Analysis of the relationship of various pathologies with the degree of multifractality of electrical activity of the brain

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Abstract

The review is devoted to the analysis of the relationship between dynamic changes in patterns of electrical activity of the brain during the occurrence of mental disorders in the form of paranoid schizophrenia and depression and in patterns of brain activity in cardiovascular pathology associated with permanent atrial fibrillation, as well as indicators of multifractality of the studied patterns. To assess these indicators of electroencephalographic patterns, we describe a method of multifractal analysis based on the search for maxima of wavelet coefficient modules, and to isolate the fractal component of the signal in the power spectrum we describe a method of autospectral analysis with irregular resampling. It has been shown that the main differences between the multifractal properties of the electrical activity of the brain in health and in pathology are the different widths of the multifractality spectrum and its location, associated with different types of sequential pattern values. In this regard, the multifractality indicators can serve as informative markers of neuronal disorders and can be included in a set of tests for studying various pathologies.

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About the authors

O. E. Dick

Pavlov Institute of Physiology of Russian Academy of Science

Author for correspondence.
Email: dickviola@gmail.com
Russian Federation, 199034, St. Petersburg, nab. Makarova, 6

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Supplementary files

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2. Fig. 1. Examples of power spectra for the EEG of a healthy person (a, g, g), a patient with schizophrenia (bde) and depression (b, e, k) (O2 lead). Initial (mixed) power spectra (a–b), averaged mixed spectra (blue curves) and fractal components of the spectra (red curves) (g–e), vibrational spectra (w–k) (data from [12]).

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3. Fig. 2. Dependencies h(q) (a) and singularity spectra (b) for frontal leads (F3, Fz, F4) (blue curves) and for central (C3, C4), occipital (O1, O2), parietal (P3, P4 and Pz) and temporal (T5 and T6) leads (red curves) for EEG patterns of a healthy person (data from [12]).

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4. Fig. 3. Dependencies h(q) (a, c) and singularity spectra (b, d) for EEG patterns of a patient with schizophrenia (a, b) and depression (c, d). Frontal and central leads (F3, Fz, F4, C3, C4) (blue curves), occipital (O1, O2), parietal (P3, P4, Pz) and (T5, T6) temporal leads (red curves) (data from [12]).

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5. Fig. 4. Examples of EEG power spectra for a healthy person (a–b) and a patient with atrial fibrillation (g–e). The initial (mixed) power spectra (a, e), the averaged mixed spectra are marked in blue, the fractal components of the spectra are marked in red (b, e), the averaged vibrational spectra (c, e). O1 lead (data from [1]).

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6. Fig. 5 Averaged spectra of the singularity D(h) for the EEG of a healthy person for various leads (a–b) and for a patient with atrial fibrillation (g–e) (data from [1]).

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