Spectral analysis of sleep EEG in patients with chronic disorders of consciousness by multitaper discrete Fourier transform

Abstract

Background. In recent years, EEG spectral analysis has become increasingly popular due to the development of computer technologies. Among the methods of spectral analysis, various variants of the window Fourier transform are most often used, taking into account the non-stationary nature of the EEG signal.

Aims: study of the spectral composition of sleep EEG in patients with chronic disorders of consciousness by the method of discrete Fourier transform with windows in the form of spheroidal sequences.

Methods. In this article, the spectral composition of the sleep EEG of 32 patients with impaired consciousness was studied using a discrete Fourier transform with windows in the form of spheroidal sequences. For spectral analysis of EEG sleep, we used polysomnography data obtained overnight. To construct hypnospectrograms, visualize data and research results, we used software written in the Python programming language using the NumPy, scipy, matplotlib, mne, yasa libraries.

Results. The correlations between characteristic changes in the spectral composition of sleep EEG and the level of consciousness and the etiology of the disease were detected.

Conclusions. Consolidation of sleep and normalization of other circadian rhythms is an important component of both the somatic state of patients in intensive care and, possibly, will become a therapeutic target for the restoration of cognition in patients with chronic impairment of consciousness.

Keywords

chronic disorders of consciousness / vegetative state / minimally consciouse state / sleep / discrete Fourier transform

References

[1] Kohlschütter EO. Messung der festigkeit des schlafes. Zeitschrift für rationelle Medicin. 1863;3(17): 209–253.

[2] Berger H. Das Elektrenkephalogramm des menschen. Nova Acta Leopoldina. 1929;6(38):173–309.

[3] Loomis AL, Harvey EN, Hobart GA. Cerebral states during sleep, as studied by human brain potentials. J Exp Psychol. 1937;21(2):127–144. doi: 10.1037/h0057431.

[4] Aserinsky E, Kleitman N. Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science. 1953;118(3062):273–274. doi: 10.1126/science.118.3062.273.

[5] Rechtschaffen A, Kales A. A manual of standardized terminology, techniques, and scoring systems for sleep stages of human subjects. Washington, DC: Public Health Service; 1968.

[6] Rossi Sebastiano D, Visani E, Panzica F, et al. Sleep patterns associated with the severity of impairment in a large cohort of patients with chronic disorders of consciousness. Clin Neurophysiol. 2018;129(3):687–693. doi: 10.1016/ j.clinph.2017.12.012.

[7] Malinowska U, Chatelle C, Bruno MA, et al. Electroencephalographic profiles for differentiation of disorders of consciousness. Biomed Eng Online. 2013;12:109. doi: 10.1186/1475-925X-12-109.

[8] Pavlov YG, Gais S, Müller F, et al. Night sleep in patients with vegetative state. J Sleep Res. 2017;26(5):629–640. doi: 10.1111/jsr.12524.

[9] Mertel I, Pavlov Y, Barner C, et al. Sleep in severe disorders of consciousness: 24-h behavioral and polysomnographic recording. ResearchGate GmbH; 2020. doi: 10.1101/ 2020.05.21.20106807.

[10] Александров М.В., Иванов Л.Б., Лытаев С.А., и др. Электроэнцефалография: руководство / Под ред. М.В. Александрова. 3-е изд., перераб. и доп. — СПб.: СпецЛит, 2020. — 224 с. [Aleksandrov MV, Ivanov LB, Lytaev SA, et al. Ehlektroehntsefalografiya: rukovodstvo. Ed by M.V. Aleksandrov. 3rd revised and updated. Saint Petersburg: SpetsLit; 2020. 224 р. (In Russ.)]

[11] Slepian D. Prolate spheroidal wave functions, Fourier analysis, and uncertainty-V: the discrete case. Bell Sys Tech J. 1978; 57(5):1371–1430. doi: 10.1002/j.1538-7305.1978.tb02104.x.

[12] Thomson DJ. Spectrum estimation and harmonic analysis. Proc IEEE. 1982;70(9):1055–1096.

[13] Prerau MJ, Brown RE, Bianchi MT, et al. Sleep neurophysiological dynamics through the lens of multitaper spectral analysis. Physiology (Bethesda). 2017;32(1):60–92. doi: 10.1152/physiol.00062.2015

[14] Giacino JT, Kalmar K. CRS-R coma recovery scale-revised: administration and scoring guidelines. 2006. doi: 10.5281/zenodo.3818479

[15] Бакулин И.С., Кремнева Е.И., Кузнецов А.В., и др. Хронические нарушения сознания / Под ред. М.А. Пирадова. 2-е изд., перераб. и доп. — М.: Горячая линия — Телеком, 2020. — 288 с. [Bakulin IS, Kremneva EI, Kuznetsov AV, et al. Khronicheskie narusheniya soznaniya. Ed by M.A. Piradov. Moscow: Goryachaya liniya — Telekom; 2020. 288 p. (In Russ).]

[16] Vallat R, Jajcay N. [Raphael Vallat and Nikola Jajcay] raphaelvallat/yasa: v0.3.0 (Version v0.3.0). Zenodo; 2020. doi: 10.5281/zenodo.3818479

[17] Combrisson E, Vallat R, Baptiste EJ, et al. Sleep: an open-source python software for visualization, analysis, and staging of sleep data. Front Neuroinform. 2017;11:60. doi: 10.3389/fninf.2017.00060

[18] Avantaggiato P, Molteni E, Formica F, et al. Polysomnographic sleep patterns in children and adolescents in unresponsive wakefulness syndrome. J Head Trauma Rehabil. 2015;30(5): 334–346. doi: 10.1097/HTR.0000000000000122.

[19] Nekrasova J, Kanarski M, Yankevich D, et al. Retrospective analysis of sleep patterns in patients with chronic disorders of consciousness. Sleep Medicine: X. 2020;2:100024. doi: 10.1016/j.sleepx.2020.100024

RIGHTS & PERMISSIONS

Kanarskii M.M., Nekrasova I.Y., Borisov I.V., Yankevich D.S., Kolesov D.L., Lukyanets O.B., Gorshkov K.M., Shpichko N.P., Krylova T.N., Kovaleva N.Y., Lutkin O.Y., Evstifeev V.V.