ABSTRACT
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. Here, we apply a 'litmus test' for heartbeat chaos based on a novel noise titration assay which affords a robust, specific, time-resolved and quantitative measure of the relative chaos level. Noise titration of running short-segment Holter tachograms from healthy subjects revealed circadian-dependent (or sleep/wake-dependent) heartbeat chaos that was linked to the HF component (respiratory sinus arrhythmia). The relative 'HF chaos' levels were similar in young and elderly subjects despite proportional age-related decreases in HF and LF power. In contrast, the near-regular heartbeat in CHF patients was primarily nonchaotic except punctuated by undetected ectopic beats and other abnormal beats, causing transient chaos. Such profound circadian-, age- and CHF-dependent changes in the chaotic and spectral characteristics of HRV were accompanied by little changes in approximate entropy, a measure of signal irregularity. The salient chaotic signatures of HRV in these subject groups reveal distinct autonomic, cardiac, respiratory and circadian/sleep-wake mechanisms that distinguish health and aging from CHF.
Subject(s)
Aging/physiology , Heart Failure/physiopathology , Heart Rate/physiology , Adult , Aged , Circadian Rhythm/physiology , Female , Humans , Male , Middle AgedABSTRACT
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. In this work, we compare the robustness of some nonlinear indices and evaluate their applicability to HRV analysis in the presence of noise.
Subject(s)
Heart Rate/physiology , Nonlinear Dynamics , Pattern Recognition, Automated/methods , Signal Processing, Computer-Assisted , Algorithms , Electrocardiography , Entropy , Fractals , Humans , Models, CardiovascularABSTRACT
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. The results showed the ApEn values of FHRV were correlated with pH, Pco2, Po2, HCO3-, ABE, SO2 and neonatal Apgar scores (r = 0.51, -0.29, 0.49, 0.29, 0.45, 0.56, 0.28, respectively, P < 0.05). The ApEn values for acidotic fetuses (pH less than 7.20) were significantly different from those of normal fetuses (P < 0.01), however the time domain parameters of FHRV were unable to identify the difference. The results suggest that the ApEn values of FHRV are closely related to the fetal hypoxia and acidemia. Thus the ApEn analysis appears to be useful for improving the sensitivity in the diagnosis of fetal distress.
