Compensatory mechanisms of reduced interhemispheric EEG connectivity during sleep in patients with apnea

Zhuravlev, Maksim O.; Agaltsov, M. V.; Kiselev, Anton R.; Simonyan, Margarita A.; Novikov, Mikhail; Selskii, Anton O.; Ukolov, Rodion; Drapkina, О. М.; Orlova, A. A.; Penzel, Thomas; Runnova, Anastasiya E.

doi:10.1038/s41598-023-35376-1

Cited by 4 publications

(1 citation statement)

References 69 publications

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“…Another study performed the mathematical analysis of functional connectivity in the EEG of patients with OSA and healthy subjects by calculating wavelet bicoherence from nighttime polysomnograms. After observing the interhemispheric synchronization deterioration, this study demonstrated that high-frequency EEG activity includes a compensatory increase in intrahemispheric connection and a modest increase in the connectivity of the central and occipital lobes 14 . OSA has been linked to structural brain changes in areas related to memory and Alzheimer's disease, and a recent study showed that OSA is becoming more widely acknowledged as a risk factor for cognitive decline.…”

Section: Introductionmentioning

confidence: 85%

Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals

Rajeswari,

Jagannath

2024

Sci Rep

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Obstructive sleep apnea (OSA) is a disorder which blocks the upper airway during sleep. The severity of OSA will lead heart attack, stroke and end of life. This proposed study explored the classification of OSA and healthy subjects using brain connectivity analysis from electroencephalogram (EEG) signals. Institute of System and Robotics—University of Coimbra (ISRUC) database were used for acquiring 50 EEG signals using 4 channels and noise removal has been accomplished by 50 Hz notch filter. The Institute of System and Robotics—University of Coimbra (ISRUC) database contained 50 EEG signals, with four channels, and a 50 Hz notch filter was applied to remove noise. Wavelet packet decomposition method was performing the segregation of EEG signals into five bands; Gamma (γ), beta (β), alpha (α), theta (θ) and delta (δ). A total of 4 electrode positions were used for the brain connectivity analysis for each EEG band. Pearson correlation method was effectively used for measuring the correlation between healthy and OSA subjects. The nodes and edges were highlighted the connection between brain and subjects. The highest correlation was achieved in delta band of OSA subjects which starts from 0.7331 to 0.9172 respectively. For healthy subjects, the positive correlation achieved was 0.6995. The delta band has been correlated well with brain when compared other bands. It has been noted that the positive correlation well associated with brain in OSA subjects, which classifies OSA from healthy subjects.

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Section: Introductionmentioning

confidence: 85%

Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals

Rajeswari,

Jagannath

2024

Sci Rep

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Wavelet analysis of intermittent dynamics in nocturnal electrocardiography and electroencephalography data

Zhuravlev,

Egorov,

Moskalenko

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This paper presents the results of a study of the characteristics of phase synchronization between electrocardiography(ECG) and electroencephalography (EEG) signals during night sleep. Polysomnographic recordings of eight generally healthy subjects and eight patients with obstructive sleep apnea syndrome were selected as experimental data. A feature of this study was the introduction of an instantaneous phase for EEG and ECG signals using a continuous wavelet transform at the heart rate frequency using the concept of time scale synchronization, which eliminated the emergence of asynchronous areas of behavior associated with the “leaving” of the fundamental frequency of the cardiovascular system. Instantaneous phase differences were examined for various pairs of EEG and ECG signals during night sleep, and it was shown that in all cases the phase difference exhibited intermittency. Laminar areas of behavior are intervals of phase synchronization, i.e., phase capture. Turbulent intervals are phase jumps of 2π. Statistical studies of the observed intermittent behavior were carried out, namely, distributions of the duration of laminar sections of behavior were estimated. For all pairs of channels, the duration of laminar phases obeyed an exponential law. Based on the analysis of the movement of the phase trajectory on a rotating plane at the moment of detection of the turbulent phase, it was established that in this case the eyelet intermittency was observed. There was no connection between the statistical characteristics of laminar phase distributions for intermittent behavior and the characteristics of night breathing disorders (apnea syndrome). It was found that changes in statistical characteristics in the phase synchronization of EEG and ECG signals were correlated with blood pressure at the time of signal recording in the subjects, which is an interesting effect that requires further research.

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Behavioral measurement of interhemispheric interactions during multiple episodes of falling asleep

Manaenkov,

Prokhorenko,

Sveshnikov

et al. 2023

Eur. Phys. J. Spec. Top.

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Compensatory mechanisms of reduced interhemispheric EEG connectivity during sleep in patients with apnea

Cited by 4 publications

References 69 publications

Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals

Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals

Wavelet analysis of intermittent dynamics in nocturnal electrocardiography and electroencephalography data

Behavioral measurement of interhemispheric interactions during multiple episodes of falling asleep

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