Cross-Wavelet Time-Frequency Analysis Reveals Sympathetic Contribution to Baroreflex Sensitivity as Cause of Variable Phase Delay Between Blood Pressure and Heart Rate

Abstract

Introduction Baroreflex sensitivity (BRS) is often presented as a single number, but it is actually a frequency-dependent phenomenon whose value changes constantly due to internal and external stimuli. The standing posture, for instance, necessitates a changeover from vagal to sympathetic predominance for cardiovascular control. We present a wavelet cross-spectral analysis of blood pressure (BP) and interbeat interval (IBI) recordings in the search for variations in gain and phase between these signals. Additionally, we show how the lag in sympathetic response dictates BP-to-IBI phase relations. Methods Recordings in supine and head-up tilted (HUT) position, obtained earlier in 10 healthy subjects (4f/6m, aged 27–47 years) were used. BP and IBI were measured from the continuous finger pressure (by Finometer). The cross-wavelet analysis produced time- and frequency dependent gain (wBRS, wavelet derived BRS) and phase, using the MATLAB ® wavelet toolbox. We also applied the wBRS method to model-generated BP- and IBI-data with known interrelations to test the results of this analysis technique. Finally, wBRS values were compared with the xBRS-approach, which is a time domain method for continuous BRS estimation in a sliding 10-s window. Results In resting supine conditions, wBRS fluctuates; more at respiratory frequencies than in the 0.1 Hz band. After HUT, wBRS at the respiratory frequency decreases from average 22.7 to 8.5 ms/mmHg, phase between BP and IBI increases from −30° to −54°; in the sympathetic 0.1 Hz range these numbers are 13.3→6.3 ms/mmHg and −54°→−59°. The values found by xBRS are intermediate between wBRS-resp and wBRS-0.1 Hz. The Appendix shows that for the simulated data the BRS and phase values as found by the wavelet technique can be explained from vector additions of vagal and sympathetic BRS contributions. Discussion During supine rest parasympathetic control of heart rate dominates BRS; after HUT this is diminished and less effective. Due to the reaction times of the autonomic effectors, the phase relations between the signals depend on the relative contribution of the sympathetics, which explains the larger phase shift. Conclusion Cross wavelet analysis allows to follow fast BRS changes in time and frequency, while the computed phase relations help understand sympathetic participation.

A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter

“…Although the new modGauss method proposed in this paper has various advantages, limitations may include the double reflection that is required at the start and end points of the filter to perform signal filtering. In practical use, a short waiting time is For BRS estimation, the phase spectrum is sometimes adopted in spectral approaches to depict the timing difference from the sympathetic and vagal nerves in different conditions [29]. In addition, some spectral approaches utilize the threshold of coherence (e.g., greater than 0.5) between SBP and IBI spectral estimates to determine the bands used for the computation of BRS [45].…”

“…Several approaches have been suggested to overcome the above constraints. These approaches mainly focus on computing algorithms and can be categorized as spontaneous sequence methods (in the time domain) [26], spectral methods (in the frequency domain) [27,28] and cross-wavelet methods (in the time-frequency domain) [29]. Of these, the cross-wavelet approach is comparatively novel and can track BRS changes in the time as well as the frequency domains.…”

Long-Term Effects of Ischemic Stroke on Autonomic Regulation

“…XWT (between SBP and RR) and CWT of SBP (using analytical Morlet wavelet) were performed. The baroreflex sensitivity [41], [42] as a function of time was then calculated as BRS = XWT SBP,RR /CWT SBP (11) From this function, the baroreflex sensitivity in the LF region (BRSLF) was obtained as a mean BRS distributed in the lowfrequency range (0.04-0.15Hz) where SBP↔RR coherence was greater than 0.5 to assure gain of the system was reported only when the SBP↔RR interaction was robust (i.e. SBP↔RR coherence>0.5) [43].…”

Beyond the Baroreflex: A New Measure of Autonomic Regulation Based on the Time-Frequency Assessment of Variability, Phase Coherence and Couplings

“…Spectral analysis methods have also been used to analyse changes in the amplitude and phase of the cardiovascular oscillations (Elstad et al, 2011). However, the wealth of information that can be extracted in the time-frequency domain has been left mostly untouched apart from a handful of studies (Stankovski et al, 2013;de Boer and Karemaker, 2019).…”