Approximate entropy and point correlation dimension of heart rate variability in healthy subjects.

R J Storella; H W Wood; K M Mills; J K Kanters; M V Højgaard; N H Holstein-Rathlou

Approximate entropy and point correlation dimension of heart rate variability in healthy subjects.

R J Storella, H W Wood, K M Mills, J K Kanters, M V Højgaard, N H Holstein-Rathlou

Biomedicinsk Institut

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

29 Citationer (Scopus)

Abstract

Udgivelsesdato: null-null

Originalsprog	Engelsk
Tidsskrift	Integrative Physiological & Behavioral Science
Vol/bind	33
Udgave nummer	4
Sider (fra-til)	315-20
Antal sider	5
ISSN	1053-881X
Status	Udgivet - 1999

Bibliografisk note

Keywords: Adult; Algorithms; Electrocardiography; Entropy; Heart Rate; Humans; Nonlinear Dynamics

Citationsformater

@article{cbb3c090ab6311ddb5e9000ea68e967b,

title = "Approximate entropy and point correlation dimension of heart rate variability in healthy subjects.",

abstract = "The contribution of nonlinear dynamics to heart rate variability in healthy humans was examined using surrogate data analysis. Several measures of heart rate variability were used and compared. Heart rates were recorded for three hours and original data sets of 8192 R-R intervals created. For each original data set (n = 34), three surrogate data sets were made by shuffling the order of the R-R intervals while retaining their linear correlations. The difference in heart rate variability between the original and surrogate data sets reflects the amount of nonlinear structure in the original data set. Heart rate variability was analyzed by two different nonlinear methods, point correlation dimension and approximate entropy. Nonlinearity, though under 10 percent, could be detected with both types of heart rate variability measures. More importantly, not only were the correlations between these measures and the standard deviation of the R-R intervals weak, the correlation among the nonlinear measures themselves was also weak (generally less than 0.6). This suggests that in addition to standard linear measures of heart rate variability, the use of multiple nonlinear measures of heart rate variability might be useful in monitoring heart rate dynamics.",

author = "Storella, {R J} and Wood, {H W} and Mills, {K M} and Kanters, {J K} and H{\o}jgaard, {M V} and Holstein-Rathlou, {N H}",

note = "Keywords: Adult; Algorithms; Electrocardiography; Entropy; Heart Rate; Humans; Nonlinear Dynamics",

year = "1999",

language = "English",

volume = "33",

pages = "315--20",

journal = "Pavlovian Journal of Biological Science",

issn = "1932-4502",

publisher = "springer verlag (germany)",

number = "4",

}

TY - JOUR

T1 - Approximate entropy and point correlation dimension of heart rate variability in healthy subjects.

AU - Storella, R J

AU - Wood, H W

AU - Mills, K M

AU - Kanters, J K

AU - Højgaard, M V

AU - Holstein-Rathlou, N H

N1 - Keywords: Adult; Algorithms; Electrocardiography; Entropy; Heart Rate; Humans; Nonlinear Dynamics

PY - 1999

Y1 - 1999

N2 - The contribution of nonlinear dynamics to heart rate variability in healthy humans was examined using surrogate data analysis. Several measures of heart rate variability were used and compared. Heart rates were recorded for three hours and original data sets of 8192 R-R intervals created. For each original data set (n = 34), three surrogate data sets were made by shuffling the order of the R-R intervals while retaining their linear correlations. The difference in heart rate variability between the original and surrogate data sets reflects the amount of nonlinear structure in the original data set. Heart rate variability was analyzed by two different nonlinear methods, point correlation dimension and approximate entropy. Nonlinearity, though under 10 percent, could be detected with both types of heart rate variability measures. More importantly, not only were the correlations between these measures and the standard deviation of the R-R intervals weak, the correlation among the nonlinear measures themselves was also weak (generally less than 0.6). This suggests that in addition to standard linear measures of heart rate variability, the use of multiple nonlinear measures of heart rate variability might be useful in monitoring heart rate dynamics.

AB - The contribution of nonlinear dynamics to heart rate variability in healthy humans was examined using surrogate data analysis. Several measures of heart rate variability were used and compared. Heart rates were recorded for three hours and original data sets of 8192 R-R intervals created. For each original data set (n = 34), three surrogate data sets were made by shuffling the order of the R-R intervals while retaining their linear correlations. The difference in heart rate variability between the original and surrogate data sets reflects the amount of nonlinear structure in the original data set. Heart rate variability was analyzed by two different nonlinear methods, point correlation dimension and approximate entropy. Nonlinearity, though under 10 percent, could be detected with both types of heart rate variability measures. More importantly, not only were the correlations between these measures and the standard deviation of the R-R intervals weak, the correlation among the nonlinear measures themselves was also weak (generally less than 0.6). This suggests that in addition to standard linear measures of heart rate variability, the use of multiple nonlinear measures of heart rate variability might be useful in monitoring heart rate dynamics.

M3 - Journal article

C2 - 10333974

VL - 33

SP - 315

EP - 320

JO - Pavlovian Journal of Biological Science

JF - Pavlovian Journal of Biological Science

SN - 1932-4502

IS - 4

ER -