Quantification of Heart Rate Variability Using Methods Derived from Nonlinear Dynamics

Abstract

Considerable interest has developed in the idea that analysis of heart rate variability (HRV) may be a useful clinical tool. Unfortunately, the studies thus far have been confounding not only because of the numerous conditioning physiological processes, but also because of the varieties of ways of quantifying HRV. Perhaps most confusing have been the claims drawn from application of power spectral analysis and nonlinear dynamics (“chaos theory”) (8,14,20). In the former, this may result not only from an inadequate appreciation of the mathematical requirements for spectral analysis, but also from the fact that it is generally incapable of resolving nonlinearities. As an alternative, claims have been made that HRV is chaotic as analyzed by dimensions, entropies, and Lyapunov exponents. Such measurements, however, often disregard the requirements for stationarity, data length, and minimal noise (1). Moreover, careful analysis of a typical electrocardiographic (ECG) period demonstrate that the dynamics are not deterministic (in the chaotic sense) but are nondeterministic and cannot be predicted for the future even if initial conditions were known with infinite precision and without noise (4,5).