A matching pursuit-based signal complexity measure for the analysis of newborn EEG

L. Rankine, M. Mesbah, B. Boashash

Research output: Contribution to journalArticle

30 Citations (Scopus)


This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity (RSC), which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed RSC measure is used in the analysis of newborn electroencephalogram (EEG). To do this, firstly, a time-frequency decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Original languageEnglish
Pages (from-to)251-260
Number of pages10
JournalMedical and Biological Engineering and Computing
Issue number3
Publication statusPublished - Mar 2007



  • Coherent dictionary
  • Matching pursuit
  • Newborn EEG
  • Relative structural complexity
  • Time-frequency

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

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