Normalized correlation dimension for heart rate variability analysis

Effects of breathing patterns and light exercise on linear and nonlinear heart rate variability

Despite their use in cardiac risk stratification, the physiological meaning of nonlinear heart rate variability (HRV) measures is not well understood. The aim of this study was to elucidate effects of breathing frequency, tidal volume, and light exercise on nonlinear HRV and to determine associations with traditional HRV indices. R–R intervals, blood pressure, minute ventilation, breathing frequency, and respiratory gas concentrations were measured in 24 healthy male volunteers during 7 conditions: voluntary breathing at rest, and metronome guided breathing (0.1, 0.2 and 0.4 Hz) during rest, and cycling, respectively. The effect of physical load was significant for heart rate (HR; p < 0.001) and traditional HRV indices SDNN, RMSSD, lnLFP, and lnHFP (p < 0.01 for all). It approached significance for sample entropy (SampEn) and correlation dimension (D2) (p < 0.1 for both), while HRV detrended fluctuation analysis (DFA) measures DFAα1 and DFAα2 were not affected by load condition. Breathing did not affect HR but affected all traditional HRV measures. D2 was not affected by breathing; DFAα1 was moderately affected by breathing; and DFAα2, approximate entropy (ApEn), and SampEn were strongly affected by breathing. DFAα1 was strongly increased, whereas DFAα2, ApEn, and SampEn were decreased by slow breathing. No interaction effect of load and breathing pattern was evident. Correlations to traditional HRV indices were modest (r from –0.14 to –0.67, p < 0.05 to <0.01). In conclusion, while light exercise does not significantly affect short-time HRV nonlinear indices, respiratory activity has to be considered as a potential contributor at rest and during light dynamic exercise.

Heart rate variability and blood pressure during dynamic and static exercise at similar heart rate levels

Aim was to elucidate autonomic responses to dynamic and static (isometric) exercise of the lower limbs eliciting the same moderate heart rate (HR) response. Method: 23 males performed two kinds of voluntary exercise in a supine position at similar heart rates: static exercise (SE) of the lower limbs (static leg press) and dynamic exercise (DE) of the lower limbs (cycling). Subjective effort, systolic (SBP) and diastolic blood pressure (DBP), mean arterial pressure (MAP), rate pressure product (RPP) and the time between consecutive heart beats (RR-intervals) were measured. Time-domain (SDNN, RMSSD), frequency-domain (power in the low and high frequency band (LFP, HFP)) and geometric measures (SD1, SD2) as well as non-linear measures of regularity (approximate entropy (ApEn), sample entropy (SampEn) and correlation dimension D2) were calculated. Results: Although HR was similar during both exercise conditions (88±10 bpm), subjective effort, SBP, DBP, MAP and RPP were significantly enhanced during SE. HRV indicators representing overall variability (SDNN, SD 2) and vagal modulated variability (RMSSD, HFP, SD 1) were increased. LFP, thought to be modulated by both autonomic branches, tended to be higher during SE. ApEn and SampEn were decreased whereas D₂ was enhanced during SE. It can be concluded that autonomic control processes during SE and DE were qualitatively different despite similar heart rate levels. The differences were reflected by blood pressure and HRV indices. HRV-measures indicated a stronger vagal cardiac activity during SE, while blood pressure response indicated a stronger sympathetic efferent activity to the vessels. The elevated vagal cardiac activity during SE might be a response mechanism, compensating a possible co-activation of sympathetic cardiac efferents, as HR and LF/HF was similar and LFP tended to be higher. However, this conclusion must be drawn cautiously as there is no HRV-marker reflecting “pure” sympathetic cardiac activity.

Autonomic cardiac control in animal models of cardiovascular diseases. I. Methods of variability analysis

Analysis of heart rate variability (HRV) and blood pressure variability (BPV) and baroreceptor sensitivity (BRS) has become a proven tool in clinical cardiovascular diagnostics and risk stratification. In the present work, traditional and new methodological approaches for analysis of HRV, BPV, and BRS data are summarized. HRV, BPV, and BRS parameters were obtained from animal studies designed to study pathogenetic mechanisms of distinct cardiovascular diseases. Different non-linear approaches for HRV and BPV analysis are presented here, in particular measures of complexity based on symbolic dynamics. The dual sequence method (DSM) was employed for BRS analysis. In comparison to the classical measure of BRS using the average slope [ms/mm Hg], DSM offers additional information about the time-variant coupling between BPV and HRV. Since cardiovascular regulation shares common features among different species, data on HRV and BPV, as well as BRS, in animal models might be useful for understanding the pathogenetic mechanisms of cardiovascular diseases in humans and in the development of new diagnostic approaches.