Cross‐Spectral Analysis of Heart Rate and Blood Pressure Modulations

Abstract

The cross-spectral analysis of heart rate (HR) and blood pressure (BP) variabilities provides "amplitude" and "phase" related measures. Compared to the amplitude measure, that is the baroreflex gain, the phase related measure characterizing the time lag between HR and BP oscillations has been studied to a much lesser extent. A population of 103 patients (73 men, 30 women, aged 53 +/- 12, range 20-82 years) referred for the management of coronary artery disease and/or hypertension were studied. In each subject, electrocardiogram and BP recordings were obtained in the supine and sitting positions of 5 minutes of rest (spontaneous respiration), 3 minutes of controlled respiration at 0.1 Hz (slow-controlled respiration), and 3 minutes of controlled respiration at 0.33 Hz (fast-controlled respiration). The frequency of maximum coherence (above the arbitrary threshold of 0.5) of BP and RR interval variabilities was searched between 0.033-0.133 Hz and 0.200-0.400 Hz to obtain baroreflex gain and phase shift in low and high frequency bands, respectively. Mean phase shifts of -79.1 and -67.0 degrees (-2.4 and -2.1 s) were found during slow-controlled respiration in the supine and sitting body positions, respectively. The mean phase shift between systolic BP and RR interval in the low frequency band was found between 83 and -109 degrees for body positions and respiration regimes. The actual baroreflex related time lag between systolic BP and RR variations was found between 3.5 and 5.1 seconds. The study concludes that the appropriate, and not always easy, selection of the frequency of maximum coherence between BP and HR oscillation is crucial for an accurate cross-spectral assessment of baroreflex sensitivity.

Exploring directionality in spontaneous heart period and systolic pressure variability interactions in humans: implications in the evaluation of baroreflex gain

“…Hence, the differences between traditional and causal phase estimates should be determined mainly by the relative weight of the FF contribution to cardiovascular regulation. Particularly, at LF in the supine position, the predominance of nonbaroreflex interactions led the causal approach to estimate less negative phase lags than those derived by the noncausal method, which in turn are similar to the phase shifts reported in the literature (5,9,12,39,41). The less negative phase lags observed in the LF band for the causal method suggest that at LF the SAP variations are more rapidly transferred to the RR interval, probably reflecting the activity of a fast vagal modulation.…”

“…Particularly, the observation that oscillations in the heart period (RR interval) and the systolic arterial pressure (SAP) are correlated around 0.1 Hz and at the frequency of respiration has prompted many researchers to focus on the interrelationship between these two signals. In this context, the cross-spectral analysis of RR interval and SAP variability series constitutes one of the most widespread tools used to investigate on the coupling mechanisms underlying short-term cardiovascular regulation (5,8,9,11,34,35,39,41,42). Although limited by the assumption of linear interactions between the two series, it allows the frequency-domain evaluation of the coupling degree, through the coherence function, and of the gain and phase relations, through the magnitude and the argument of the transfer function over a preselected input-output direction.…”

Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals

“…Nevertheless, in specific conditions, such as small variations in the signals and analysis of the fluctuations in selected bands, the linearity assumption could be considered at least approximately valid. Several previous studies based on linear transfer function analysis have indeed adopted simple but useful models for the description of the interactions among cardiovascular and/or cardiorespiratory variables (de Boer et al 1985;Robbe et al 1987;Saul et al 1991;Taylor and Eckberg 1996;Baselli et al 1997;Wichterle et al 2000;. In this context, the growing body of literature about the expansion of linear models and the development of new non-linear algorithms opens new perspectives of comparison between these two different approaches.…”

“…In cardiovascular variability analysis, this tool is widely applied to describe the relationship between different cardiac variables in different frequency bands, thus focusing on various underlying physiological mechanisms. Particularly, the analysis of the transfer function was used to disclose the complex mechanical and autonomic effects of respiration on heart rate (Saul et al 1989) and on arterial pressure (Saul et al 1991), to investigate on the phase relationships between arterial pressure and heart period at the frequency of the Mayer waves (about 0.1 Hz) and at the respiratory frequency (de Boer et al 1985;Taylor and Eckberg 1996;Wichterle et al 2000), and to estimate baroreflex sensitivity from the spontaneous variability of systolic pressure and heart rate (Robbe et al 1987;Pitzalis et al 1998;Pinna et al 2002).…”

Prevalent Low-Frequency Oscillation of Heart Rate

“…Different descriptive methods have been used, including spectral power in the midfrequency band, 12 central frequency derived from autoregressive models, 13,14 median frequency from Fourier analysis in the LF band, 15 and maximum coherence between the RR interval and blood pressure oscillations detecting the dominant oscillatory component in the LF band. 16 Some of these indexes have been shown to be superior to the traditional descriptors of HRV in the discrimination of healthy control subjects and patients, eg, diabetics, 17 elderly, 12,14 borderline hypertensives, 18 and patients with coronary artery disease 16 . All these studies supported the fact that RR interval or blood pressure oscillations in the LF band are shifted toward lower frequencies in patients compared with healthy control subjects and that this shift progressively increases with severity of organic heart impairment.…”