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-min (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 non-linear analyses, the success of non-linear 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 non-linear aspects of heart beat dynamics.

Beyond Synchrony: Joint Action in a Complex Production Task Reveals Beneficial Effects of Decreased Interpersonal Synchrony

“…Alternatively, this could be due to methodological limitations, as heart rate was recorded as BPM averaged across overlapping 5-second intervals, which might have smoothed-out some of the relevant dynamics [43]. In this context it is also worth mentioning that other research investigating shared heart-rate dynamics in joint action have found that much of the subjective-emotional responses are more strongly anchored on behavioral measures of joint action, not heart-rate coordination [44].…”

Heart Rate Synchrony in Psychological Counseling: A Case Study

“…To convert the raw RRI time series data into beat-per-minute (BPM) data before the time series analysis, the RRI time series was averaged over non-overlapping intervals of 1 and 10 s for the nonlinear time series analysis (i.e., recurrence analysis) and for producing an observation of the overall tendency of the two sets of time series data, respectively. Because converting to BPM data means normalizing and smoothing the raw RRI data-the shorter the intervals for averaging RRI data, the weaker the smoothing (Wallot, Fusaroli, Tylén, & Jegindø, 2013)-we used BPM data averaged by 1 s intervals (relatively preserving the original variability of the heart rate activity) for the nonlinear time series analysis and BPM data averaged by 10 s intervals for the qualitative observation (it was relatively easy to visually identify the time-varying structure of the data).…”

Cross-recurrence quantification analysis of categorical and continuous time series: an R package

“…In behavioral sciences, such streams of information can either be as “concrete” as body sways or eye-movement trajectories, and even heart rate (Shockley et al, 2003; Richardson and Dale, 2005; Wallot et al, 2013), but they can also be more “abstract” sequences of linguistic information, such as the words exchanged by two interlocutors during a dialog (for a recent review see Fusaroli et al, in press). …”