ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Zhuravlev M. O., Akimova A. S., Panina O. S., Kiselev A. R. Oscillatory characteristics in the brain activity of the newborns and their correlation with different gestational ages. Izvestiya VUZ. Applied Nonlinear Dynamics, 2023, vol. 31, iss. 5, pp. 650-660. DOI: 10.18500/0869-6632-003063, EDN: SOMZPJ

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Language: 
Russian
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/0869-6632-003063
EDN: 
SOMZPJ

Oscillatory characteristics in the brain activity of the newborns and their correlation with different gestational ages

Autors: 
Zhuravlev Maksim Olegovich, Saratov State University
Akimova Alesia Sergeevna, Saratov State University
Panina Olga Sergeevna, Saratov State Medical University named after V. I. Razumovsky
Kiselev Anton Robertovich, Saratov research Institute of Cardiology
Abstract: 

The purpose of this study is to detect the characteristic features of the oscillatory electrical activity of the brain in early postnatal development, depending on the gestational age of newborns.

Methods. The study is based on automatic processing of clinical data from electroencephalography of newborns on the third day after birth. Behavioral characteristics assessed periods of sleep and wakefulness, without a precise division into stages of sleep and various states of wakefulness. The processing of multichannel electroencephalography signals was carried out on the basis of the method of modifying the continuous wavelet transform (CWT), which makes it possible to estimate the average characteristics of the number, duration and energy of oscillatory components (patterns) developing in different frequency ranges.

Results. A paradoxical picture has been demonstrated describing the state of sleep and wakefulness in weakly preterm infants. For this group of children, the number and average energy of patterns detected in the frequency ranges from 1 to 20 Hz behave in a reflected way during sleep compared to children born at the usual time. At the same time, the average duration of oscillatory patterns remains unchanged.

Conclusion. In the first days of a child’s life, it is possible to detect significant differences in the activity of the brain of newborns with slightly different gestational age during sleep/wake behavioral states. Quantitative estimates of the parameters of oscillatory CWT patterns are promising for use as the basis for systems for automatic processing of neonatal brain activity, additional to amplitude electroencephalography estimates. Such systems may be relevant for the search for early signs of anomalies in the development of the central nervous system.

