| Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 24, Iss. 4, Oct, 2020, pp. 367-387 @2020 Society for Chaos Theory in Psychology & Life Sciences Variability and Complexity of Non-stationary Functions: Methods for Post-exercise HRV Abstract: Heart rate variability (HRV) is a noninvasive marker of cardiac autonomic function
that has been extensively studied in a variety of populations. However, HRV analyses require
stationarity-thus, limiting the conditions in which these data can be analyzed in physiologic
and health research (e.g. post-exercise). To provide evidence and clarity on how non-stationarity
affects popular indices of variability and complexity. Simulations within physiologic
(restricted to values similar to exercise and recovery RR-intervals) and non-physiologic parameters,
with homoscedastic and heteroscedastic variances, across four sample lengths (200, 400, 800, and 2000),
and four trends (stationary, positive-linear, quadratic, and cubic) were detrended using
1-3 order polynomials and sequential differencing. Measures of variability [standard deviation
of normal intervals (SDNN) and root mean square of successive differences (rMSSD)] as well as
complexity [sample entropy (SampEn)] were calculated on each of the raw and detrended time-series.
Differential effects of trend, length, and fit were observed between physiologic and non-physiologic
parameters. rMSSD was robust against trends within physiologic parameters while both SDNN and SampEn
were positively and negatively biased by trend, respectively. Within non-physiologic parameters,
the SDNN, rMSSD, and SampEn of the raw time-series were all biased, highlighting the effect of the
scale between these two sets of parameters. However, indices of variability and complexity on the
original (trended) times-series were furthest from those of the stationary time-series, with indices
coming closer to the known values as fit become more optimal. Detrending with polynomial functions
provide reliable and accurate methods of assessing the variability and complexity of
non-stationary time-series-such as those immediately following exercise. Keywords: heart rate variability, nonlinear dynamics, post-exercise HRV, time-series, simulation study |