First, note that HRV is always calculated. It is not measured. It is not a physical quantity. While these statements are also true of any computation of heart rate (see “How do I compute heart rate?”), HRV is a step further removed from any physical measurement. Heart rate is computed from the intervals between heart beats. Heart rate variability is computed from the patterns in these intervals over more extended periods of time.
Interest in HRV derives from the understanding that the exact timing of heart beats is regulated by multiple physiological inputs and the expectation that the extent to which any particular input dominates the others can be inferred from an in-depth analysis of that timing. Several algorithms have been proposed to quantify the variation. Most can be categorized by whether comparisons are made between times directly or by first converting the time series data to frequency space.
AcqKnowledge first introduced HRV analysis as an automated routine via computation of the power spectral density (i.e., conversion to frequency space). In older versions, this analysis is simply called “Heart Rate Variability”. In AcqKnowledge 4.4 and above, the same routine is found under “Analysis > HRV and RSA > Single-epoch HRV – Spectral”. The same analysis can be run independently on multiple segments via “Analysis > HRV and RSA > Multi-epoch HRV and RSA – Spectral”.
Frequency-based methods of computing HRV require an estimation of the power spectral density (PSD) of inter-beat (also called R-R) intervals over time. There is no standard method for estimating PSD. AcqKnowledge performs the estimation by breaking the time series data up into multiple (generally overlapping) intervals, removing means and trends, windowing the segments to soften the edges, performing fast Fourier transforms on these segments, and averaging together the results. The parameters used for performing these steps (e.g., segment size, type of window, etc.) can be adjusted by the user. The FFT computation is performed through the Cooley-Tukey algorithm, and for single epoch spectral computations, the resulting spectrum is divided by the spline resampling frequency (default value of 8 Hz). This factor is removed in the spreadsheet output for the multi-epoch spectral analysis. A much more extensive discussion of the algorithm and these parameters may be found in Application Note 246.
The PSD indicates how much power a signal carries as a function of frequency. It is believed that the primary elements controlling heart beats manifest at different frequencies. In particular, sympathetic control of the adult human heart causes the R-R intervals to vary over frequencies ranging from 0.04 to 0.15 cycles per second. Parasympathetic control via the vagus nerve is believed to primarily modulate the R-R intervals between 0.15 and 0.4 cycles per second. Consequently the amount of power in the signal in these two frequency bands correlates with the extent to which the heart is controlled by one type of process or the other. The frequency based analysis of HRV in AcqKnowledge computes sympathetic ratio and vagal ratio as the amount of power in each of these two (user-adjustable) bands normalized to an approximation of the total power in the signal. The software also computes a sympathetic to vagal balance as the ratio of the power in the sympathetic frequency band to that of the vagal.
last modified 6/30/2020