Cross‐sample entropy statistic as a measure of complexity and regularity of renal sympathetic nerve activity in the rat

Abstract

In this study, we employed both power spectral analysis and cross-sample entropy measurement to assess the relationship between two time series, arterial blood pressure (ABP) and renal sympathetic nerve activity (RSNA), during a mild haemorrhage in anaesthetized Wistar rats. Removal of 1 ml of venous blood decreased BP (by 7.1 ± 0.7 mmHg) and increased RSNA (by 25.9 ± 2.4%). During these changes, the power in the RSNA signal at heart rate frequency was reduced but coherence between the spectra at heart rate frequency in RSNA and ABP remained unchanged. Cross-sample entropy was significantly increased (by 10%) by haemorrhage, revealing that there was greater asynchrony between ABP and the RSNA time series. Intrathecal administration of the glutamate receptor antagonist kynurenic acid (2 mM) almost halved (P < 0.01) the reflex increase in RSNA. Also during kynurenic acid block, haemorrhage failed to change total power, power at heart rate frequency, coherence at heart rate frequency, or the cross-sample entropy measurements. We conclude that the increase in asynchrony between ABP and RSNA during the reflex increase in RSNA was a consequence of an increase in synaptic input to the spinal renal neurones. The data show that the cross-sample entropy calculations can characterize the non-linearities of neural mechanisms underlying cardiovascular control and have a potential to reveal how some aspects of homeostatic regulation of kidney function is achieved by the autonomic nervous system.

Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

“…On the other hand, the results suggest that compared with linear CC, CF, and nonlinear MI, the three entropy-based measurements-XCE, XSampEn, and XFuzzyEn-perform better in measuring the electromechanical coupling. This is in accordance with many previous studies which have shown that entropy-based methods generally are better capable for measuring nonlinear features of cardiovascular system [10,26,32,53]. Furthermore, XFuzzyEn performed best which supports our previous study that the refined fuzzy membership function is capable to improve the stability and distinguishability of traditional entropy measures [10].…”

Cross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam

“…Pattern synchronization has been proposed in the literature as means to characterize the probability of finding similar dynamics between biomedical signals. For example, cross-entropy measures have been applied to heart rate variability (HRV; Porta et al 2007a, b) and also to neural signals (Papadelis et al 2007;Zhang et al 2007;Xie et al 2010).…”

Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography

“…34 Entropy-based measures have been widely used for the analysis of physiological time series to explore the complexity between two time-series. 35,36 C-SampEn measures the relative regularity of two signals, with lower C-SampEn values denoting greater conditional regularity or synchronicity, whereas higher values implicate that the given signals are less predictable (or more complex). While C-SampEn may be intuitively understood as the opposite of temporal correlation, it does not assume that signals are temporally stationary processes, but provides an alternative and complementary measure to assess the nonlinear statistics of PRV.…”