Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures
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 interper...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-08-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fphys.2013.00211/full |
| _version_ | 1811338956601556992 |
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| author | Sebastian eWallot RIccardo eFusaroli RIccardo eFusaroli RIccardo eFusaroli Kristian eTylen Kristian eTylen Kristian eTylen Else-Marie eJegindø Else-Marie eJegindø |
| author_facet | Sebastian eWallot RIccardo eFusaroli RIccardo eFusaroli RIccardo eFusaroli Kristian eTylen Kristian eTylen Kristian eTylen Else-Marie eJegindø Else-Marie eJegindø |
| author_sort | Sebastian eWallot |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:18:10Z |
| format | Article |
| id | doaj.art-92bb116cf26d41ec9186f8a5c51eb322 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-13T18:18:10Z |
| publishDate | 2013-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-92bb116cf26d41ec9186f8a5c51eb3222022-12-22T02:35:35ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2013-08-01410.3389/fphys.2013.0021154640Using Complexity Metrics With R-R Intervals and BPM Heart Rate MeasuresSebastian eWallot0RIccardo eFusaroli1RIccardo eFusaroli2RIccardo eFusaroli3Kristian eTylen4Kristian eTylen5Kristian eTylen6Else-Marie eJegindø7Else-Marie eJegindø8Aarhus UniversityAarhus UniversityAarhus UniversityAarhus University HospitalAarhus UniversityAarhus UniversityAarhus University HospitalAarhus UniversityAarhus University HospitalLately, 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.http://journal.frontiersin.org/Journal/10.3389/fphys.2013.00211/fullExerciseFractalsdetrended fluctuation analysisrecurrence quantification analysisHeart-beat complexityBPM |
| spellingShingle | Sebastian eWallot RIccardo eFusaroli RIccardo eFusaroli RIccardo eFusaroli Kristian eTylen Kristian eTylen Kristian eTylen Else-Marie eJegindø Else-Marie eJegindø Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures Frontiers in Physiology Exercise Fractals detrended fluctuation analysis recurrence quantification analysis Heart-beat complexity BPM |
| title | Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures |
| title_full | Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures |
| title_fullStr | Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures |
| title_full_unstemmed | Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures |
| title_short | Using Complexity Metrics With R-R Intervals and BPM Heart Rate Measures |
| title_sort | using complexity metrics with r r intervals and bpm heart rate measures |
| topic | Exercise Fractals detrended fluctuation analysis recurrence quantification analysis Heart-beat complexity BPM |
| url | http://journal.frontiersin.org/Journal/10.3389/fphys.2013.00211/full |
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