Nonlinear analysis of heart rate variability signal: physiological knowledge and diagnostic indications

The complex structure of the heart rate variability signal (HRV) has been widely studied in order to identify the "complex" nature of its control mechanisms. By adopting methods based on the reconstruction of the HRV time series, in an embedding space, the fractal dimension and the Lyapunov exponents can be computed. These estimations must be associated to a determinism test based on surrogate data, confirming that it is a deterministic instead of a linear correlation mechanism that controls the HRV dynamics. Results in 24 hours HRV series confirm that the structure generating the signal is neither linear nor stochastic. Furthermore, methods quantifying fractal and self-similar "monofractal" characteristics (1/f^α spectrum, detrended fluctuation analysis, DFA) and a regularity statistic (approximate entropy, ApEn), allow characterizing the HRV signal and distinguishing pathological from healthy subjects. Results in the HRV signal analysis confirm the presence of a nonlinear deterministic structure in time series. Moreover, nonlinear parameters can be used to separate normal from pathological subjects. Application examples are shown concerning cardiovascular pathologies and fetal heart rate analysis.