Table 1 Techniques to characterize variability

Time domain

Statistical calculations of consecutive intervals

Simple, easy to calculate; proven clinically useful; gross distinction of high and low frequency variations

Sensitive to artifact; requires stationarity; fails to discriminate distinct signals

SD, RMSDD Specific to HRV: SDANN, pNNx

Frequency distribution (plot number of observations falling in selected ranges or bins)

Visual representation of data; can fit to normal or log-normal distribution

Lacks widespread clinical application; arbitrary number of bins

Skewness (measures symmetry): positive (right tail) versus negative (left) Kurtosis (measures peakedness): flatter top (<0) versus peaked (>0)

Frequency domain

Frequency spectrum representation (spectral analysis)

Visual and quantitative representation of frequency contribution to waveform; useful to evaluate relationship to mechanisms; widespread HRV evaluation

Requires stationarity and periodicity for validity; sensitive to artifact; altered by posture, sleep, activity

Total power (area under curve) Specific to HRV: ULF (<0.003 Hz), VLF (0.003–0.04 Hz), LF (0.04–0.15 Hz), HF (0.15–0.4 Hz)

Time spectrum analysis

Scale invariant (fractal) analysis

Power law: log power versus log frequency

Ubiquitous biologic application; characterization of signal with single linear relationship; enables prognostication

Requires stationarity and periodicity; requires large datasets

Slope of power law Intercept of power law

DFA

Identifies intrinsic variations 2°system (versus external stimuli), does not require stationarity

Requires large datasets (>8000 patients)

Scaling exponent a₁ (n < 11)

Scaling exponent a₂ (n > 11)

a–ß filter

Entropy

Measures the degree of disorder (information or complexity)

Unique representation of data; requires fewest data points (100–900 patients)

Needs to be complemented by other techniques

ApEN SampEN Multi-scale entropy