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Multifractal parameters as an indication of different physiological and pathological states of the human brain

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  • Dutta, Srimonti
  • Ghosh, Dipak
  • Samanta, Shukla
  • Dey, Santanu

Abstract

This paper presents a study on multifractal parameters of EEG patterns on the human brain. Multifractal detrended fluctuation analysis was applied to human EEG for normal and epileptic patients in different states. The results show that the degree of multifractality of EEG for patients in an epileptic seizure are much higher compared to normal healthy people. The degree of multifractality for normal humans with eyes open and closed was also significantly different. Thus the multifractal parameters can be used to distinguish between different physiological and pathological states of the human brain. The results are discussed in detail.

Suggested Citation

  • Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla & Dey, Santanu, 2014. "Multifractal parameters as an indication of different physiological and pathological states of the human brain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 155-163.
    • Handle: RePEc:eee:phsmap:v:396:y:2014:i:c:p:155-163
    DOI: 10.1016/j.physa.2013.11.014
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    Citations

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    Cited by:

    1. Chatterjee, Sucharita, 2020. "Analysis of the human gait rhythm in Neurodegenerative disease: A multifractal approach using Multifractal detrended cross correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    2. Banerjee, Archi & Sanyal, Shankha & Roy, Souparno & Nag, Sayan & Sengupta, Ranjan & Ghosh, Dipak, 2021. "A novel study on perception–cognition scenario in music using deterministic and non-deterministic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    3. Pal, Mayukha & P., Manimaran & Panigrahi, Prasanta K., 2022. "A multi scale time–frequency analysis on Electroencephalogram signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    4. 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.
    5. Fernandes, Leonardo H.S. & de Araújo, Fernando H.A. & Silva, Igor E.M., 2020. "The (in)efficiency of NYMEX energy futures: A multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    6. Chatterjee, Sucharita & Ghosh, Dipak, 2021. "Impact of Global Warming on SENSEX fluctuations — A study based on Multifractal detrended cross correlation analysis between the temperature anomalies and the SENSEX fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    7. Ghosh, Dipak & Dutta, Srimonti & Chakraborty, Sayantan, 2014. "Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status," Chaos, Solitons & Fractals, Elsevier, vol. 67(C), pages 1-10.
    8. 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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