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Oscillatory wavelet-patterns in complex data: mutual estimation of frequencies and energy dynamics

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Abstract

In this work, we propose a modification of the wavelet oscillatory pattern method for analyzing energy characteristics of oscillatory components in complex signals. The energy analysis of oscillatory wavelet patterns allows fast two-dimensional sorting of oscillatory components in frequency and power, thus allows for further statistical calculation of the observed technologies. Counting operations are simply realized on the base of parallel calculations. The presented technique could be used in studying the electrophysiological features of brain activity during animals sleep and awake. The method was used in investigations of brain’s electrophysiological characteristics during sleep and awake in animals. We found out that standard energy analysis could determine NREM sleep and awake condition in rats with normal weight and obesity. However, calculation of energy characteristics of the ECoG patterns in animals of two groups demonstrate a significant transformation of electrophysiological signals oscillatory structure during NREM sleep and awake in rats with severe visceral obesity. We suppose the changes of these characteristics may be associated with shifts in homeostasis indicators due to animal obesity.

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Data availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. A.S. Karavaev, A.S. Borovik, E.I. Borovkova, E.A. Orlova, M.A. Simonyan, V.I. Ponomarenko, V.V. Skazkina, V.I. Gridnev, B.P. Bezruchko, M.D. Prokhorov et al., Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure. Biophys. J. 120(13), 2657–2664 (2021)

    Article  ADS  Google Scholar 

  2. C. Metzner, A. Schilling, M. Traxdorf, H. Schulze, P. Krauss, Sleep as a random walk: a super-statistical analysis of eeg data across sleep stages. Commun. Biol. 4(1), 1–11 (2021)

    Article  Google Scholar 

  3. D. Parbat, M. Chakraborty, A novel methodology to study the cognitive load induced eeg complexity changes: Chaos, fractal and entropy based approach. Biomed. Signal Process. Control 64, 102277 (2021)

    Article  Google Scholar 

  4. J. Lerga, N. Saulig, L. Stanković, D. Seršić, Rule-based EEG classifier utilizing local entropy of time-frequency distributions. Mathematics 9(4), 451 (2021). https://doi.org/10.3390/math9040451

    Article  Google Scholar 

  5. R.A. Jaswal, S. Dhingra, J.D. Kumar, Designing multimodal cognitive model of emotion recognition using voice and EEG signal, in Recent Trends in Electronics and Communication (Springer, London, 2022), pp. 581–592

    Chapter  Google Scholar 

  6. A. Runnova, A. Selskii, E. Emelyanova, M. Zhuravlev, M. Popova, A. Kiselev, R. Shamionov, Modification of joint recurrence quantification analysis (JRQA) for assessing individual characteristics from short EEG time series. Chaos Interdiscip. J. Nonlinear Sci. 31(9), 093116 (2021)

    Article  Google Scholar 

  7. A.J. Mackintosh, R. de Bock, Z. Lim, V.-N. Trulley, A. Schmidt, S. Borgwardt, C. Andreou, Psychotic disorders, dopaminergic agents and EEG/MEG resting-state functional connectivity: a systematic review. Neurosci. Biobehav. Rev. 120, 354–371 (2021)

    Article  Google Scholar 

  8. T. Talukdar, A. Nikolaidis, C.E. Zwilling, E.J. Paul, C.H. Hillman, N.J. Cohen, A.F. Kramer, A.K. Barbey, Aerobic fitness explains individual differences in the functional brain connectome of healthy young adults. Cerebral Cortex 28, 1–10 (2017)

    Google Scholar 

  9. Z. Dai, J. De Souza, J. Lim, P.M. Ho, Y. Chen, J. Li, N. Thakor, A. Bezerianos, Y. Sun, EEG cortical connectivity analysis of working memory reveals topological reorganization in theta and alpha bands. Front. Hum. Neurosci. 11, 237 (2017)

    Article  Google Scholar 

  10. U. Braun, A. Schäfer, H. Walter, S. Erk, N. Romanczuk-Seiferth, L. Haddad, J.I. Schweiger, O. Grimm, A. Heinz, H. Tost et al., Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Natl. Acad. Sci. 112(37), 11678–11683 (2015)

    Article  ADS  Google Scholar 

  11. B. Schöne, T. Gruber, S. Graetz, M. Bernhof, P. Malinowski, Mindful breath awareness meditation facilitates efficiency gains in brain networks: a steady-state visually evoked potentials study. Sci. Rep. 8(1), 1–10 (2018)

    Article  Google Scholar 

  12. A. Runnova, A. Selskii, A. Kiselev, R. Shamionov, R. Parsamyan, M. Zhuravlev, Changes in EEG alpha activity during attention control in patients: association with sleep disorders. J. Personal. Med. 11(7), 601 (2021)

    Article  Google Scholar 

  13. A.D. Nordin, W.D. Hairston, D.P. Ferris, Faster gait speeds reduce alpha and beta EEG spectral power from human sensorimotor cortex. IEEE Trans. Biomed. Eng. 67(3), 842–853 (2019)

