The Measurements And Applications Of HRV
By prof WB Yeung
In short, HRV appears to be a marker of two processes, relevant to the conceptualization of allostatic load: (1) frequent activation (short
term dips in HRV in response to acute stress); and (b) inadequate response (long-term vagal withdrawal, resulting in the over-activity of the
counter-regulatory system -- in this case, the sympathetic control of cardiac rhythm).
How is HRV measured?
Originally, HRV was assessed manually from calculation of the mean R-R interval and its standard deviation measured on short-term (e.g.,
5 minute) electrocardiograms. The smaller the standard deviation in R-R intervals, the lower is the HRV. To date, over 26 different types of
arithmetic manipulations of R-R intervals have been used in the literature to represent HRV. Examples include: the standard deviations of
the normal mean R-R interval obtained from successive 5-minute periods over 24-hour Holter recordings (called the SDANN index); the
number of instances per hour in which two consecutive R-R intervals differ by more than 50 msec over 24-hours (called the pNN50 index);
the root-mean square of the difference of successive R-R intervals (the rMSSD index); the difference between the shortest R-R interval
during inspiration and the longest during expiration (called the MAX-MIN, or peak-valley quantification of HRV); and the base of the
triangular area under the main peak of the R-R interval frequency distribution diagram obtained from 24-hour recording; and so on. So far,
experimental and simulation data appear to indicate that the various methods of expressing HRV are largely equivalent, and there is no
evidence that any one method is superior to another, provided measurement windows are 5 minutes or longer.
Figure 1. Tachogram
In contrast to the so-called time domain measures of HRV cited above, recent developments in microprocessor technology has enabled the calculation of frequency measures based on mathematical manipulations performed on the same ECG-derived data. Frequency
measures involve the spectral analysis of HRV. Briefly, R-R interval data are represented on a tachogram (Figure 1), in which the y-axis
plots the R-R intervals, and the x-axis the total number of beats. Spectral analysis of the tachogram transforms the signal from time to
frequency on the x-axis, by representing the signal as a combination of sine and cosine waves, with different amplitudes and frequencies
Figure 2. Power spectrum of HRV (PSD = power spectral density)
The approach uses Fourier transforms. The HRV spectrum contains two major components: the high frequency (0.18-0.4 Hz) component,
which is synchronous with respiration and is identical to RSA. The second is a low frequency (0.04 to 0.15 Hz) component that appears to
be mediated by both the vagus and cardiac sympathetic nerves. The power of spectral components is the area below the relevant
frequencies presented in absolute units (square milliseconds). The total power of a signal, integrated over all frequencies, is equal to the
variance of the entire signal. Some investigators have used the ratio of the low-to-high frequency spectra as an index of parasympatheticsympathetic
balance; however, this remains controversial because of our lack of complete understanding of the low frequency component
(which seems to be affected by centrally generated brainstem rhythms, baroreceptor feedback influences, as well as both sympathetic and
As a measure of vagal activity, spectral analysis of the high-frequency component probably offers no additional information over timedomain
measures of RSA. On the other hand, the meaning and utility of the low frequency component deserves further investigation.