Oscillatory patterns in heart rate variability and complexity: A meta-analysis

Abstract

Abstract The study of instantaneous heart rate changes is a non invasive form to obtain indirect information about heart rate control. This beat-to-beat variation is denominated heart rate variability (HRV) and when estimated through frequency domain methods provides information about the sympathetic (SNS) or parasympathetic (PNS) nervous system. Beat-to-beat variation can also be estimated by nonlinear methods, then termed heart rate complexity (HRC). Even though HRC does not possess a straightforward relationship with the SNS or PNS, these estimators are also utilized to infer changes in the autonomic nervous system (ANS). In many situations, a low value of both indexes (HRV/HRC) is associated with several cardiovascular diseases. On the other hand, there are scenarios (such as, exercise and temperature challenges) in which those indexes appear to be less informative, mainly because the association between HRV/HRC and the ANS ceases to hold tight. Therefore, it is interesting to extract additional information from HRV/HRC analyses that could lead to a broader understanding of cardiac control. Previous experiments in our laboratory suggested the existence of an oscillatory component in HRV/HRC results along the time of experiment. The present study tested the existence of this pattern in HRV/HRC of 13 subjects running at constant speed. For this purpose, sine wave, linear and quadratic models were fitted to the results of these estimators. The sine wave model significantly, and more adequately than the other models, fitted the results obtained. Furthermore, the correlation obtained was significantly higher for the HRC data. This meta -analysis is a novel technique not found in the literature survey, moreover, it reveals a new way to approach cardiac control.