    Article  Google Scholar 

  14. S. Shustak, L. Inzelberg, S. Steinberg, D. Rand, M.D. Pur, I. Hillel, S. Katzav, F. Fahoum, M. De Vos, A. Mirelman et al., Home monitoring of sleep with a temporary-tattoo EEG, EOG and EMG electrode array: a feasibility study. J. Neural Eng. 16(2), 026024 (2019)

    Article  ADS  Google Scholar 

  15. P. Pearl, J. Beal, M. Eisermann, S. Misra, P. Plouin, S. Moshe, J. Riviello, D. Nordli, E. Mizrahi, Normal EEG in wakefulness and sleep, preterm, term, infant, adolescent, in Niedermeyer’s Electroecephalography: Basic Principles, Clinical Applications, and Related Fields, 7th edn (Oxford University Press, Oxford, 2018)

    Google Scholar 

  16. M. Sifuzzaman, M.R. Islam, M.Z. Ali, Application of wavelet transform and its advantages compared to Fourier transform. J. Phys. Sci. 13(1), 121–134 (2009)

    Google Scholar 

  17. A. Cohen, Wavelet methods in numerical analysis. Handb. Numer. Anal. 7, 417–711 (2000)

    MathSciNet  MATH  Google Scholar 

  18. P. Bhatia, J. Boudy, R. Andreão, Wavelet transformation and pre-selection of mother wavelets for ECG signal processing, in Proceedings of the 24th IASTED International Conference on Biomedical Engineering (2006) pp. 390–395

  19. A. Runnova, M. Zhuravlev, R. Ukolov, I. Blokhina, A. Dubrovski, N. Lezhnev, E. Sitnikova, E. Saranceva, A. Kiselev, A. Karavaev et al., Modified wavelet analysis of ECoG-pattern as promising tool for detection of the blood-brain barrier leakage. Sci. Rep. 11(1), 1–8 (2021)

    Article  Google Scholar 

  20. B. Torresani, Continuous Wavelet Transform, vol. 675 (Savoire, Paris, 1995), p.676

    Google Scholar 

  21. A.E. Hramov, A.A. Koronovskii, V.A. Makarov, A.N. Pavlov, E. Sitnikova, Wavelets in Neuroscience (Springer, London, 2015)

    Book  MATH  Google Scholar 

  22. A.N. Pavlov, A.E. Hramov, A.A. Koronovskii, E.Y. Sitnikova, V.A. Makarov, A.A. Ovchinnikov, Wavelet analysis in neurodynamics. Phys. Usp. 55(9), 845 (2012)

    Article  Google Scholar 

  23. E. Sitnikova, A.E. Hramov, V. Grubov, A.A. Koronovsky, Age-dependent increase of absence seizures and intrinsic frequency dynamics of sleep spindles in rats. Neurosci. J. 2014 (2014)

  24. E. Sitnikova, A.E. Hramov, V. Grubov, A. A. Koronovsky, Time-frequency characteristics and dynamics of sleep spindles in WAG/Rij rats with absence epilepsy. Brain research. 1543, 290-299 (2014)

    Article  Google Scholar 

  25. K. Sergeev, A. Runnova, M. Zhuravlev, O. Kolokolov, N. Akimova, A. Kiselev, A. Titova, A. Slepnev, N. Semenova, T. Penzel, Wavelet skeletons in sleep EEG-monitoring as biomarkers of early diagnostics of mild cognitive impairment. Chaos Interdiscip. J. Nonlinear Sci. 31(7), 073110 (2021)

    Article  Google Scholar 

  26. N. Percie du Sert, A. Ahluwalia, S. Alam, M.T. Avey, M. Baker, W.J. Browne, A. Clark, I.C. Cuthill, U. Dirnagl, M. Emerson et al., Reporting animal research: explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18(7), 3000411 (2020)

    Article  Google Scholar 

  27. M. Zhuravlev, A. Runnova, K. Smirnov, E. Sitnikova, Spike-wave seizures, NREM sleep and micro-arousals in WAG/Rij rats with genetic predisposition to absence epilepsy: developmental aspects. Life 12(4), 576 (2022)

    Article  ADS  Google Scholar 

  28. A. Runnova, M. Zhuravlev, A. Kiselev, R. Ukolov, K. Smirnov, A. Karavaev, E. Sitnikova, Automatic wavelet-based assessment of behavioral sleep using multichannel electrocorticography in rats. Sleep Breath. 25(4), 2251–2258 (2021)

    Article  Google Scholar 

  29. R.F. Woolson, W.R. Clarke, Statistical Methods for the Analysis of Biomedical Data (Wiley, New York, 2011)

    Google Scholar 

  30. S.W. Hughes, V. Crunelli, Thalamic mechanisms of EEG alpha rhythms and their pathological implications. Neuroscientist 11(4), 357–372 (2005)

