Aarhus University Seal / Aarhus Universitets segl

Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures. / Wallot, Sebastian; Fusaroli, Riccardo; Tylén, Kristian; Jegindø, Else-Marie Elmholdt.

In: Frontiers in Physiology, Vol. 4, No. 211, 13.08.2013.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

APA

CBE

MLA

Vancouver

Author

Bibtex

@article{e8f882c429424cab881a0fa052c263fa,
title = "Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures",
abstract = "Lately, growing attention in the health sciences has been paid to the dynamics of heart rate as indicator of impending failures and for prognoses. Likewise, in social and cognitive sciences, heart rate is increasingly employed as a measure of arousal, emotional engagement and as a marker of interpersonal coordination. However, there is no consensus about which measurements and analytical tools are most appropriate in mapping the temporal dynamics of heart rate and quite different metrics are reported in the literature. As complexity metrics of heart rate variability depend critically on variability of the data, different choices regarding the kind of measures can have a substantial impact on the results. In this article we compare linear and non-linear statistics on two prominent types of heart beat data, beat-to-beat intervals (R-R interval) and beats-per-minute (BPM). As a proof-of-concept, we employ a simple rest-exercise-rest task and show that non-linear statistics – fractal (DFA) and recurrence (RQA) analyses – reveal information about heart beat activity above and beyond the simple level of heart rate. Non-linear statistics unveil sustained post-exercise effects on heart rate dynamics, but their power to do so critically depends on the type data that is employed: While R-R intervals are very susceptible to nonlinear analyses, the success of nonlinear methods for BPM data critically depends on their construction. Generally, ‘oversampled’ BPM time-series can be recommended as they retain most of the information about nonlinear aspects of heart beat dynamics.",
author = "Sebastian Wallot and Riccardo Fusaroli and Kristian Tyl{\'e}n and Jegind{\o}, {Else-Marie Elmholdt}",
year = "2013",
month = "8",
day = "13",
doi = "10.3389/fphys.2013.00211",
language = "English",
volume = "4",
journal = "Frontiers in Physiology",
issn = "1664-042X",
publisher = "Frontiers Media S.A",
number = "211",

}

RIS

TY - JOUR

T1 - Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures

AU - Wallot, Sebastian

AU - Fusaroli, Riccardo

AU - Tylén, Kristian

AU - Jegindø, Else-Marie Elmholdt

PY - 2013/8/13

Y1 - 2013/8/13

N2 - Lately, growing attention in the health sciences has been paid to the dynamics of heart rate as indicator of impending failures and for prognoses. Likewise, in social and cognitive sciences, heart rate is increasingly employed as a measure of arousal, emotional engagement and as a marker of interpersonal coordination. However, there is no consensus about which measurements and analytical tools are most appropriate in mapping the temporal dynamics of heart rate and quite different metrics are reported in the literature. As complexity metrics of heart rate variability depend critically on variability of the data, different choices regarding the kind of measures can have a substantial impact on the results. In this article we compare linear and non-linear statistics on two prominent types of heart beat data, beat-to-beat intervals (R-R interval) and beats-per-minute (BPM). As a proof-of-concept, we employ a simple rest-exercise-rest task and show that non-linear statistics – fractal (DFA) and recurrence (RQA) analyses – reveal information about heart beat activity above and beyond the simple level of heart rate. Non-linear statistics unveil sustained post-exercise effects on heart rate dynamics, but their power to do so critically depends on the type data that is employed: While R-R intervals are very susceptible to nonlinear analyses, the success of nonlinear methods for BPM data critically depends on their construction. Generally, ‘oversampled’ BPM time-series can be recommended as they retain most of the information about nonlinear aspects of heart beat dynamics.

AB - Lately, growing attention in the health sciences has been paid to the dynamics of heart rate as indicator of impending failures and for prognoses. Likewise, in social and cognitive sciences, heart rate is increasingly employed as a measure of arousal, emotional engagement and as a marker of interpersonal coordination. However, there is no consensus about which measurements and analytical tools are most appropriate in mapping the temporal dynamics of heart rate and quite different metrics are reported in the literature. As complexity metrics of heart rate variability depend critically on variability of the data, different choices regarding the kind of measures can have a substantial impact on the results. In this article we compare linear and non-linear statistics on two prominent types of heart beat data, beat-to-beat intervals (R-R interval) and beats-per-minute (BPM). As a proof-of-concept, we employ a simple rest-exercise-rest task and show that non-linear statistics – fractal (DFA) and recurrence (RQA) analyses – reveal information about heart beat activity above and beyond the simple level of heart rate. Non-linear statistics unveil sustained post-exercise effects on heart rate dynamics, but their power to do so critically depends on the type data that is employed: While R-R intervals are very susceptible to nonlinear analyses, the success of nonlinear methods for BPM data critically depends on their construction. Generally, ‘oversampled’ BPM time-series can be recommended as they retain most of the information about nonlinear aspects of heart beat dynamics.

U2 - 10.3389/fphys.2013.00211

DO - 10.3389/fphys.2013.00211

M3 - Journal article

C2 - 23964244

VL - 4

JO - Frontiers in Physiology

JF - Frontiers in Physiology

SN - 1664-042X

IS - 211

ER -