DOI: https://doi.org/10.31071/kit2018.14.05

Inventory reference

ISSN 1812-7231 Klin.inform.telemed. Volume 13, Issue 14, 2018, Pages 37–46

Author(s)

O. Yu. Mayorov ^{1, 2, 4}, V. N. Fenchenko ^{1, 2, 3}

Institution(s)

¹Kharkiv Medical Academy of Postgraduate Education, Ministry of Healthcare of Ukraine, ²Institute for Medical Informatics and Telemedicine LTD(Kharkiv), ³Physico-Technical Institute of Low Temperatures NAS of Ukraine named after B. I. Verkin (Kharkiv), ⁴Institute of Children and Adolescents Health protection NAMS of Ukraine (Kharkiv)

Article title

Method of detection of schizophrenic row disorders at early stages in patients from groups with "functional psychoses" basing on EEG scaling indicators

Abstract (resume)

Introduction. The use of various hardware methods — PET, fMRI, qEEG has deepened the understanding of the schizophrenic state, however, still, no valid "neuromarkers" of schizophrenia were identified that reliably distinguished schizophrenia patients from groups of patients with other "functional psychoses". There are no standard criteria for diagnosing schizophrenia based on EEG. Significant differences in the EEG of patients with schizophrenia and healthy individuals are detected only when comparing the averaged data for large groups of patients and healthy ones. In this paper, it was proposed to use reliable mathematical indicators to compare individual EEG parameters with reference groups for classifying patients (identifying schizophrenia and other "functional" psychosis).

Methods. Three reference groups of male subjects, 20–26 years old, were studied: healthy (35), patients with depression (34), untreated schizophrenic patients (28), whose diagnosis was clinically confirmed. Studies were conducted in a state of calm wakefulness and during mental load (counting in the mind). EEG was recorded monopolarly on a 24-channels electroencephalograph ("DX-systems", Ukraine) with an averaged reference electrode, with an arrangement of electrodes in the 10–20 system, with a discretisation frequency of 400 Hz. Frontal (F3, F4), parietal (P3, P4), and temporal (T3, T4) leads were studied.

Results. A study of scaling EEG indicators was performed. Multifractal Detrended Fluctuation Analysis (МDFA) was used. Its application is most effective given the heterogeneity and nonstationarity of the signal and has a number of features due to the complex structure and specific nature of the EEG. A new method for the early diagnosis of mental disorders of the schizophrenic series according to scaling EEG indicators registered in the state of rest and mental load with subsequent patient classification has been proposed.

Keywords

EEG, Scaling EEG indicators, "Neuromarkers", Schizophrenia, Depression.

References

1. Friston K. J. Theoretical neurobiology and schizophrenia. Brain Med. Bull. 1996. vol. 52, no. 3, pp. 644–655.
https://doi.org/10.1093/oxfordjournals.bmb.a011573

2. Woodruff P., Murray R. The aetiology of brain abnormalities in schizophrenia. In: Ancil R. J., Holliday S., Higenbottam J., eds. Schizophrenia: Exploring the Spectrum of Psychosis. Chichester, UK, Wiley, 1994, pp. 95–144.

3. Andreasen N. C. A Unitary Model of Schizophrenia: Bleuler's "Fragmented Phrene" as Schizencephaly. Arch. Gen. Psychol., 1999. vol. 56. no. 9. pp. 781–787.
https://doi.org/10.1001/archpsyc.56.9.781

4. Peled A. Multiple constraint organization in the brain: A the ory for schizophrenia. Brain Res. Bull. 1999, vol. 49, iss. 4, pp. 245-250.
https://doi.org/10.1016/S0361-9230(99)00048-9

5. Coutin-Churchman Р., Anez У., Uzcategui М., Alvarez L., Vergara F., Mendez L., Fleitas R. Quantitative spectral analysis of EEG in psychiatry revisited: drawing signs out of numbers in а clinical setting. Clin. Neurophysiol., 2003, vol. 114, pp. 2294–2306.
https://doi.org/10.1016/S1388-2457(03)00228-1

6. Gruzelier J. Н. Theory, methods and new directions in the psychophysiology of the schizophrenic process and schizotypy. Int. J. Psychophysiol., 2003, vol. 48, pp. 221–245.
https://doi.org/10.1016/S0167-8760(03)00055-2

7. Oswiecimka P., Kwapin J., Drozdz S. Wavelet versus detrended fluctuation analysis of multifractal structures. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics. 2006, vol. 74, pp. 161–203.
https://doi.org/10.1103/PhysRevE.74.016103

8. Mayorov O. Yu. and Fenchenko V. N. Searching for "Neuromakers" Characteristic for Pathologic Changes in Schizophrenia by Using the Scaling Indices of the Cerebral Bioelectric Activity. European J. of Biomed. Informatics, 2018, vol. 14, iss. 1, pp. 67–74.
https://doi.org/10.24105/ejbi.2018.14.1.11

9. Mandelbrot B. Possible refinement of the lognormal hypothe sis concerning the distribution of energy dissipation in intermittent turbulence. Proc. Sympos. Statistical Models and Turbulence, Eds M. Rosenblatt, C. Van Atta, 1972. pp. 333–351.
https://doi.org/10.1007/3-540-05716-1_20

10. Mandelbrot B. B. Intermittent turbulence in self-similar cascades: divergence of high moments and dimension of the carrier. J. of Fluid Mechanics., 1974, vol. 62, no. 2, pp. 331–358.
https://doi.org/10.1017/S0022112074000711

11. Parisi G., Frisch U. On the singularity structure of fully deve loped turbulence. Proc. of the Intern. School of Physics "Enrico Fermi", 1985, pp. 84–87.

