Die präiktale Phase von epileptischen Anfällen - Untersuchung mit neuen Ansätzen der multivariaten Zeitreihenanalyse

癫痫发作的发作前阶段 - 使用多变量时间序列分析新方法进行研究

• 批准号: 16645421
• 负责人: Privatdozent Dr. Christian Rummel
• 金额: --
• 依托单位: Facultad de Ciencias (UEM)
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2005
• 资助国家: 德国
• 起止时间: 2004-12-31 至 2007-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/16645421?language=en
• 关键词: Die; pr; iktale; Phase; von

The purpose of the present document is twofold: First, an interim report is given about the work done in Cuernavaca (Mexico) in connection with the DFG research grant “Die präiktale Phase von epileptischen Anfällen - Untersuchung mit neuen Ansätzen der multivariaten Zeitreihenanalyse” (RU 1401/1-1). Measures taken from Random Matrix Theory (RMT) were adapted in order to be applied to multivariate time series analysis of the electroencephalogram (EEG). The novel tools turned out powerful and valuable for separation of random from non-random correlations. Even weak genuine correlations in the EEG can be detected reliably.As during the investigation many new and urgent questions came up it is suggested to extend the grant for another 3 months in order to be able to complete the investigation. The development of the concept of the Cluster Participation Vectors (CPV), which can be used for detection of clusters among the EEG channels, shall be finished. Furthermore it is planned to study in detail the behavior of the measure of the individually unfolded nearest neighbor spacing distribution of the eigenvalues of the equal-time correlation matrix in the presence of EEG non-stationarity.

本文件有两个目的：首先，提供一份关于库埃尔纳瓦卡（墨西哥）在DFG研究资助“Die präiktale Phase von epileptischen Anfällen - Untersuchung mit neuen Ansätzen der multivariaten Zeitreihenanalysis”（RU 1401/1-1）方面所做工作的临时报告。从随机矩阵理论（RMT）采取的措施进行了调整，以适用于多变量时间序列分析的脑电图（EEG）。新的工具变成了强大的和有价值的随机非随机相关性的分离。即使是EEG中微弱的真实相关性也可以可靠地检测到。由于在调查过程中出现了许多新的和紧迫的问题，因此建议将补助金再延长3个月，以便能够完成调查。应完成群集参与向量（CPV）概念的开发，该概念可用于检测EEG通道中的群集。此外，计划详细研究EEG非平稳性存在下的等时间相关矩阵的特征值的单独展开的最近邻间距分布的测量的行为。