Fast Multidimensional Directed Information
Fast Multidimensional Directed Information
复制标题
快速多维定向信息
DOI：
10.1002/tee.21777
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发表时间：
2012
影响因子：
1
通讯作者：
Osamu Sakata
中科院分区：
文献类型：
--
作者：
Toshiaki Mochizuki;Yu Shuchun;Takasumi Katoh;Katsunori Aoki;Shigehito Sato;Osamu Sakata
多维有向信息分析（MDI）是一种将多通道时间序列的因果关系以信息流的形式量化和可视化的信号处理方法。MDI分析被定义为条件互信息，需要大量的计算。虽然MDI用于脑电图（EEG）分析，但大的计算时间是一个问题。假设多通道时间序列具有高斯分布，MDI可以在没有直接概率计算的情况下计算。然而，计算量随着信道数量的增加而呈指数级增加。这种大的计算已经阻止了MDI分析在医学领域中的实际使用，例如需要处理许多多维时间序列的临床EEG分析。在本文中，我们提出了一种新的计算方法，大大减少MDI分析的计算时间。所提出的方法使得它有可能减少计算时间指数多通道时间序列，可以近似多维自回归模型。© 2012日本电气工程师协会。由John Wiley & Sons公司出版
Multidimensional directed information (MDI) analysis is a signal processing method to quantify and visualize the causality of multichannel time series in the form of information flow. MDI analysis is defined as conditional mutual information and needs large calculations. Although MDI is used for electroencephalogram (EEG) analysis, large computation time is a problem. MDI can be calculated without direct probability calculations, assuming that the multichannel time series has Gaussian profiles. However, the amount of calculation increases exponentially with increase in the number of channels. Such large calculations have prevented practical use of MDI analysis in medical fields such as clinical EEG analysis in which many multidimensional time series need to be processed. In this paper, we propose a new calculation approach to drastically decrease the calculation time of MDI analysis. The proposed method makes it possible to decrease the calculation time exponentially for multichannel time series that can be approximated with multidimensional autoregressive models. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.