  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.
