Association Between Phase Coupling of Respiratory Sinus Arrhythmia and Slow Wave Brain Activity During Sleep - Niizeki, Kyuichi; Saitoh, Tadashi (Frontiers in Physiology, 2018)
Phase coupling of respiratory sinus arrhythmia (RSA) has been proposed to be an alternative measure for evaluating autonomic nervous system activity. The aim of this study was to analyze how phase coupling of RSA is altered during sleep, in order to explore whether this measure is a predictor of slow wave sleep (SWS). Overnight electroencephalograms (EEG), electrocardiograms (ECG), and breathing using inductance plethysmography were recorded from 30 healthy volunteers (six females, age range 21 – 64, 31.6 ± 14.7 years). Slow wave activity was evaluated by the envelope of the amplitude of the EEG delta-wave (0.5 – 4 Hz). The RSA was extracted from the change in the R-R interval (RRI) by band-pass filter, where pass band frequencies were determined from the profile of the power spectral density for respiration. The analytic signals of RSA and respiration were obtained by Hilbert transform, after which the amplitude of RSA (ARSA) and the degree of phase coupling (lambda) were quantified. Additionally, the normalized high-frequency component (HFn) of the frequency-domain heart rate variability (HRV) was calculated. Using auto- and cross-correlation analyses, we found that overnight profiles of lambda and delta-wave were correlated, with significant cross-correlation coefficients (0.461 ± 0.107). The delta-wave and HFn were also correlated (0.426 ± 0.115). These correlations were higher than that for the relationship between delta-wave and ARSA (0.212 ± 0.161). The variation of lambda precedes the onset of the delta-wave by approximately 3 min, suggesting a vagal enhancement prior to the onset of SWS. Auto correlation analysis revealed that the periodicity of lambda was quite similar to that of the delta-wave (88.3 ± 15.7 min vs. 88.6 ± 16.3 min, lambda-cycle = 0.938 × delta-cycle + 5.77 min, r = 0.902). These results suggest that phase coupling analysis of RSA appears to be a marker for predicting SWS intervals, thereby complementing other noninvasive tools and diagnostic efforts.