Comparison of short-term heart rate variability indexes evaluated through electrocardiographic and continuous blood pressure monitoring

Abstract

Heart rate variability (HRV) analysis represents an important tool for the characterization of complex cardiovascular control. HRV indexes are usually calculated from electrocardiographic (ECG) recordings after measuring the time duration between consecutive R peaks, and this is considered the gold standard. An alternative method consists of assessing the Pulse Rate Variability (PRV) from signals acquired through photoplethysmography, a technique also employed for the continuous noninvasive monitoring of blood pressure. In this work, we carry out a thorough analysis and comparison of short term variability indexes computed from HRV time series obtained from the ECG and from PRV time series obtained from continuous blood pressure (CBP) signals, in order to evaluate the reliability of using CBP-based recordings in place of standard ECG tracks. The analysis has been carried out on short time series (300 beats) of HRV and PRV in 76 subjects studied in different conditions: resting in the supine position, postural stress during 45° head-up tilt, and mental stress during computation of arithmetic test. Nine different indexes have been taken into account, computed in the time domain (mean, variance, root mean square of the successive differences), frequency domain (low-to-high frequency power ratio LF/HF, HF spectral power and central frequency) and information domain (entropy, conditional entropy, self entropy). Thorough validation has been performed using comparison of the HRV and PRV distributions, robust linear regression, and Bland-Altman plots. Results demonstrate the feasibility of extracting HRV indexes from CBP-based data, showing an overall relatively good agreement of time, frequency, and information domain measures. The agreement decreased during postural and mental arithmetic stress, especially with regard to band-power ratio, conditional and self entropy. This finding suggests to use caution in adopting PRV as a surrogate of HRV during stress conditions.

Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular and Respiratory Variability Series

“…The complexity of HP assessed at lower time scales (cutoff 0.4 Hz) always decreases in the UP position, for all measures (univariate, both bivariate, and multivariate), as shown in Figure 4 . This documents the well-known simplification of heart rate variability induced by head-up tilt, which is known to evoke sympathetic activation and vagal withdrawal making the cardiac dynamics more regular [ 31 , 40 , 43 , 44 ]. The fact that this result is observed identically for the eVAR and eVARFI approaches indicates that long-range correlations do not impact significantly the evaluation of complexity performed at short time scales.…”

“…Several studies report the interaction between the dynamics of these three time series [ 24 , 25 , 26 , 27 , 28 ], which motivates their use in a multivariate context. The variation of heart period, usually referred as heart rate variability (HRV), reflecting cardiovascular complexity and representing the capability of the organism to react to environmental and psychological stimuli, is the most studied variable and main target variable in cardiovascular and cardiorespiratory spontaneous variability [ 29 , 30 , 31 ]. For this reason, the conditional measures of a single scalar process will focus on the HP time series, as it has been shown that SAP and RESP have an effect (direct or indirectly) on this process [ 24 , 25 , 26 , 27 , 28 ].…”

Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks

“…The directed links between different physiological systems observed in this study can reflect either well-defined physiological mechanisms, such as the respiratory and heart rate effects on the pulse arrival time [ 74 , 84 ], or statistical associations with likely common determinants of physiological origin, like the brain–heart interactions which are thought to be mediated by dynamic alterations of the sympatho-vagal balance [ 7 , 22 , 85 ]. In either case, approaches like ours that allow the probing of the dynamic interaction among different organ systems can be very useful to show how an imbalanced interaction may have a negative impact on health [ 85 ].…”

Information Decomposition in the Frequency Domain: a New Framework to Study Cardiovascular and Cardiorespiratory Oscillations

“…The potentiality of combining information and spectral decompositions was verified experimentally in the proposed application to cardiovascular and cardiorespiratory interactions. Indeed, although significant modifications of physiological dynamics induced by the transition from rest to tilt are detectable using simpler univariate variability markers (see, e.g., [12,37,41,42]), the proposed multivariate indexes can describe peculiar features that might be correlated with specific properties of the physiological dynamical systems that go beyond their traditional assessment based on power of oscillations, transfer function gain and latency but deal with higher level functions related to the overall organization of the cardiovascular control system like synergy and redundancy. Specific results observed in this work are the opposite response to tilt observed integrating the cardiovascular interaction information within the LF and HF frequency bands, with the increase in the LF band and the decrease in HF band associated respectively to sympathetic activation and parasympathetic withdrawal, and the different trends observed with tilt for the global and unique measures of coupling between HP ad SAP, with tendency to decrease for and to increase for that highlight the dampening of redundant interactions in the response to postural stress.…”