Human fetuses have nonlinear cardiac dynamics

Abstract

Approximate entropy (ApEn) is a statistic that quantifies regularity in time series data, and this parameter has several features that make it attractive for analyzing physiological systems. In this study, ApEn was used to detect nonlinearities in the heart rate (HR) patterns of 12 low-risk human fetuses between 38 and 40 wk of gestation. The fetal cardiac electrical signal was sampled at a rate of 1,024 Hz by using Ag-AgCl electrodes positioned across the mother's abdomen, and fetal R waves were extracted by using adaptive signal processing techniques. To test for nonlinearity, ApEn for the original HR time series was compared with ApEn for three dynamic models: temporally uncorrelated noise, linearly correlated noise, and linearly correlated noise with nonlinear distortion. Each model had the same mean and SD in HR as the original time series, and one model also preserved the Fourier power spectrum. We estimated that noise accounted for 17.2-44.5% of the total between-fetus variance in ApEn. Nevertheless, ApEn for the original time series data still differed significantly from ApEn for the three dynamic models for both group comparisons and individual fetuses. We concluded that the HR time series, in low-risk human fetuses, could not be modeled as temporally uncorrelated noise, linearly correlated noise, or static filtering of linearly correlated noise.

Temporal asymmetries of short-term heart period variability are linked to autonomic regulation

“…In addition, we found that heart period variability is irreversible in a significant percentage of healthy fetuses between the 25th and 40th wk of gestation and that the presence of irreversible patterns increases as pregnancy progresses. This result confirms the observation that heart period variability in healthy fetuses is nonlinear between the 38th and 40th week of gestation (8) and that nonlinear dynamics are more frequent as pregnancy progresses (12). However, none of the studies about humans or fetuses was able to identify the temporal scheme responsible for the nonlinear behavior.…”

“…THE VARIABILITY OF HEART PERIOD (usually approximated as the temporal distance between two consecutive R peaks on the ECG, R-R) has been proven to be nonlinear in healthy fetuses between 38th and 40th week of gestation (8) and in healthy humans (1,4), mostly during experimental conditions periodically forcing cardiovascular regulation (i.e., controlled breathing) (15,16). However, this finding has not been translated yet into a notion actually helpful in pathophysiology.…”

How random is your heart beat?

“…Most of these methods use fractal or multifractal scaling analysis [9]. The approach has also yielded stochastic models of heart rate variability [10,11].…”

Nonlinear properties of vagal and sympathetic modulations of heart rate variability in ovine fetus near term

“…Understanding fHRV dynamics during physiological (e.g., sleep states dependent changes) and pathophysiological (e.g., asphyxia) conditions has evolved over the past two decades, propelled by analyses of the linear aspects of fHRV in human and ovine fetuses (4,11,16,20,29,32). However, characterization of the linear fHRV properties is fundamentally limited in its power to describe the nonlinear structure of the underlying sympathovagal interactions (12,14).The origin of the nonlinearity of sympathovagal interactions lies in their intrinsic complexity. This complexity emerges from interaction of neuronal brain stem networks as weakly coupled nonlinear oscillators that are influenced by various afferent signals (3,17,28,30), suggesting that sympathetic and vagal influences are superimposed nonlinearly on fHRV and can act synergistically.…”

“…Understanding fHRV dynamics during physiological (e.g., sleep states dependent changes) and pathophysiological (e.g., asphyxia) conditions has evolved over the past two decades, propelled by analyses of the linear aspects of fHRV in human and ovine fetuses (4,11,16,20,29,32). However, characterization of the linear fHRV properties is fundamentally limited in its power to describe the nonlinear structure of the underlying sympathovagal interactions (12,14).…”