Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis: Application to electroencephalogram recordings

Javier Escudero*, Roberto Hornero, Daniel Abasolo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

Original languageEnglish
Pages (from-to)187-199
Number of pages13
JournalPhysiological Measurement
Volume30
Issue number2
DOIs
Publication statusPublished - Feb 2009

Keywords / Materials (for Non-textual outputs)

  • auto-mutual information
  • biomedical signal analysis
  • electroencephalogram
  • nonlinear analysis
  • signal regularity
  • ALZHEIMERS-DISEASE PATIENTS
  • APPROXIMATE ENTROPY
  • EEG RESPONSES
  • COMPLEXITY
  • FLOW
  • STIMULATION
  • SYSTEM
  • MODEL

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