Key words: 
continuous wavelet analysis
automatic recognition
oscillatory patterns
brain activity
sleep
newborns
Acknowledgments: 
The study was conducted with the financial support of the Russian Science Foundation (Project No. 22-22-00517)
Reference: 
  1.  Massimini M, Huber R, Ferrarelli F, Hill S, Tononi G. The sleep slow oscillation as a traveling wave. J. Neurosci. 2004;24(31):6862–6870. DOI: 10.1523/JNEUROSCI.1318-04.2004.
  2. Gabdrakipova AA, Chervatyuk MI, Mishchenko AN. Sleep as a marker of health. European Research. 2017;(7(30)):69–71 (in Russian).
  3. Loddo G, Calandra-Buonaura G, Sambati L, Giannini G, Cecere A, Cortelli P, Provini F. The treatment of sleep disorders in Parkinson’s disease: From research to clinical practice. Front. Neurol. 2017;8:42. DOI: 10.3389/fneur.2017.00042.
  4. Stevenson NJ, Oberdorfer L, Koolen N, O’Toole JM, Werther T, Klebermass-Schrehof K, Vanhatalo S. Functional maturation in preterm infants measured by serial recording of cortical activity. Sci. Rep. 2017;7(1):12969. DOI: 10.1038/s41598-017-13537-3.
  5. O’Toole JM, Boylan GB, Vanhatalo S, Stevenson NJ. Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram. Clin. Neurophysiol. 2016;127(8):2910–2918. DOI: 10.1016/j.clinph.2016.02.024.
  6. Koolen N, Oberdorfer L, Rona Z, Giordano V, Werther T, Klebermass-Schrehof K, Stevenson N, Vanhatalo S. Automated classification of neonatal sleep states using EEG. Clin. Neurophysiol. 2017;128(6):1100–1108. DOI: 10.1016/j.clinph.2017.02.025.
  7. Pillay K, Dereymaeker A, Jansen K, Naulaers G, Van Huffel S, De Vos M. Automated EEG sleep staging in the term-age baby using a generative modelling approach. J. Neural Eng. 2018;15(3):036004. DOI: 10.1088/1741-2552/aaab73.
  8. Kiselev AR, Drapkina OM, Novikov MY, Panina OS, Chernenkov YV, Zhuravlev MO, Runnova AE. Examining time-frequency mechanisms of full-fledged deep sleep development in newborns of different gestational age in the first days of their postnatal development. Sci. Rep. 2022;12(1):21593. DOI: 10.1038/s41598-022-26111-3.
  9. Heraghty JL, Hilliard TN, Henderson AJ, Fleming PJ. The physiology of sleep in infants. Arch. Dis. Child. 2008;93(11):982–985. DOI: 10.1136/adc.2006.113290.
  10. Scher MS, Loparo KA. Neonatal EEG/sleep state analyses: a complex phenotype of developmental neural plasticity. Dev. Neurosci. 2009;31(4):259–275. DOI: 10.1159/000216537.
  11. Villa MP, Calcagnini G, Pagani J, Paggi B, Massa F, Ronchetti R. Effects of sleep stage and age on short-term heart rate variability during sleep in healthy infants and children. Chest. 2000;117(2):460–466. DOI: 10.1378/chest.117.2.460.
  12. Anders TF, Keener MA, Kraemer H. Sleep-wake state organization, neonatal assessment and development in premature infants during the first year of life. II. Sleep. 1985;8(3):193–206. DOI: 10.1093/sleep/8.3.193.
  13. Runnova A, Zhuravlev M, Ukolov R, Blokhina I, Dubrovski A, Lezhnev N, Sitnikova E, Saranceva E, Kiselev A, Karavaev A, Selskii A, Semyachkina-Glushkovskaya O, Penzel T, Jurgen Kurths J. Modified wavelet analysis of ECoG-pattern as promising tool for detection of the blood–brain barrier leakage. Sci. Rep. 2021;11(1):18505. DOI: 10.1038/s41598-021-97427-9.
  14. Sergeev K, Runnova A, Zhuravlev M, Kolokolov O, Akimova N, Kiselev A, Titova A, Slepnev A, Semenova N, Penzel T. Wavelet skeletons in sleep EEG-monitoring as biomarkers of early diagnostics of mild cognitive impairment. Chaos. 2021;31(7):073110. DOI: 10.1063/5.0055441.
  15. Runnova АE, Zhuravlev MO, Pysarchik AN, Khramova MV, Grubov VV. The study of cognitive processes in the brain EEG during the perception of bistable images using wavelet skeleton. In: Proc. SPIE. Vol. 10063. Dynamics and Fluctuations in Biomedical Photonics XIV. 3 March 2017, San Francisco, California, United States. SPIE; 2017. P. 1006319. DOI: 10.1117/12.2250403.
  16. Maksimenko VA, Runnova AE, Zhuravlev MO, Makarov VV, Nedayvozov V, Grubov VV, Pchelintceva SV, Hramov AE, Pisarchik AN. Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface. PLoS ONE. 2017;12(12):e0188700. DOI: 10.1371/journal.pone.0188700.
  17. Simonyan M, Fisun A, Afanaseva G, Glushkovskaya-Semyachkina O, Blokhina I, Selskii A, Zhuravlev M, Runnova A. Oscillatory wavelet-patterns in complex data: mutual estimation of frequencies and energy dynamics. Eur. Phys. J. Spec. Top. 2023;232(5):595–603. DOI: 10.1140/epjs/ s11734-022-00737-w. 
Received: 
22.05.2023
Accepted: 
19.08.2023
Available online: 
19.09.2023
Published: 
29.09.2023
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