Association between Mean Heart Rate and Recurrence Quantification Analysis of Heart Rate Variability in End-Stage Renal Disease

Abstract

Linear heart rate variability (HRV) indices are dependent on the mean heart rate, which has been demonstrated in different models (from sinoatrial cells to humans). The association between nonlinear HRV indices, including those provided by recurrence plot quantitative analysis (RQA), and the mean heart rate (or the mean cardiac period, also called meanNN) has been scarcely studied. For this purpose, we analyzed RQA indices of five minute-long HRV time series obtained in the supine position and during active standing from 30 healthy subjects and 29 end-stage renal disease (ESRD) patients (before and after hemodialysis). In the supine position, ESRD patients showed shorter meanNN (i.e., faster heart rate) and decreased variability compared to healthy subjects. The healthy subjects responded to active standing by shortening the meanNN and decreasing HRV indices to reach similar values of ESRD patients. Bivariate correlations between all RQA indices and meanNN were significant in healthy subjects and ESRD after hemodialysis and for most RQA indices in ESRD patients before hemodialysis. Multiple linear regression analyses showed that RQA indices were also dependent on the position and the ESRD condition. Then, future studies should consider the association among RQA indices, meanNN, and these other factors for a correct interpretation of HRV.

Recurrence Quantitative Analysis of Wavelet-Based Surrogate Data for Nonlinearity Testing in Heart Rate Variability

“…ESRD patients treated with hemodialysis (HD) are in fact subjected to a significant physiological stress during each HD session (Kooman et al, 2018), which involves a sympathetic "challenge" on a regular basis (Lerma et al, 2015) and thus becomes a robust model for the study of autonomic impairments. According to changes indicated by RQA indices during an active standing test, the cardiovascular dynamics associated with both ESRD and HD are consistent with the loss of access to some dynamic physiological conditions (Gonzalez et al, 2013;Calderon-Juarez et al, 2020). Furthermore, recent reports of the correlation between the mean duration of the cardiac cycle (meanNN) with RQA indices in HRV time series (Calderon-Juarez et al, 2020;Robles-Cabrera et al, 2021), suggest that the meanNN parameter, which reflects changes in the cardiac activity required to address different hemodynamic challenges, may influence the nonlinear dynamics of HRV as well.…”

“…According to changes indicated by RQA indices during an active standing test, the cardiovascular dynamics associated with both ESRD and HD are consistent with the loss of access to some dynamic physiological conditions (Gonzalez et al, 2013;Calderon-Juarez et al, 2020). Furthermore, recent reports of the correlation between the mean duration of the cardiac cycle (meanNN) with RQA indices in HRV time series (Calderon-Juarez et al, 2020;Robles-Cabrera et al, 2021), suggest that the meanNN parameter, which reflects changes in the cardiac activity required to address different hemodynamic challenges, may influence the nonlinear dynamics of HRV as well. Yet, it has not been fully demonstrated whether the RQA indices in short-term HRV time series exhibit nonlinear dynamics by using surrogate data approach, in particular, considering the nonstationary behavior of these time series.…”

Dependence of Heart Rate Variability Indices on the Mean Heart Rate in Women with Well-Controlled Type 2 Diabetes

“…This approach entails a risk of bias for overestimation in the correlation analyses, since combining the samples increases the number of non-independent data points. Nevertheless, the combination of data from the resting position and autonomic challenge maneuvers aims to move the set point (meanNN) during the maneuver, which increases the dynamic range of the HRV analysis, as reported in other publications [ 63 , 65 , 66 ].…”

Introducing BisQ, A Bicoherence-Based Nonlinear Index to Explore the Heart Rhythm

“…The same is valid for SD1, that is highly correlated with the (linear) spectral measure HF (R = 0.93), as well as RMSDD (R = 0.99 [8]). Similarly, a very strong association was found between heart rate and a group of RQA-derived indices [9].…”

“…BisQ is obtained as the sum of the Bicoherence values estimated at the three frequency pairs yielding the highest correlation with L mean. BisQ = BC(f1a, f2a) + BC(f1b, f2b) + BC(f1c, f2c), (9) where indices a, b, and c denote the three frequency pairs exhibiting the highest correlation between bicoherence and L mean . Figure 1 illustrates the flow chart followed to obtain BisQ.…”