On the Application of the Auto Mutual Information Rate of Decrease to Biomedical Signals

Javier Escudero*, Roberto Hornero, Daniel Abasolo, Miguel Lopez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The auto mutual information function (AMIF) evaluates the signal predictability by assessing linear and non-linear dependencies between two measurements taken from a single time series. Furthermore, the AMIF rate of decrease (AMIFRD) is correlated with signal entropy. This metric has (been used to analyze biomedical data, including cardiac and (brain activity recordings. Hence, the AMIFRD can be a relevant parameter in the context of biomedical signal analysis. Thus, in this pilot study, we have analyzed a synthetic sequence (a Lorenz system) and real biosignals (electroencephalograms recorded with eyes open and closed) with the AMIFRD. We aimed at illustrating the application of this parameter to (biomedical time series. Our results show that the AMIFRD can detect changes in the non-linear dynamics of a sequence and that it can distinguish different physiological conditions.

Original languageEnglish
Title of host publication2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-8
Place of PublicationNEW YORK
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2137-2140
Number of pages4
ISBN (Print)978-1-4244-1814-5
Publication statusPublished - 2008
Event30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society - Vancouver, Canada
Duration: 20 Aug 200824 Aug 2008

Publication series

NameIEEE Engineering in Medicine and Biology Society Conference Proceedings
PublisherIEEE
ISSN (Print)1557-170X

Conference

Conference30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
Country/TerritoryCanada
Period20/08/0824/08/08

Keywords

  • ALZHEIMERS-DISEASE
  • EEG
  • FLOW

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