Heart rate contribution to the clinical value of heart rate variability

higher fluctuations of R-R intervals for the slow average HR than for the fast one. Moreover, the fluctuations of R-R intervals for fast HR may not be as high as for slow HR because the R-R intervals should have become negative (Fig. Due to these facts, standard HRV analysis may be mathematically biased, particularly if patients exhibit different average HRs. To overcome this problem, one should calculate the variability of R-R intervals with respect to the average R-R interval, i.e. normalise the oscillations with respect to the mean value. One can do that by dividing the sequence of R-R intervals by the corresponding average R-R interval calculation of coefficient of variation), or divide HRV power spectrum (or its components) by the average R-R interval squared Such a normalisation is critical for investigations of HRV after different interventions which change HR because by employing this approach, one may differentiate between physiologically and mathematically mediated changes in HRV (i.e. one may exclude the mathematical bias) For example, metoprolol-induced changes of HRV become insignificant after they are normalised to the same R-R interval, suggesting that the increase in HRV after beta-blockade can be explained by a change of HR Also, in an animal model, it has been shown that beta-adrenergic receptor blockade may reduce rather than increase R-R interval variability after correction for the drug-induced HR reductions Furthermore, an employment of this correction method has helped to demonstrate that HR is a better indicator of higher fitness than its variability -i.e. an association between HRV indices and maximal oxygen intake (VO 2 max) exists mainly due to the relationship between HR and VO 2 max On the other hand, the same method has shown that an increase in HRV following dengue viral infection does not result from the accompanying reduction in HR, but reflects a real improvement in cardiac autonomic nervous control