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Neonatal EEG signal characteristics using time frequency analysis

Author

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  • Abdulla, Waleed
  • Wong, Lisa

Abstract

Time-frequency analysis is a way to represent the energy contents of a signal in the joint time-frequency domain. It provides a good visual way to separate the frequency contents of a multi-component signal, and display the changes of these components with respect to time. This paper outlines investigative work on neonatal EEG signals using time-frequency analysis. The Cohen’s class distributions are discussed, and kernel optimisation for the Cohen’s class distributions is outlined. Segments of EEG with different background continuity states are analysed using a Cohen’s class distribution, and their characteristics are discussed. Through this paper, interesting information that offers insight towards the EEG signal can be visualized from the time frequency analysis.

Suggested Citation

  • Abdulla, Waleed & Wong, Lisa, 2011. "Neonatal EEG signal characteristics using time frequency analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1096-1110.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:6:p:1096-1110
    DOI: 10.1016/j.physa.2010.11.013
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    Cited by:

    1. Lahmiri, Salim, 2018. "Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 378-385.
    2. Bozhokin, S.V. & Suslova, I.B., 2015. "Wavelet-based analysis of spectral rearrangements of EEG patterns and of non-stationary correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 151-160.

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