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Generalized Multiscale Entropy Analysis: Application to Quantifying the Complex Volatility of Human Heartbeat Time Series.

Costa MD, Goldberger AL - Entropy (Basel) (2015)

Bottom Line: Furthermore, we find that the multiscale complexity of the volatility, not only the multiscale complexity of the mean heart rate, degrades with aging and pathology.The "bursty" behavior of the dynamics may be related to intermittency in energy and information flows, as part of multiscale cycles of activation and recovery.Generalized MSE may also be useful in quantifying the dynamical properties of other physiologic and of non-physiologic time series.

View Article: PubMed Central - HTML - PubMed

Affiliation: Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.

ABSTRACT

We introduce a generalization of multiscale entropy (MSE) analysis. The method is termed MSE n , where the subscript denotes the moment used to coarse-grain a time series. MSE μ , described previously, uses the mean value (first moment). Here, we focus on [Formula: see text], which uses the second moment, i.e., the variance. [Formula: see text] quantifies the dynamics of the volatility (variance) of a signal over multiple time scales. We use the method to analyze the structure of heartbeat time series. We find that the dynamics of the volatility of heartbeat time series obtained from healthy young subjects is highly complex. Furthermore, we find that the multiscale complexity of the volatility, not only the multiscale complexity of the mean heart rate, degrades with aging and pathology. The "bursty" behavior of the dynamics may be related to intermittency in energy and information flows, as part of multiscale cycles of activation and recovery. Generalized MSE may also be useful in quantifying the dynamical properties of other physiologic and of non-physiologic time series.

No MeSH data available.


Related in: MedlinePlus

Top: Cardiac interbeat interval (RR) time series from a healthy 20 year-old subject (left) and a 53 year-old patient with congestive heart failure (right). Middle and bottom: Variance of the RR interval time series calculated in a 20 (middle) and 40 (bottom) data point moving window. The horizontal axes are the same for all plots.
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Figure 1: Top: Cardiac interbeat interval (RR) time series from a healthy 20 year-old subject (left) and a 53 year-old patient with congestive heart failure (right). Middle and bottom: Variance of the RR interval time series calculated in a 20 (middle) and 40 (bottom) data point moving window. The horizontal axes are the same for all plots.

Mentions: Figure 1 shows the RR interval time series from a healthy subject and from a patient with congestive heart failure (top panel), and the corresponding variance derived coarse-grained time series for scales 20 and 40 (middle and lower panels). The latter panels show complex fluctuation patterns with higher amplitude in the case of the healthy subject. We note that the structure of the fluctuations appears to be preserved with re-scaling in both the healthy and pathologic cases.


Generalized Multiscale Entropy Analysis: Application to Quantifying the Complex Volatility of Human Heartbeat Time Series.

Costa MD, Goldberger AL - Entropy (Basel) (2015)

Top: Cardiac interbeat interval (RR) time series from a healthy 20 year-old subject (left) and a 53 year-old patient with congestive heart failure (right). Middle and bottom: Variance of the RR interval time series calculated in a 20 (middle) and 40 (bottom) data point moving window. The horizontal axes are the same for all plots.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834981&req=5

Figure 1: Top: Cardiac interbeat interval (RR) time series from a healthy 20 year-old subject (left) and a 53 year-old patient with congestive heart failure (right). Middle and bottom: Variance of the RR interval time series calculated in a 20 (middle) and 40 (bottom) data point moving window. The horizontal axes are the same for all plots.
Mentions: Figure 1 shows the RR interval time series from a healthy subject and from a patient with congestive heart failure (top panel), and the corresponding variance derived coarse-grained time series for scales 20 and 40 (middle and lower panels). The latter panels show complex fluctuation patterns with higher amplitude in the case of the healthy subject. We note that the structure of the fluctuations appears to be preserved with re-scaling in both the healthy and pathologic cases.

Bottom Line: Furthermore, we find that the multiscale complexity of the volatility, not only the multiscale complexity of the mean heart rate, degrades with aging and pathology.The "bursty" behavior of the dynamics may be related to intermittency in energy and information flows, as part of multiscale cycles of activation and recovery.Generalized MSE may also be useful in quantifying the dynamical properties of other physiologic and of non-physiologic time series.

View Article: PubMed Central - HTML - PubMed

Affiliation: Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.

ABSTRACT

We introduce a generalization of multiscale entropy (MSE) analysis. The method is termed MSE n , where the subscript denotes the moment used to coarse-grain a time series. MSE μ , described previously, uses the mean value (first moment). Here, we focus on [Formula: see text], which uses the second moment, i.e., the variance. [Formula: see text] quantifies the dynamics of the volatility (variance) of a signal over multiple time scales. We use the method to analyze the structure of heartbeat time series. We find that the dynamics of the volatility of heartbeat time series obtained from healthy young subjects is highly complex. Furthermore, we find that the multiscale complexity of the volatility, not only the multiscale complexity of the mean heart rate, degrades with aging and pathology. The "bursty" behavior of the dynamics may be related to intermittency in energy and information flows, as part of multiscale cycles of activation and recovery. Generalized MSE may also be useful in quantifying the dynamical properties of other physiologic and of non-physiologic time series.

No MeSH data available.


Related in: MedlinePlus