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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

Multiscale entropy  analysis of cardiac interbeat interval time series from 26 healthy young, 46 healthy older subjects and 43 patients with congestive heart failure (CHF). The time series were derived from 24 h Holter monitoring recordings. Parameters for calculating sample entropy: m = 2, r = .5% of the original time series’ standard deviations. MSEμ analysis of the same time series were presented in [3]. The symbols and the error bars represent mean and standard deviation, respectively. The time series are available at www.physionet.org/physiobank/database/, under nsrdb, nsr2db, chfdb and chf2db.
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Figure 2: Multiscale entropy analysis of cardiac interbeat interval time series from 26 healthy young, 46 healthy older subjects and 43 patients with congestive heart failure (CHF). The time series were derived from 24 h Holter monitoring recordings. Parameters for calculating sample entropy: m = 2, r = .5% of the original time series’ standard deviations. MSEμ analysis of the same time series were presented in [3]. The symbols and the error bars represent mean and standard deviation, respectively. The time series are available at www.physionet.org/physiobank/database/, under nsrdb, nsr2db, chfdb and chf2db.

Mentions: Figure 2 shows the results of the analysis of RR interval time series from three groups, comprising health young and older subjects, and patients with chronic heart failure syndrome. The mean and standard deviation values of the complexity indices for the young, older and heart failure groups were 75.2 ± 24.3, 39.0 ± 15.7 and 20.9 ± 14.1, respectively. The complexity indices of healthy young subjects were significantly higher than those of healthy older subjects (p < 0.0001, two-tail Mann-Whitney test) and of patients with heart failure (p < 0.0001). In addition, the complexity indices of the healthy older subjects were significantly higher than those of the heart failure patients (p < 0.0001). These intergroup differences were confirmed using a fixed r value (0.0002 s2) for the computation of sample entropy as described in [13], and using quadratic entropy [14] in place of sample entropy. Both of these methods mitigate the impact of outlier values on entropy estimates. In addition, comparable intergroup differences to those presented here were obtained using an r value that is a percentage of one of the first variance coarse-grained time series.


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

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

Multiscale entropy  analysis of cardiac interbeat interval time series from 26 healthy young, 46 healthy older subjects and 43 patients with congestive heart failure (CHF). The time series were derived from 24 h Holter monitoring recordings. Parameters for calculating sample entropy: m = 2, r = .5% of the original time series’ standard deviations. MSEμ analysis of the same time series were presented in [3]. The symbols and the error bars represent mean and standard deviation, respectively. The time series are available at www.physionet.org/physiobank/database/, under nsrdb, nsr2db, chfdb and chf2db.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Multiscale entropy analysis of cardiac interbeat interval time series from 26 healthy young, 46 healthy older subjects and 43 patients with congestive heart failure (CHF). The time series were derived from 24 h Holter monitoring recordings. Parameters for calculating sample entropy: m = 2, r = .5% of the original time series’ standard deviations. MSEμ analysis of the same time series were presented in [3]. The symbols and the error bars represent mean and standard deviation, respectively. The time series are available at www.physionet.org/physiobank/database/, under nsrdb, nsr2db, chfdb and chf2db.
Mentions: Figure 2 shows the results of the analysis of RR interval time series from three groups, comprising health young and older subjects, and patients with chronic heart failure syndrome. The mean and standard deviation values of the complexity indices for the young, older and heart failure groups were 75.2 ± 24.3, 39.0 ± 15.7 and 20.9 ± 14.1, respectively. The complexity indices of healthy young subjects were significantly higher than those of healthy older subjects (p < 0.0001, two-tail Mann-Whitney test) and of patients with heart failure (p < 0.0001). In addition, the complexity indices of the healthy older subjects were significantly higher than those of the heart failure patients (p < 0.0001). These intergroup differences were confirmed using a fixed r value (0.0002 s2) for the computation of sample entropy as described in [13], and using quadratic entropy [14] in place of sample entropy. Both of these methods mitigate the impact of outlier values on entropy estimates. In addition, comparable intergroup differences to those presented here were obtained using an r value that is a percentage of one of the first variance coarse-grained time series.

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