From: Complex systems and the technology of variability analysis
Variability analysis | Description | Advantages | Limitations | Output variables |
---|---|---|---|---|
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 |