Abstract
Multiscale entropy (MSE) profiles of heart rate variability (HRV) in patients with atrial fibrillation (AFib) provides clinically useful information for ischemic stroke risk assessment, suggesting that the complex properties characterized by MSE profiles are associated with ischemic stroke risk. However, the meaning of HRV complexity in patients with AFib has not been clearly interpreted, and the physical and mathematical understanding of the relation between HRV dynamics and the ischemic stroke risk is not well established. To gain a deeper insight into HRV dynamics in patients with AFib, and to improve ischemic stroke risk assessment using HRV analysis, we study the HRV characteristics related to MSE profiles, such as the long-range correlation and probability density function. In this study, we analyze the HRV time series of 173 patients with permanent AFib. Our results show that, although HRV time series in patients with AFib exhibit long-range correlation (1/f fluctuations)—as observed in healthy subjects—in a range longer than 90 s, these autocorrelation properties have no significant predictive power for ischemic stroke occurrence. Further, the probability density function structure of the coarse-grained times series at scales greater than 2 s is dominantly associated with ischemic stroke risk. This observation could provide valuable information for improving ischemic stroke risk assessment using HRV analysis.
Highlights
Biosignal time series often exhibit multiscale complexity, as characterized by 1/f power spectra [1,2]
Applying this analysis method to human heart rate variability (HRV), they demonstrated that Multiscale entropy (MSE) profiles identified pathological states (congestive heart failure and atrial fibrillation (AFib)); significant decreases in MSE occurred as the scale increased, indicating a lower degree of complexity compared with healthy controls [2]
To gain a better understanding of HRV dynamics in patients with AFib, we study HRV properties related to MSE profiles
Summary
Biosignal time series often exhibit multiscale complexity, as characterized by 1/f power spectra ( called 1/f fluctuation or long-range correlation) [1,2]. Detailed characterizations of biosignal complexity have potentially important applications in evaluating bedside diagnostics To measure this complexity, Costa et al developed a multiscale entropy (MSE) analysis, in which sample entropy proposed by Richman and Moorman [5] is estimated at multiple coarse-grained scales [2,6]. AFib exhibit 1/f long-range correlation in the range greater than 90 s, autocorrelation properties have no predictive power for ischemic stroke incidence and the probability density function structure of the coarse-grained (locally averaged) times series at scales of 2 s or longer dominantly contributes to risk assessments of ischemic stroke occurrence
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