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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.

References

  1. Aminoff M (1992) Electroecephalograhy: general principles and clinical applications. In: Aminoff M (ed) Electrodiagnosis in clinical neurology, 3rd edn. Churchill Livingstone, New York, pp 41–91

  2. Bergey G, Franaszczuk P (2001) Epileptic seizures are characterised by changing signal complexity. Clin Neurophysiol 112(2):241–249

    Article  Google Scholar 

  3. Boashash B (2003) Heuristic formulation of TFDs. In: Boashash B (ed) Time–frequency signal analysis and processing: a comprehensive reference, Chap 2. Elsevier, London, pp 29–57

  4. Boashash B (2003) Theory of quadratic TFDs. In: Boashash B (ed) Time–frequency signal analysis and processing: a comprehensive reference, Chap 3. Elsevier, London, pp 59–81

  5. Boashash B (2003) Time–frequency concepts. In: Boashash B (ed) Time–frequency signal analysis and processing: a comprehensive reference, Chap 1. Elsevier, London, pp 4–27

  6. Boashash B, Mesbah M (2001) A time–frequency approach for newborn seizure detection. IEEE Eng Med Biol Mag 20(5):54–64

    Article  Google Scholar 

  7. Boashash B, Mesbah M (2003) Time–frequency methodology for newborn electroencephalographic seizure detection. In: Papandreou-Suppappola A (ed) Applications in time–frequency signal processing. CRC Press, Boca Raton

    Google Scholar 

  8. Boashash B, Mesbah M, Colditz P (2001) Newborn EEG seizure pattern characterisation using time–frequency analysis. In: Proceedings of IEEE international conference on acoustics, speech and signal processing, Salt Lake City, USA, pp 1041–1044

  9. Boashash B, Mesbah M, Colditz P (2003) Time–frequency detection of EEG abnormalities. In: Boashash B (ed) Time–frequency signal analysis and processing: a comprehensive reference, Chap 15.5. Elsevier, London, pp 663–670

  10. Boashash B, Putland G (2003) Discrete time–frequency distributions. In: Boashash B (ed) Time–frequency signal analysis and processing: a comprehensive reference, Chap 6.1. Elsevier, London, pp 232–241

  11. Boashash B, Sucic V (2003) Resolution measure criteria for the objective assessment of the performance of quadratic time–frequency distributions. IEEE Trans Signal Process 51(5):1253–1263

    Article  MathSciNet  Google Scholar 

  12. Celka P, Boashash B, Colditz P (2001) Preprocessing and time–frequency analysis of newborn EEG seizures. IEEE Eng Med Biol 20(5):30–39

    Article  Google Scholar 

  13. Chen S, Donoho D, Saunders M (2001) Atomic decomposition by basis pursuit. Soc Ind Appl Math Rev 43(1):129–159

    MATH  MathSciNet  Google Scholar 

  14. Donoho D, Elad M, Temlyakov V (2006) Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans Inf Theory 52(1):6–18

    Article  MathSciNet  Google Scholar 

  15. Donoho D, Huo X (2001) Uncertainty principles and ideal atomic decomposition. IEEE Trans Inf Theory 46(7):2845–2862

    Article  MathSciNet  Google Scholar 

  16. Durka P (2004) Adaptive parameterization of epileptic spikes. Phys Rev E 69:051914

    Google Scholar 

  17. Elad M, Bruckstein A (2002) A generalized uncertainty principle and sparse representation in pairs of bases. IEEE Trans Inf Theory 48(9):2558–2567

    Article  MATH  MathSciNet  Google Scholar 

  18. Gotman J, Flanagan D, Zhang J, Rosenblatt B (1997) Automatic seizure detection in the newborn: methods and initial evaluation. Electroencephalogr Clin Neurophysiol 103(3):356–362

    Article  Google Scholar 

  19. Kantz H, Shreiber T (1997) Nonlinear time series analysis. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  20. Lombroso C (1993) Neonatal EEG polygraphy in normal and abnormal newborns. In: Niedermeyer E, Silva FLD (eds) Electroencephalography: basic principles, clinical applications and related fields, 3rd edn. Williams and Wilkins, Baltimore, pp 803–875

    Google Scholar 

  21. Mallat S, Zhang Z (1993) Matching pursuits with time–frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415

    Article  MATH  Google Scholar 

  22. Mizrahi E, Kellaway P (1998) Diagnosis and management of neonatal seizure. Lippincott–Raven, Philadelphia

    Google Scholar 

  23. Rangayyan RM, Krishnan S (2001) Feature identification in the time–frequency plane by using the Hough–Radon transform. Pattern Recognit 34(6):1147–1158

    Article  MATH  Google Scholar 

  24. Rankine L (2006) Newborn EEG seizure detection using adaptive time–frequency signal processing. Ph.D. thesis, Queensland University of Technology, Brisbane

  25. Rankine L, Mesbah M, Boashash B (2004) A novel algorithm for newborn EEG seizure detection using matching pursuits with a coherent time–frequency dictionary. In: Proceedings of international conference on scientific and engineering computation, Singapore, pp CD-ROM

  26. Rankine L, Mesbah M, Boashash B (2006) IF estimation for multicomponent signals using image processing techniques in the time–frequency domain. Signal Process (accepted)

  27. Rankine L, Stevenson N, Mesbah M, Boashash B (2007) A nonstationary model of newborn EEG. IEEE Trans Biomed Eng 54(1):19–28

    Article  Google Scholar 

  28. Scher M, Sun M, Steppe D, Guthrie R, Sclabassi R (1994) Comparison of EEG spectral and correlation measures between healthy term and preterm infants. Pediatr Neurol 10(2):104–108

    Article  Google Scholar 

  29. Teixeira A, Tomé A, Lang E, Gruber P, da Silva AM (2006) Automatic removal of high-amplitude artefacts from single-channel electroencephalograms. Compu Methods Programs Biomed 83(2):125–138

    Article  Google Scholar 

  30. Tropp J (2004) Greed is good: algorithmic results for sparse approximation. IEEE Trans Inf Theory 50(10):2231–2242

    Article  MathSciNet  Google Scholar 

  31. Tropp J, Gilbert A, Strauss M (2006) Algorithms for simulataneous sparse approximation. Part I: greedy pursuit. Signal Process 86(3):572–588

    Article  Google Scholar 

  32. Umapathy K, Krishnan S, Parsa V, Jamieson D (2005) Discrimination of pathological voices using a time–frequency approach. IEEE Trans Biomed Eng 52(3):421–430

    Article  Google Scholar 

  33. Wilson S, Scheuer M, Plummer C, Young B, Pacia S (2003) Seizure detection: correlation of human experts. Clin Neurophysiol 114:2156–2164

    Article  Google Scholar 

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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

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  1. L. Rankine
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  2. M. Mesbah
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  3. B. Boashash
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Correspondence to L. Rankine.

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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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  • DOI: https://doi.org/10.1007/s11517-006-0143-0

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