Chaotic signatures of heart rate variability and its power spectrum in health, aging and heart failure.

A paradox regarding the classic power spectral analysis of heart rate variability (HRV) is whether the characteristic high- (HF) and low-frequency (LF) spectral peaks represent stochastic or chaotic phenomena. Resolution of this fundamental issue is key to unraveling the mechanisms of HRV, which is critical to its proper use as a noninvasive marker for cardiac mortality risk assessment and stratification in congestive heart failure (CHF) and other cardiac dysfunctions. However, conventional techniques of nonlinear time series analysis generally lack sufficient sensitivity, specificity and robustness to discriminate chaos from random noise, much less quantify the chaos level.

Comparison of nonlinear indices in analyses of heart rate variability.

Recent advances in study of heart rate variability (HRV) call for a paradigm shift from linear to nonlinear analysis. However, whether HRV per se is chaotic or merely random has remained an open problem. Myriad idealized measures of nonlinear dynamics or fractals which have been proposed in the literatures to characterize HRV are limited by their sensitivity to discernable noise.

[Approximate entropy analysis of fetal heart rate variability and its application in the diagnosis of fetal distress].

To introduce approximate entropy (ApEn) in the analysis of fetal heart rate variability (FHRV) and to discuss the relationship between the ApEn values and perinatal outcomes, 67 computerized cardiotocographs were recorded. Approximate entropy and index in time domain were used to analyze FHRV. After childbirth, the neonatal Apgar scores were recorded and umbilical cord arterial blood gas analyses were performed.