来自多元时间序列的因果关系网络及其在癫痫中的应用。

Causality networks from multivariate time series and application to epilepsy.

作者信息

Siggiridou Elsa, Koutlis Christos, Tsimpiris Alkiviadis, Kimiskidis Vasilios K, Kugiumtzis Dimitris

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:4041-4. doi: 10.1109/EMBC.2015.7319281.

DOI:10.1109/EMBC.2015.7319281

Abstract

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.

摘要

格兰杰因果关系及其相关概念变体，使得对复杂动力系统的研究能够像从多元时间序列构建的网络那样进行。在这项工作中，评估了大量用于从多元时间序列形成因果关系网络的格兰杰因果关系度量。为此，考虑了高维耦合动力系统的实现，并评估了格兰杰因果关系度量的性能，旨在寻找能形成最接近动力系统真实网络的网络的度量。特别地，比较聚焦于在观测到许多变量时能降低状态空间维度的格兰杰因果关系度量。此外，还将降维的线性和非线性格兰杰因果关系度量与包含癫痫样放电发作的脑电图（EEG）记录上的标准格兰杰因果关系度量进行了比较。