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Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

Author

Listed:
  • Timashev, Serge F.
  • Panischev, Oleg Yu.
  • Polyakov, Yuriy S.
  • Demin, Sergey A.
  • Kaplan, Alexander Ya.

Abstract

We apply flicker-noise spectroscopy (FNS), a time series analysis method operating on structure functions and power spectrum estimates, to study the clinical electroencephalogram (EEG) signals recorded in children/adolescents (11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical Sciences. The EEG signals for these subjects were compared with the signals for a control sample of chronically depressed children/adolescents. The purpose of the study is to look for diagnostic signs of subjects’ susceptibility to schizophrenia in the FNS parameters for specific electrodes and cross-correlations between the signals simultaneously measured at different points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes at locations F3 and F4, which are symmetrically positioned in the left and right frontal areas of cerebral cortex, respectively, demonstrates an essential role of frequency–phase synchronization, a phenomenon representing specific correlations between the characteristic frequencies and phases of excitations in the brain. We introduce quantitative measures of frequency–phase synchronization and systematize the values of FNS parameters for the EEG data. The comparison of our results with the medical diagnoses for 84 subjects performed at NCPH makes it possible to group the EEG signals into 4 categories corresponding to different risk levels of subjects’ susceptibility to schizophrenia. We suggest that the introduced quantitative characteristics and classification of cross-correlations may be used for the diagnosis of schizophrenia at the early stages of its development.

Suggested Citation

  • Timashev, Serge F. & Panischev, Oleg Yu. & Polyakov, Yuriy S. & Demin, Sergey A. & Kaplan, Alexander Ya., 2012. "Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1179-1194.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1179-1194
    DOI: 10.1016/j.physa.2011.09.032
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    References listed on IDEAS

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    1. Panischev, O.Yu. & Demin, S.A. & Bhattacharya, J., 2010. "Cross-correlation markers in stochastic dynamics of complex systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4958-4969.
    2. Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
    3. Yulmetyev, Renat M. & Yulmetyeva, Dinara & Gafarov, Fail M., 2005. "How chaosity and randomness control human health," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 404-414.
    4. Plamen Ch. Ivanov & Luís A. Nunes Amaral & Ary L. Goldberger & Shlomo Havlin & Michael G. Rosenblum & Zbigniew R. Struzik & H. Eugene Stanley, 1999. "Multifractality in human heartbeat dynamics," Nature, Nature, vol. 399(6735), pages 461-465, June.
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    1. Mulligan, Robert F., 2017. "The multifractal character of capacity utilization over the business cycle: An application of Hurst signature analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 147-152.
    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.
    3. Pakrashi, Vikram & Kelly, Joe & Harkin, Julie & Farrell, Aidan, 2013. "Hurst exponent footprints from activities on a large structural system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1803-1817.
    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. Litak, Grzegorz & Polyakov, Yuriy S. & Timashev, Serge F. & Rusinek, Rafał, 2013. "Dynamics of stainless steel turning: Analysis by flicker-noise spectroscopy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6052-6063.
    6. Mulligan, Robert F., 2014. "Multifractality of sectoral price indices: Hurst signature analysis of Cantillon effects in disequilibrium factor markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 252-264.

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