An algorithm for quantifying cardiorespiratory synchronization to assess the functional state and intersystems interactions in athletes

Abstract

Introduction. The rhythm of the heart (heart rate, HR) is closely related to the rhythm of breathing. The phenomenon of respiratory sinus arrhythmia (an increase in heart rate during inhalation and a decrease during exhalation) is well known. Cardiorespiratory interactions and synchronization of these two signals are evaluated differently in the literature. The purpose of this work was to propose and test an approach to assess cardiorespiratory synchronization – the calculation of the cross-correlation coefficient between heart rate and respiration curve, for a more objective characterization of an athlete’s bodily states. Methods. A photoplethysmogram and respiration were recorded in 45 healthy athletes (18-25 years old) in three situations: rest, paced breathing at 6 times per minute (resonant frequency) and the performance of a sensorimotor task (tapping with the rhythmic metronome sounds and then reproducing a given rhythm from memory). For the HR and respiration curves, filtering and smoothing algorithms using the Savitsky-Goley method were applied sequentially, and then the cross-correlation coefficient between the two curves was calculated. The spectral parameters of heart rate variability (LF and HF) were also obtained, since during spontaneous breathing its contribution to the HR is reflected in HF band, and when breathing at a resonant frequency, a peak occurs at a frequency of 0.1 Hz in the LF band. Results. The cross-correlation coefficient, as well as the peak of HR spectral power at 0.1 Hz, increases significantly when breathing at a resonant frequency. The relationship between these indicators is most accurately described not by a linear, but by a logarithmic dependence. When performing a sensorimotor task, the so-called respiratory waves in the heart rhythm spectrum (HF) do not change from rest to tapping by a metronome and then to holding the rhythm by memory. At the same time, the cross-correlation coefficient demonstrates significant changes between these situations. In addition, a significant correlation was found between the change in the cross-correlation coefficient and the stability of rhythm maintenance (from memory): an increase in the cardiorespiratory synchronization leads to a decrease in motor rhythm stability. Conclusion. Assessment of cardiorespiratory synchronization due to the peculiarities of both signals (changes in heart rate and respiratory phases) requires preliminary preparation of data arrays: filtering and smoothing the stepped curve of heart rate dynamics. The calculation of the cross-correlation coefficient can be used to assess cardiorespiratory synchronization in real time and is likely to be applicable in short time fragments to assess emotional reactions and other short-term fluctuations in psychophysiological conditions, where the classical method of assessing changes in heart rhythm in the frequency or time domains is not applicable.