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

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Abstract

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).

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Notes

  1. The definition of complexity here is related to the complexity of the phase space representation (level of chaotic behaviour) of the signal, often used in nonlinear time series analysis [19].

  2. The MATLAB code used to create the synthetic newborn EEG background and EEG seizure used in this paper is based on the models described in [27]. The code is freely available from http://www.som.uq.edu/research/sprcg.

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Acknowledgments

The authors gratefully acknowledge Prof. Paul Colditz for organizing the acquisition of the real newborn EEG data and Dr. Chris Burke and Jane Richmond for their expertise in newborn EEG reading.

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Authors and Affiliations

  1. Perinatal Research Centre, Royal Brisbane and Women’s Hospital, Herston, QLD, 4029, Australia

    L. Rankine, M. Mesbah & B. Boashash

  2. College of Engineering, University of Sharjah, University City, Sharjah, United Arab Emirates

    B. Boashash

Authors
  1. L. Rankine
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  2. M. Mesbah
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  3. B. Boashash
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Corresponding author

Correspondence to L. Rankine.

Additional information

This work was supported by grants from the NHMRC and ARC.

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Rankine, L., Mesbah, M. & Boashash, B. A matching pursuit-based signal complexity measure for the analysis of newborn EEG. Med Bio Eng Comput 45, 251–260 (2007). https://doi.org/10.1007/s11517-006-0143-0

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