Abstract
The purpose of this study is to characterize and attenuate the influence of mean heart rate (HR) on nonlinear heart rate variability (HRV) indices (correlation dimension, sample, and approximate entropy) as a consequence of being the HR the intrinsic sampling rate of HRV signal. This influence can notably alter nonlinear HRV indices and lead to biased information regarding autonomic nervous system (ANS) modulation. First, a simulation study was carried out to characterize the dependence of nonlinear HRV indices on HR assuming similar ANS modulation. Second, two HR-correction approaches were proposed: one based on regression formulas and another one based on interpolating RR time series. Finally, standard and HR-corrected HRV indices were studied in a body position change database. The simulation study showed the HR-dependence of non-linear indices as a sampling rate effect, as well as the ability of the proposed HR-corrections to attenuate mean HR influence. Analysis in a body position changes database shows that correlation dimension was reduced around 21% in median values in standing with respect to supine position (p < 0.05), concomitant with a 28% increase in mean HR (p < 0.05). After HR-correction, correlation dimension decreased around 18% in standing with respect to supine position, being the decrease still significant. Sample and approximate entropy showed similar trends. HR-corrected nonlinear HRV indices could represent an improvement in their applicability as markers of ANS modulation when mean HR changes.
Highlights
Heart rate (HR) variability (HRV) has been studied as a non-invasive technique to assess autonomic nervous system (ANS) regulation of the heart
Changes in nonlinear heart rate variability (HRV) indices were studied under different sympathetic conditions where mean HR changed
A simulation study was carried out emulating ANS modulation no linked to mean HR
Summary
Heart rate (HR) variability (HRV) has been studied as a non-invasive technique to assess autonomic nervous system (ANS) regulation of the heart. HRV analysis has been extended including nonlinear indices based on chaos theory. These methodologies describe ANS in terms of regularity and complexity. Sampling Rate in Non-linear HRV indices have been studied in a wide range of cardiovascular diseases revealing discriminant power for risk stratification (Maestri et al, 2007). The integration of linear and nonlinear HRV indices has been shown relevant to stratify cardiac risk patients (Voss et al, 2009) and to describe pathophysiological mechanisms in the cardiovascular and neural system control (Signorini et al, 2011). Some studies pointed out that HRV complexity changes as a result of sympathetic activation (Porta et al, 2007; Turianikova et al, 2011; Weippert et al, 2013)
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