The Measurements And Applications Of HRV

By prof WB Yeung

2007/08/26

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).

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

vagal input).

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.

完整全文下載：點這裡 (PDF)

## 沒有留言:

## 張貼留言