线性逆方法，并在仿真中进行了评价。第一种方法属于分布式逆解，能够同时处理多个有源源。该解决方案基于局部自回归平均(Laura)模型。由于没有对激活的源的数量进行假设，因此该方法可以应用于具有多个源的数据。第二种方法是EPIFOCUS，它假定只有一个震源。然而，与单偶极子模型相比，它允许源具有超出单点的空间范围，并避免了偶极子拟合所需的非线性优化过程。利用合成数据对这两种方法在有噪声和无噪声条件下的性能进行了评估。仿真结果表明，与最小范数、加权最小范数和最小拉普拉斯算子(Loreta)相比，Laura和EPIFOCUS算法增加了零偶极子定位误差的信源数目，并产生了较小的最大误差和较小的平均误差。结果表明，EPIFOCUS是一种强大的震源定位工具。讨论了本地化由多个源生成的数据的替代方案。一篇配套论文(Lantz et al.2001，本期)说明了Laura和EPIFOCUS在癫痫患者发作间歇期数据分析中的应用。
This paper considers the solution of the bioelectromagnetic inverse problem with particular emphasis on focal compact sources that are likely to arise in epileptic data. Two linear inverse methods are proposed and evaluated in simulations. The first method belongs to the class of distributed inverse solutions, capable of dealing with multiple simultaneously active sources. This solution is based on a Local Auto Regressive Average (LAURA) model. Since no assumption is made about the number of activated sources, this approach can be applied to data with multiple sources. The second method, EPIFOCUS, assumes that there is only a single focal source. However, in contrast to the single dipole model, it allows the source to have a spatial extent beyond a single point and avoids the non-linear optimization process required by dipole fitting. The performance of both methods is evaluated with synthetic data in noisy and noise free conditions. The simulation results demonstrate that LAURA and EPIFOCUS increase the number of sources retrieved with zero dipole localization error and produce lower maximum error and lower average error compared to Minimum Norm, Weighted Minimum Norm and Minimum Laplacian (LORETA). The results show that EPIFOCUS is a robust and powerful tool to localize focal sources. Alternatives to localize data generated by multiple sources are discussed. A companion paper (Lantz et al. 2001, this issue) illustrates the application of LAURA and EPIFOCUS to the analysis of interictal data in epileptic patients.