Coordination of the EEG and the heart rate of preterm neonates during quiet sleep

Schwab, Karin; Skupin, Helga; Eiselt, M.; Walther, Mario; Voss, Andreas; Witte, Herbert

doi:10.1016/j.neulet.2009.09.031

Cited by 17 publications

(11 citation statements)

References 19 publications

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“…During the quiet sleep of preterm neonates, the EEG alternates between a high-voltage burst discharge and a suppressed interburst activity ("tracé discontinue" EEG pattern), where a HR acceleration is coupled to the burst onset. We found that the higher the burst amplitude ( > 60 μV), the more pronounced is the HR change [33]. It should be noted here that preterm neonates have a deficit in ANS activity and a sympathetic-parasympathetic imbalance characterized by sympathetic predominance [17].…”

Section: Comparison With the Results Of Other Hrv-eeg Coupling Studiesmentioning

confidence: 98%

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure

Piper¹,

Schiecke

Leistritz

et al. 2014

Biomedical Engineering / Biomedizinische Technik

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An innovative concept for synchronization analysis between heart rate (HR) components and rhythms in EEG envelopes is represented; it applies time-variant analyses to heart rate variability (HRV) and EEG, and it was tested in children with temporal lobe epilepsy (TLE). After a removal of ocular and movement-related artifacts, EEG band activity was computed by means of the frequency-selective Hilbert transform providing envelopes of frequency bands. Synchronization between HRV and EEG envelopes was quantified by Morlet wavelet coherence. A surrogate data approach was adapted to test for statistical significance of time-variant coherences. Using this processing scheme, significant coherence values between a HRV low-frequency sub-band (0.08-0.12 Hz) and the EEG δ envelope (1.5-4 Hz) occurring both in the preictal and early postictal periods of a seizure can be shown. Investigations were performed for all electrodes at 20-s intervals and for selected electrode pairs (T3÷C3, T4÷C4) in a time-variant mode. Synchronization was more pronounced in the group of right hemispheric TLE patients than in the left hemispheric group. Such a group-specific augmentation of synchronization confirms the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the low-frequency HRV components.

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Section: Comparison With the Results Of Other Hrv-eeg Coupling Studiesmentioning

confidence: 98%

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure

Piper¹,

Schiecke

Leistritz

et al. 2014

Biomedical Engineering / Biomedizinische Technik

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“…Couplings between cortical activity and the autonomic nervous system (ANS) were investigated, e.g. in experimental animal studies [1], by means of polysomnographic studies [2,3], during sleep in fullterm and preterm neonates [4,5] as well as in experimental studies e.g. of resting state [6].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Directed Interactions Between HRV and EEG Activity in Children With TLE

Schiecke

Pester

Piper

et al. 2016

IEEE Trans. Biomed. Eng.

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“…Initially, brain and cardiovascular interactions during sleep were studied only indirectly, correlating indexes of cardiac and cerebral variability to each other through simple non-dynamical analyses [9][10][11][12][13][14]. Dynamic approaches to the joint characterization of cardiac and neural time series were introduced only recently, and were based on bivariate and non-causal analyses [15][16][17][18][19][20][21][22]. However, complex physiological networks are composed of multiple nodes, where each node may represent the activity of a specific physiological system that exhibits autonomous dynamics but is also connected to other diverse systems.…”

Section: Introductionmentioning

confidence: 99%

Predictability decomposition detects the impairment of brain–heart dynamical networks during sleep disorders and their recovery with treatment

Faes

Marinazzo

Stramaglia

et al. 2016

Phil. Trans. R. Soc. A.

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This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac processη) and the amplitude of the different electroencephalographic waves (brain processes δ, θ, α, σ, β) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction between the sources. The framework is here applied to theη,δ,θ,α,σ,βtime series measured from the sleep recordings of eight severe sleep apnoea-hypopnoea syndrome (SAHS) patients studied before and after long-term treatment with continuous positive airway pressure (CPAP) therapy, and 14 healthy controls. Results show that the full and self-predictability of η, δ and θ decreased significantly in SAHS compared with controls, and were restored with CPAP forδandθbut not forη The causal predictability of η and δ occurred through significantly redundant source interaction during healthy sleep, which was lost in SAHS and recovered after CPAP. These results indicate that predictability analysis is a viable tool to assess the modifications of complexity and causality of the cerebral and cardiac processes induced by sleep disorders, and to monitor the restoration of the neuroautonomic control of these processes during long-term treatment.

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Coordination of the EEG and the heart rate of preterm neonates during quiet sleep

Cited by 17 publications

References 19 publications

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure

Nonlinear Directed Interactions Between HRV and EEG Activity in Children With TLE

Predictability decomposition detects the impairment of brain–heart dynamical networks during sleep disorders and their recovery with treatment

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