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A method for decomposing multivariate time series into a causal hierarchy within specific frequency bands.

TitleA method for decomposing multivariate time series into a causal hierarchy within specific frequency bands.
Publication TypeJournal Article
Year of Publication2018
AuthorsDrover, Jonathan D., and Schiff Nicholas D.
JournalJ Comput Neurosci
Volume45
Issue2
Pagination59-82
Date Published2018 Oct
ISSN1573-6873
Abstract

We propose a method - Frequency extracted hierarchical decomposition (FEHD) - for studying multivariate time series that identifies linear combinations of its components that possess a causally hierarchical structure - the method orders the components so that those at the "top" of the hierarchy drive those below. The method shares many of the features of the "hierarchical decomposition" method of Repucci et al. (Annals of Biomedical Engineering, 29, 1135-1149, 2001) but makes a crucial advance - the proposed method is capable of determining this causal hierarchy over arbitrarily specified frequency bands. Additionally, a novel minimization strategy is used to generate the decomposition resulting in an increase in stability, reliability, and an improvement in the sensitivity to model parameters. We demonstrate the utility of the method by applying it to both artificial time series constructed to have specific causal graphs, and to the EEG of healthy volunteers and patient subjects who are recovering from a severe brain injury.

DOI10.1007/s10827-018-0691-y
Alternate JournalJ Comput Neurosci
PubMed ID30062615

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