    Article  Google Scholar 

  31. V.V. Makarov, M.O. Zhuravlev, A.E. Runnova, P. Protasov, V.A. Maksimenko, N.S. Frolov, A.N. Pisarchik, A.E. Hramov, Betweenness centrality in multiplex brain network during mental task evaluation. Phys. Rev. E 98(6), 062413 (2018)

    Article  ADS  Google Scholar 

  32. T. Tuncer, S. Dogan, A. Subasi, EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection. Biomed. Signal Process. Control 68, 102591 (2021)

    Article  Google Scholar 

  33. B. Venkata Phanikrishna, P. Pławiak, A. Jaya Prakash, A brief review on EEG signal pre-processing techniques for real-time brain-computer interface applications (2021)

  34. J.R. Speakman, Use of High-Fat Diets to Study Rodent Obesity as a Model of Human Obesity (Nature Publishing Group, Berlin, 2019)

    Book  Google Scholar 

  35. K. Kanasaki, D. Koya, Biology of obesity: lessons from animal models of obesity. J. Biomed. Biotechnol. 2011 (2011)

  36. V. Von Diemen, E.N. Trindade, M.R.M. Trindade, Experimental model to induce obesity in rats. Acta Cirurgica Brasileira 21, 425–429 (2006)

    Article  Google Scholar 

  37. L. Thibault, Chapter 13—animal models of dietary-induced obesity, in Animal Models for the Study of Human Disease. ed. by P.M. Conn (Academic Press, Boston, 2013), pp.277–303

    Chapter  Google Scholar 

  38. S.K. Panchal, H. Poudyal, A. Iyer, R. Nazer, A. Alam, V. Diwan, K. Kauter, C. Sernia, F. Campbell, L. Ward et al., High-carbohydrate, high-fat diet-induced metabolic syndrome and cardiovascular remodeling in rats. J. Cardiovasc. Pharmacol. 57(5), 611–624 (2011)

    Google Scholar 

  39. H.-J. Kim, S. Kim, A.Y. Lee, Y. Jang, O. Davaadamdin, S.-H. Hong, J.S. Kim, M.-H. Cho, The effects of gymnema sylvestre in high-fat diet-induced metabolic disorders. Am. J. Chin. Med. 45(04), 813–832 (2017)

    Article  Google Scholar 

  40. A.M. Stranahan, Models and mechanisms for hippocampal dysfunction in obesity and diabetes. Neuroscience 309, 125–139 (2015)

    Article  Google Scholar 

  41. R.K. Bains, S.E. Wells, D.M. Flavell, K.M. Fairhall, M. Strom, P. Le Tissier, I.C. Robinson, Visceral obesity without insulin resistance in late-onset obesity rats. Endocrinology 145(6), 2666–2679 (2004)

    Article  Google Scholar 

  42. A. Tchernof, J.-P. Després, Pathophysiology of human visceral obesity: an update. Physiol. Rev. (2013)

  43. M.-È. Piché, S.J. Weisnagel, L. Corneau, A. Nadeau, J. Bergeron, S. Lemieux, Contribution of abdominal visceral obesity and insulin resistance to the cardiovascular risk profile of postmenopausal women. Diabetes 54(3), 770–777 (2005)

    Article  Google Scholar 

  44. G.A. Chumakova, T.Y. Kuznetsova, M.A. Druzhilov, N.G. Veselovskaya, Visceral adiposity as a global factor of cardiovascular risk (in rus.). Russ. J. Cardiol. 5, 7–14 (2018)

    Article  Google Scholar 

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Acknowledgements

This work has been supported by the Russian Science Foundation (Project no. 22-72-10061) in part of the development of wavelet skeleton modification for data analysis. The experimental work with Wistar rats was carried out with the support of the RF Government Grant no. 075-15-2022-1094

M.S., A.R, M.O. would like to express their thanks to Dr. Olga Posnenkova (Institute of Cardiological Research, SSMU) for helpful discussions and general support of the work.

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Authors and Affiliations

  1. Institute of Cardiological Research, Saratov State Medical University, Saratov, 410005, Russia

    Margarita Simonyan, Anton Selskii & Anastasiya Runnova

  2. Department of Pathological Physiology named after Academician A. A. Bogomolets, Saratov State Medical University, Saratov, 410005, Russia

    Anna Fisun & Galina Afanaseva

  3. Laboratory of Smart Sleep, Saratov State University, Saratov, 410012, Russia

    Oxana Glushkovskaya-Semyachkina, Inna Blokhina, Anton Selskii & Maksim Zhuravlev

  4. Center for Coordination of Fundamental Scientific Activities, National Medical Research Center for Therapy and Preventive Medicine, Moscow, 101990, Russia

    Maksim Zhuravlev & Anastasiya Runnova

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  1. Margarita Simonyan
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Correspondence to Anastasiya Runnova.

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Simonyan, M., Fisun, A., Afanaseva, G. et al. Oscillatory wavelet-patterns in complex data: mutual estimation of frequencies and energy dynamics. Eur. Phys. J. Spec. Top. 232, 595–603 (2023). https://doi.org/10.1140/epjs/s11734-022-00737-w

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