Premium
Fast multidimensional directed information analysis
Author(s) -
Sakata Osamu,
Ohki Makoto,
Saito Yoichi
Publication year - 2012
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.21777
Subject(s) - autoregressive model , computer science , computation , series (stratigraphy) , time series , causality (physics) , signal processing , mutual information , algorithm , mathematics , statistics , artificial intelligence , machine learning , paleontology , telecommunications , radar , physics , quantum mechanics , biology
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.