Wendling F, Badier J M, Chauvel P, Coatrieux J L
Laboratoire Traitement du Signal et de L'Image, INSERM CJF 93-04, Université de Rennes 1, France.
Electroencephalogr Clin Neurophysiol. 1997 Jun;102(6):472-85. doi: 10.1016/s0013-4694(96)96633-3.
In the field of epilepsy, the analysis of stereoelectroencephalographic (SEEG) signals recorded with depth electrodes provides major information on interactions between brain structures during seizures. A methodology of comparing SEEG seizure recordings is applied in 4 patients suffering from temporal lobe epilepsy. It proceeds in 3 steps: (i) segmentation of SEEG signals, (ii) characterization and labeling of segments and (iii) comparison of observations coded as sequences of symbol vectors. The third step is based on a vectorial extension of Wagner and Fischer's algorithm to first, quantify similarities between observations and second, extract invariant information, referred to as spatio-temporal signatures. These are automatically extracted by the algorithm without the need to make a priori assumptions on the 'patterns' to be searched for. Theoretical results show that two observations of non-equal duration can be matched by deforming the first one (using insertion/deletion operations on vectors) to optimally fit the second, under a minimal cost constraint. Clinical results show that the study brings objective results on reproducible mechanisms occurring during seizures: for a given patient, quantified descriptions of seizure periods are compared and similar ictal patterns, or signatures, are extracted from SEEG signals. Some of these signatures (particularly those containing spikes, spike-and-waves, slow waves and rapid discharges) are relevant: they seem to reflect reproducible propagation schemes whose analysis may help in the understanding of epileptogenic networks.
在癫痫领域,对通过深度电极记录的立体脑电图(SEEG)信号进行分析可提供有关癫痫发作期间脑结构之间相互作用的主要信息。一种比较SEEG癫痫发作记录的方法应用于4例颞叶癫痫患者。该方法分三步进行:(i)SEEG信号的分割,(ii)片段的特征描述和标记,以及(iii)对编码为符号向量序列的观察结果进行比较。第三步基于Wagner和Fischer算法的向量扩展,首先量化观察结果之间的相似性,其次提取不变信息,即时空特征。这些特征由算法自动提取,无需对要搜索的“模式”进行先验假设。理论结果表明,在最小成本约束下,通过对第一个观察结果进行变形(对向量使用插入/删除操作)以最佳拟合第二个观察结果,可以匹配两个持续时间不相等的观察结果。临床结果表明,该研究为癫痫发作期间发生的可重复机制带来了客观结果:对于给定患者,比较癫痫发作期的量化描述,并从SEEG信号中提取相似的发作模式或特征。其中一些特征(特别是那些包含棘波、棘慢波、慢波和快速放电的特征)是相关的:它们似乎反映了可重复的传播模式,对其进行分析可能有助于理解致痫网络。