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@article{IVP_2023_31_5_a10, author = {M. O. Zhuravlev and A. S. Akimova and O. S. Panina and A. R. Kiselev}, title = {Oscillatory characteristics in the brain activity of the newborns and their correlation with different gestational ages}, journal = {Izvestiya VUZ. Applied Nonlinear Dynamics}, pages = {650--660}, publisher = {mathdoc}, volume = {31}, number = {5}, year = {2023}, language = {ru}, url = {http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a10/} }
TY - JOUR AU - M. O. Zhuravlev AU - A. S. Akimova AU - O. S. Panina AU - A. R. Kiselev TI - Oscillatory characteristics in the brain activity of the newborns and their correlation with different gestational ages JO - Izvestiya VUZ. Applied Nonlinear Dynamics PY - 2023 SP - 650 EP - 660 VL - 31 IS - 5 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a10/ LA - ru ID - IVP_2023_31_5_a10 ER -
%0 Journal Article %A M. O. Zhuravlev %A A. S. Akimova %A O. S. Panina %A A. R. Kiselev %T Oscillatory characteristics in the brain activity of the newborns and their correlation with different gestational ages %J Izvestiya VUZ. Applied Nonlinear Dynamics %D 2023 %P 650-660 %V 31 %N 5 %I mathdoc %U http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a10/ %G ru %F IVP_2023_31_5_a10
M. O. Zhuravlev; A. S. Akimova; O. S. Panina; A. R. Kiselev. Oscillatory characteristics in the brain activity of the newborns and their correlation with different gestational ages. Izvestiya VUZ. Applied Nonlinear Dynamics, Tome 31 (2023) no. 5, pp. 650-660. http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a10/
[1] Massimini M., Huber R., Ferrarelli F., Hill S., Tononi G., “The sleep slow oscillation as a traveling wave”, J. Neurosci., 24:31 (2004), 6862–6870 | DOI
[2] Gabdrakipova A. A., Chervatyuk M. I., Mischenko A. N., “Son kak marker zdorovya”, European Research, 2017, no. 7(30), 69–71
[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, 8 (2017), 42 | DOI
[4] Stevenson N. J., Oberdorfer L., Koolen N., O’Toole J. M., Werther T., Klebermass-Schrehof K., Vanhatalo S., “Functional maturation in preterm infants measured by serial recording of cortical activity”, Sci. Rep., 7:1 (2017), 12969 | DOI
[5] O'Toole J. M., Boylan G. B., Vanhatalo S., Stevenson N. J., “Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram”, Clin. Neurophysiol, 127:8 (2016), 2910–2918 | DOI
[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, 128:6 (2017), 1100–1108 | DOI
[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, 15:3 (2018), 036004 | DOI
[8] Kiselev A. R., Drapkina O. M., Novikov M. Y., Panina O. S., Chernenkov Y. V., Zhuravlev M. O., Runnova A. E., “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., 12:1 (2022), 21593 | DOI
[9] Heraghty J. L., Hilliard T. N., Henderson A. J., Fleming P. J., “The physiology of sleep in infants”, Arch. Dis. Child, 93:11 (2008), 982–985 | DOI
[10] Scher M. S., Loparo K. A., “Neonatal EEG/sleep state analyses: a complex phenotype of developmental neural plasticity”, Dev. Neurosci., 31:4 (2009), 259–275 | DOI
[11] Villa M. P., 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, 117:2 (2000), 460–466 | DOI
[12] Anders T. F., Keener M. A., Kraemer H., “Sleep-wake state organization, neonatal assessment and development in premature infants during the first year of life. II”, Sleep, 8:3 (1985), 193–206 | DOI
[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., 11:1 (2021), 18505 | DOI
[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, 31:7 (2021), 073110 | DOI
[15] Runnova A. E., Zhuravlev M. O., Pysarchik A. N., Khramova M. V., Grubov V. V., “The study of cognitive processes in the brain EEG during the perception of bistable images using wavelet skeleton”, Proc. SPIE. Dynamics and Fluctuations in Biomedical Photonics XIV (3 March 2017, San Francisco, California, United States), v. 10063, SPIE, 2017, 1006319 | DOI
[16] Maksimenko V. A., Runnova A. E., Zhuravlev M. O., Makarov V. V., Nedayvozov V., Grubov V. V., Pchelintceva S. V., Hramov A. E., Pisarchik A. N., “Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface”, PLoS ONE, 12:12 (2017), e0188700 | DOI
[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, 232:5 (2023), 595–603 | DOI