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      The Prognostic Value of Non-Linear Analysis of Heart Rate Variability in Patients with Congestive Heart Failure—A Pilot Study of Multiscale Entropy

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      1 , 2 , 3 , 4 , 5 , 2 , 3 , 4 , *
      PLoS ONE
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          Abstract

          Aims

          The influences of nonstationarity and nonlinearity on heart rate time series can be mathematically qualified or quantified by multiscale entropy (MSE). The aim of this study is to investigate the prognostic value of parameters derived from MSE in the patients with systolic heart failure.

          Methods and Results

          Patients with systolic heart failure were enrolled in this study. One month after clinical condition being stable, 24-hour Holter electrocardiogram was recording. MSE as well as other standard parameters of heart rate variability (HRV) and detrended fluctuation analysis (DFA) were assessed. A total of 40 heart failure patients with a mea age of 56±16 years were enrolled and followed-up for 684±441 days. There were 25 patients receiving β-blockers treatment. During follow-up period, 6 patients died or received urgent heart transplantation. The short-term exponent of DFA and the slope of MSE between scale 1 to 5 were significantly different between patients with or without β-blockers (p = 0.014 and p = 0.028). Only the area under the MSE curve for scale 6 to 20 (Area 6–20) showed the strongest predictive power between survival (n = 34) and mortality (n = 6) groups among all the parameters. The value of Area 6–20 21.2 served as a significant predictor of mortality or heart transplant (p = 0.0014).

          Conclusion

          The area under the MSE curve for scale 6 to 20 is not relevant to β-blockers and could further warrant independent risk stratification for the prognosis of CHF patients.

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          Most cited references30

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          Multiscale entropy analysis of complex physiologic time series.

          There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
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            On the trend, detrending, and variability of nonlinear and nonstationary time series.

            Determining trend and implementing detrending operations are important steps in data analysis. Yet there is no precise definition of "trend" nor any logical algorithm for extracting it. As a result, various ad hoc extrinsic methods have been used to determine trend and to facilitate a detrending operation. In this article, a simple and logical definition of trend is given for any nonlinear and nonstationary time series as an intrinsically determined monotonic function within a certain temporal span (most often that of the data span), or a function in which there can be at most one extremum within that temporal span. Being intrinsic, the method to derive the trend has to be adaptive. This definition of trend also presumes the existence of a natural time scale. All these requirements suggest the Empirical Mode Decomposition (EMD) method as the logical choice of algorithm for extracting various trends from a data set. Once the trend is determined, the corresponding detrending operation can be implemented. With this definition of trend, the variability of the data on various time scales also can be derived naturally. Climate data are used to illustrate the determination of the intrinsic trend and natural variability.
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              Natural history of asymptomatic left ventricular systolic dysfunction in the community.

              Information is limited regarding the rates of progression to congestive heart failure (CHF) and death in individuals with asymptomatic left ventricular systolic dysfunction (ALVD). We sought to characterize the natural history of ALVD, by studying unselected individuals with this condition in the community. We studied 4257 participants (1860 men) from the Framingham Study who underwent routine echocardiography. The prevalence of ALVD (visually estimated ejection fraction [EF] 50%, n=4128) and ALVD (n=129) were 0.7 and 5.8 per 100 person-years, respectively. After adjustment for cardiovascular disease risk factors, ALVD was associated with a hazards ratio (HR) for CHF of 4.7 (95% CI 2.7 to 8.1), compared with individuals without ALVD. An elevated risk of CHF after ALVD was observed even in individuals without prior myocardial infarction or valvular disease, with an adjusted HR of 6.5 (CI 3.1 to 13.5). Mild ALVD (EF 40% to 50%, n=78) and moderate-to-severe ALVD (EF <40%, n=51) were associated with adjusted HRs for CHF of 3.3 (CI 1.7 to 6.6) and 7.8 (CI 3.9 to 15.6), respectively. ALVD was also associated with an increased mortality risk (adjusted HR 1.6, CI 1.1 to 2.4). The median survival of ALVD subjects was 7.1 years. Individuals with ALVD in the community are at high risk of CHF and death, even when only mild impairment of EF is present. Additional studies are needed to define optimal therapy for mild ALVD.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                13 April 2011
                : 6
                : 4
                : e18699
                Affiliations
                [1 ]Graduate Institute of Clinical Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
                [2 ]Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
                [3 ]Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli, Taiwan
                [4 ]Research Center for Adaptive Data Analysis, National Central University, Taoyuan, Taiwan
                [5 ]Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan
                University of Maribor, Slovenia
                Author notes

                Conceived and designed the experiments: YLH MTL. Performed the experiments: YHL CL. Analyzed the data: MTL CL. Contributed reagents/materials/analysis tools: YLH MTL. Wrote the paper: YLH CL.

                Article
                PONE-D-11-00918
                10.1371/journal.pone.0018699
                3076441
                21533258
                e6a83c1e-1d03-453a-9fee-16b52b74f9a7
                Ho et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 31 December 2010
                : 8 March 2011
                Page count
                Pages: 6
                Categories
                Research Article
                Biology
                Developmental Biology
                Morphogenesis
                Heart Development
                Mathematics
                Nonlinear Dynamics
                Medicine
                Cardiovascular
                Heart Failure

                Uncategorized
                Uncategorized

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