cvsd

Coefficient Of Variation Of Successive Differences

Aliasescvsd
Domaintime, statistics
Distribution familyGamma
Equationsdsd / mean(IBI)
Resource intensity◍◍◌◌◌ low, Level 1–2 (base statistics over successive differences). Warm (shared representation cached) is 4× cheaper. (measured, see §Resources)

Definition

Coefficient of variation of successive differences. Formally: sdsd / mean(IBI).

What does normal look like?

Fitted normative prior: Gamma(α = 2.929, θ = 0.0146), KS p = 6.3e-44, n = 61715.

Normative distribution of cvsd

Empirical distribution over the pooled nsrdb+nsr2db normative windows (360-beat windows, 120-beat stride), overlaid with the fitted Gamma prior density. Vertical lines mark the median and the 5–95% range.

Normal-range summary (pooled nsrdb+nsr2db)

statisticvalue
median0.03348
IQR (25–75%)0.02369 – 0.04859
5–95% range0.01718 – 0.09653
mean ± sd0.04277 ± 0.03589
n windows61715

n varies by feature only through per-window validity over the full pooled nsrdb+nsr2db table (n up to 61 715; e.g. sampen/mse drop windows where the statistic is undefined). ulf is the one exception: a 360-beat (~5 min) window contains no ULF-band power, so it uses a long-window NSRDB-only extraction (see its own page).

Use cases

  • Heart-rate-normalised short-term variability (RMSSD / mean NN).
  • Comparing vagal tone across subjects with different baseline rates.
  • Recovery / stress monitoring insensitive to mean HR.

Applications by area

Evidence is reported at the measure-family level; a specific variant may not be the exact index measured in every cited study.

Clinical

Coverage: statistics. A pooled literature; reviews or meta-analyses exist.

SDNN/SDANN are among the most validated HRV mortality predictors: low values predict higher cardiac/all-cause mortality post-MI, in heart failure, and even in the general population (8 cohorts, n = 21,988). CVNNI has its own well-established sub-literature as a diabetic cardiac autonomic neuropathy screening marker. One acute-decompensated-HF study reports the opposite polarity (higher SDNN/SDANN in the poor-prognosis group).

Dominant reported direction: down: low SDNN/SDANN/CVNNI → higher mortality (one acute-HF exception reversed).

Key references: (Hillebrand et al., 2013); (Rueda-Ochoa et al., 2024).

Sports & peak performance

Coverage: individual papers. A small, scattered literature with no pooled meta-analysis.

Comparatively under-used next to RMSSD in sports HRV monitoring: the field's dedicated meta-analysis pools RMSSD/HF/SD1, not SDNN. Individual studies find SDNN falls with heavy training/overreaching and recovers during taper, and tends to be higher in higher-level athletes, but effect sizes are small-sample and heterogeneous.

Dominant reported direction: down with overreaching, recovers with taper (RMSSD-dominated literature, SDNN secondary).

Key references: (Bellenger et al., 2016).

Contemplative practice

Coverage: individual papers. A small, scattered literature with no pooled meta-analysis.

The weakest of the three domains for this family: two dedicated meta-analyses found the evidence for resting and vagally-mediated HRV (including SDNN) inconclusive or null, with only a handful of trials reporting SDNN at all. Individual acute-state studies diverge sharply in direction (SDNN falls during Heartfulness meditation, rises during Chi/Kundalini-yoga).

Dominant reported direction: contested: meta-analytic null vs. technique-dependent individual-study increases/decreases.

Key references: (Rådmark et al., 2019); (Brown et al., 2021).

See the effect-distribution meta-analysis page for the harvested per-study effect sizes/p-values behind these domain summaries (docs/zoo_gen/effect_stats.csv).

Resources

Resource-intensity rank ◍◍◌◌◌ low is measured: median wall-clock time and allocations over a 360-beat window on synthetic realistic RR (docs/zoo_gen/bench_resources.jl; full grid in resource_bench.csv).

metric (360-beat window)value
cold median wall-time0.01371 ms
warm median wall-time0.003096 ms
allocations (cold)9.3 KiB

Cold = fresh memoization caches (builds every shared representation from scratch); warm = shared representations (diff, periodogram, Poincaré coords, DFA fluctuation) already cached, so only this feature is recomputed. The tier is derived from the cold cost; see Level 1–2 (base statistics over successive differences). Warm (shared representation cached) is 4× cheaper.

Citation

CV of successive differences (RMSSD/mean), Task Force (1996).

Seminal reference(s): (Task Force, European Society of Cardiology / North American Society of Pacing & Electrophysiology, 1996).

See the References page for the full bibliography.