Dynamical Heart Beat Correlations during Running

TL;DR

This paper introduces a novel dynamical analysis method to study real-time changes in heart beat correlations during running, revealing scale-dependent structures influenced by exercise intensity and stride frequency.

Contribution

It develops dynamical detrended fluctuation analysis and partial autocorrelation functions to analyze non-stationary heart rate data during exercise, advancing real-world cardiovascular research.

Findings

RRIs show multiscale anticorrelations beyond certain heart rate thresholds

Correlation structures are influenced by stride frequency

Methodology applicable across disciplines

Abstract

Fluctuations of the human heart beat constitute a complex system that has been studied mostly under resting conditions using conventional time series analysis methods. During physical exercise, the variability of the fluctuations is reduced, and the time series of beat-to-beat RR intervals (RRIs) become highly non-stationary. Here we develop a dynamical approach to analyze the time evolution of RRI correlations in running across various training and racing events under real-world conditions. In particular, we introduce dynamical detrended fluctuation analysis and dynamical partial autocorrelation functions, which are able to detect real-time changes in the scaling and correlations of the RRIs as functions of the scale and the lag. We relate these changes to the exercise intensity quantified by the heart rate (HR). Beyond subject-specific HR thresholds the RRIs show multiscale anticorrelations with both universal and individual scale-dependent structure that is potentially affected by the stride frequency. These preliminary results are encouraging for future applications of the dynamical statistical analysis in exercise physiology and cardiology, and the presented methodology is also applicable across various disciplines.