12. Benzi R., Paladin G., Parisi G., Vulpiani A. On the multifractal nature of fully developed turbulence and chaotic systems. J. of Physics A: Mathematical and General., 1984, vol. 17, no. 18, pp. 3521–3531.
https://doi.org/10.1088/0305-4470/17/18/021

13. Ivanov P. C., Amaral L. A., Goldberger A. L. et al. Multifractality in human heartbeat dynamics. Nature. 1999, vol. 399, iss. 6735, pp. 461–465.
https://doi.org/10.1038/20924

14. Ivanov P. C., Nunes C., Amaral L. A., Goldberger A. L. et al. From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos, Woodbury, N. Y., 2001, vol. 11, no. 3. pp. 641–652.
https://doi.org/10.1103/PhysRevLett.67.3515

15. Muzy J. F., Bacry E. and Arneodo A. Wavelets and multifractal formalism for singular signals: Application to turbulence data. Phys. Rev. Lett., 1991, vol. 67, iss. 25, 3515.
https://doi.org/10.1103/PhysRevLett.67.3515

16. Muzy J. F., Bacry E. and Arneodo A. Multifractal formalism for fractal signals: The structure-function approach versus the wavelet-transform modulus-maxima method. Phys. Rev., 1993, E 47, iss.2, 875.
https://doi.org/10.1103/PhysRevE.47.875

17. Muzy J. F., Bacry E. and Arneodo A. The multifractal formalism revisited with wavelets. Intern. J. of Bifurcation and Chaos, 1994, vol. 04, no. 02, pp. 245–302.
https://doi.org/10.1142/S0218127494000204

18. Kantelhardt J. W., Zschiegner S. A., Bunde A., Havlin S., Koscielny-Bunde E., Stanley H. E. Multifractal detrended fluctuation analysis of non-stationary time series. Physica A., 2002, no. 316, pp. 87–114.
https://doi.org/10.1016/S0378-4371(02)01383-3

19. Kantelhardt J. W., Koscielny-Bunde E., Rego H. A., Havlin S., Bunde A. Detecting long-range correlations with detrended fluctuation analysis. Physica A., 2001, no. 295, pp. 441–454.
https://doi.org/10.1016/S0378-4371(01)00144-3

20. Peng C.-K., Havlin S., Stanley H. E., Goldberger A. L., Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. CHAOS, 1995, vol. 5, no. 1, pp. 82–87.
https://doi.org/10.1063/1.166141

21. Stanley H. E., Amaral L. A., Goldberger A. L., Havlin S., Ivanov P. Ch., Peng C. K., Statistical physics and physiology: monofractal and multifractal approaches. Physica A., 1999, vol. 270, iss. 1–2, pp. 309–324.
https://doi.org/10.1016/S0378-4371(99)00230-7

22. Veneziano D., Moglen G. E., Bras R. L. Multifractal analysis: pitfalls of standard procedures and alternatives. Phys. Rev. E., 1995. vol.52, pp. 1387–1398.
https://doi.org/10.1103/PhysRevE.52.1387

23. Bishop C. M. Neural Networks for Pattern Recognition. Oxford: Clarendon Press, 1995, 482 p.

24. Haykin S., Neural networks and learning machines. 3rd ed., Pearson Educ., Inc., Upper Saddle River, N. J., Prentice Hall, Inc., 2009, 906 p.

25. Ruthowski L. Computational Intelligence. Methodsand Techniques. Berlin-Heidelberg: Springer-Verlag, 2008, 514 p.

26. Du Ke-Lin, Swamy M. N. S. Neural Networks and Statistical Learning. Springer-Verlag London, 2014, 834 p.
https://doi.org/10.1007/978-1-4471-5571-3

27. Jang, J.-S. R. ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. Syst., Man, and Cybern., 1993, vol. 23, no. 3, pp. 665–685.
https://doi.org/10.1109/21.256541

28. Perova I., Mulesa P. Fuzzy spacial extrapolation method using Manhattan metrics for tasks of Medical Data mining. Proc. Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT), 2015, pp. 104–106.
https://doi.org/10.1109/STC-CSIT.2015.7325443

29. Tetsuya Takahashi, Takashi Goto, Sou Nobukawa, and oth. Abnormal functional connectivity of high-frequency rhythms in drug-naive schizophrenia. Clin. Neurophysiology., 2018, iss. 129, pр. 222–231.
https://doi.org/10.1016/j.clinph.2017.11.004

30. Mayorov O. Yu., Fenchenko V. M. Multifractal analysis in the study of brain bioelectrical activity. Zhurnal Kibernetika i vychislitel'naya tekhnika. [Cybernetics and Computing.], 2015, iss. 181, pp. 81–94.
https://doi.org/10.15407/kvt181.01.070

31. Goldman D. The clinical use of the "average" reference electrode in monopolar recording. Electroencephalogr. Clin. Neurophysiol., 1950, vol. 2, pp. 209 – 212.
https://doi.org/10.1016/0013-4694(50)90039-3

32. Offner F. F. The EEG as potential mapping: the value of the average monopolar reference. Electroencephalogr. Clin. Neurophysiol., 1950, vol. 2, pp. 213–214.
https://doi.org/10.1016/0013-4694(50)90040-X

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