Time-Varying HRV (Sliding-Window Time and Frequency Metrics)

Computes heart-rate-variability trajectories by sliding a time window over the RR series and evaluating time-domain (SDNN, RMSSD) and frequency-domain (LF, HF, LF/HF) metrics in each window, reusing ecgHRVtime and ecgHRVfreq (Task Force 1996; Mainardi 2009).

Usage

ecgHRVtimevarying(
  rr,
  window_sec = 300,
  step_sec = 30,
  freq_method = c("ar", "welch", "lomb"),
  detrend = FALSE,
  detrend_lambda = 500,
  min_beats = 20L,
  rhythm_check = FALSE
)

Arguments

rr: A data.frame with columns channel, rr_ms and time_sec (as returned by ecgRRintervals), assumed ordered in time per channel.
window_sec: Analysis window length in seconds (default 300).
step_sec: Step between successive windows in seconds (default 30).
freq_method: Spectral method for ecgHRVfreq: "ar" (default), "welch" or "lomb".
detrend, detrend_lambda: Passed to ecgHRVfreq for optional smoothness-priors detrending of the resampled tachogram.
min_beats: Minimum RR intervals in a window to compute metrics; windows with fewer beats yield NA metrics (default 20).
rhythm_check: Passed to the per-window HRV functions (default FALSE, so the trajectory is continuous and not gated by the AF detector).

Value

A data.frame with one row per window per channel and columns channel, window, time_start, time_center, n_beats, sdnn, rmssd, lf, hf and lf_hf.

References

Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043-1065. Mainardi, L.T. (2009). On the quantification of heart rate variability spectral parameters using time-frequency and time-varying methods. Philosophical Transactions of the Royal Society A, 367(1887), 255-275.

Examples

set.seed(1)
n <- 600
rr <- data.frame(channel = 1L,
                 rr_ms = 800 + 25 * sin(2 * pi * 0.1 * cumsum(rep(0.8, n))) +
                   rnorm(n, sd = 10))
rr$time_sec <- cumsum(rr$rr_ms) / 1000
traj <- ecgHRVtimevarying(rr, window_sec = 120, step_sec = 30)
head(traj)
#>   channel window  time_start time_center n_beats     sdnn    rmssd       lf
#> 1       1      1   0.8057793    60.80578     150 19.04766 15.23051 286.0625
#> 2       1      2  30.8057793    90.80578     150 19.60577 15.93059 296.2325
#> 3       1      3  60.8057793   120.80578     150 20.10344 16.30058 326.4678
#> 4       1      4  90.8057793   150.80578     150 20.81888 17.25596 314.6790
#> 5       1      5 120.8057793   180.80578     150 21.21406 17.74164 342.8496
#> 6       1      6 150.8057793   210.80578     150 20.86647 17.52086 318.8135
#>         hf    lf_hf
#> 1 20.42544 14.00521
#> 2 22.10450 13.40146
#> 3 24.45791 13.34815
#> 4 27.57396 11.41218
#> 5 27.30200 12.55768
#> 6 25.95065 12.28538