A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men

Jurysta, Fabrice; Borne, P van de; Migeotte, Pierre-François; Dumont, Martine; Lanquart, Jean Pol; Degaute, Jean Paul; Linkowski, Paul

doi:10.1016/s1388-2457(03)00215-3

Cited by 147 publications

(124 citation statements)

References 29 publications

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“…The onset of sleep stages according to R&K is sometimes difficult to determine and appears to be different from the onset of the changes in the respective heart activity [7,22]. PDFA and other ECG analyzing methods should potentially be helpful for the definition and on-line detection of sleep stages, e.g., sleep-onset REM.…”

Section: Discussionmentioning

confidence: 99%

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Temporally Resolved Fluctuation Analysis of Sleep ECG

et al. 2007

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Section: Discussionmentioning

confidence: 99%

“…REM sleep is preceded by about 2 min of a relative activation of the sympathetic branch of the autonomic nervous system [7]. SWS onset is preceded by 12 ± 5 min of a relative vagal dominance [22]. Spontaneous behavioral arousals are preceded by around 8 s of pulse rate increase [6].…”

mentioning

confidence: 99%

Temporally Resolved Fluctuation Analysis of Sleep ECG

et al. 2007

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“…We hypothesised that similar to the mental stress condition, during non-REM sleep as reflecting cardiac parasympathetic activation, since it has been demonstrated a strong interaction between ANS activity and delta wave activity in the EEG during sleep [2] [3]. The aim of the present study was to examine depending on the sleep status and to evaluate the temporal relationship during sleep.…”

Section: Extended Abstractmentioning

confidence: 99%

Association between Phase Coupling of Respiratory Sinus Arrhythmia and Brain Activity during Sleep

Niizeki¹,

Narumi²,

Saitoh³

2017

World Congress on Electrical Engineering and Computer Systems and Science

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Extended AbstractOur previous study [1] demonstrated that mental stress in humans exerts an influence on the oscillations of respiratory sinus arrhythmia (RSA), inducing an incoherent phase lag with respect to breathing, i.e., decrease in the phase coherence a positive correlation with high-frequency component (HF) of the frequency domain measures of heart rate variability (HRV) and a negative correlation with sympathovagal balance (lowfrequency to HF; LF/HF), suggesting that the phase coupling of RSA could be used as a noninvasive measure for evaluating the activity of the autonomic nervous system (ANS). We hypothesised that similar to the mental stress condition, during non-REM sleep as reflecting cardiac parasympathetic activation, since it has been demonstrated a strong interaction between ANS activity and delta wave activity in the EEG during sleep [2] [3]. The aim of the present study was to examine depending on the sleep status and to evaluate the temporal relationship during sleep. Overnight EEG and ECG of seventeen healthy volunteers were recorded. The EEG was band-pass filtered to extract the activity in the delta, theta, alpha and beta ranges, and then Hilbert transform was applied to each extracted signal to calculate the analytic amplitude. Respiratory signal was derived from the amplitude modulation of R-waves caused by breathing. Beat-to-beat R-R intervals (RRI) were determined and the time series of the RRI was further band-pass filtered with a frequency range of 0.15-0.4 Hz to determine RSA. From the oscillatory signals of RSA and respiration, each analytic signal was constructed from a real signal and its Hilbert transform, and then the timen RSA and respiration was obtained with 10-s window. Using auto-and cross-correlation analyses, we found that periodicity of was quite similar to that of delta wave (93.3 min vs. 90.8 min, r = 0.900) and that related with highly significant crosscorrelation coefficients (0.46±0.11). T preceded changes in delta wave by 7.6 min on average. These s and suggest that the phase coupling analysis of RSA could provide important information on sleep status and cardiac autonomic modulation during sleep.

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“…The preprocessing employed was the same presented by Jurysta [10] and Faes [13], which is briefly explained as follows: with respect to EEG preprocessing, the channel C3-A2 was chosen to develop the EEG analysis. For each patient, the complete time series of EEG was processed to eliminate the accumulative effects in the data groups (linear detrended), and in that way demonstrate only the absolute changes in the values and allow that the potential cyclic behaviors were identified, Additionally of being filtered and a Fast Fourier Transform (FFT) was applied each 5 consecutive seconds to the window filtered EEG data, where it was calculated the strength of each sub-band EEG using trapezoidal integration.…”

Section: Preprocessingmentioning

confidence: 99%

“…The authors of this work found out that the β waves are the node that sends the majority of the information to the cardiac system that flows in the subnet brain-brain and, at the same time, transmits to the other brain subsystems the information that arrives from the cardiac system. Jurysta et al, carried out a study applying linear measures of connectivity to predict changes in δ activity (brain sub-band) [10]. Modifications in the heart activity was characterized by the RR intervals in normal patients, and reading of visual inspection revealed that the fluctuations of high frequency of the CS (HF reflect cardiac parasympathetic activity) are related with δ frequency band of the brain signals.…”

Section: Introductionmentioning

confidence: 99%

Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

Cárdenas

Orjuela-Cañón

Cerquera

et al. 2017

13th International Conference on Medical Information Processing and Analysis

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Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

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A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men

Cited by 147 publications

References 29 publications

Temporally Resolved Fluctuation Analysis of Sleep ECG

Temporally Resolved Fluctuation Analysis of Sleep ECG

Association between Phase Coupling of Respiratory Sinus Arrhythmia and Brain Activity during Sleep

Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

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