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Identification of Hidden Sources by Estimating Instantaneous Causality in High-Dimensional Biomedical Time Series.

Authors
  • Koutlis, Christos1
  • Kimiskidis, Vasilios K2
  • Kugiumtzis, Dimitris1
  • 1 1 Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece. , (Greece)
  • 2 2 Laboratory of Clinical Neurophysiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece. , (Greece)
Type
Published Article
Journal
International journal of neural systems
Publication Date
May 01, 2019
Volume
29
Issue
4
Pages
1850051–1850051
Identifiers
DOI: 10.1142/S012906571850051X
PMID: 30563386
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The study of connectivity patterns of a system's variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a better understanding of its internal evolutionary mechanisms. Here, the problem of estimating the connectivity network from multivariate time series in the presence of prominent unobserved variables is addressed. The causality measure of partial mutual information from mixed embedding (PMIME), designed to estimate direct lag-causal effects in the presence of many observed variables, is adapted to estimate also zero-lag effects, the so-called instantaneous causality. We term the proposed advanced method, PMIME0. The estimation of instantaneous causality by PMIME0 is a signature of the presence of hidden source in the observed system, as demonstrated analytically in a toy model. It is further demonstrated that the PMIME0 identifies the true instantaneous with great accuracy in a variety of high-dimensional dynamical systems. The method is applied to EEG data with epileptiform discharges (EDs), and the results imply a strong impact of unobserved confounders during the EDs. This finding comes as a possible explanation for the increased levels of causality during epileptic seizures estimated by some measures affected by the presence of a common source.

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