Dynamic behavior of heart rate in ischemic stroke

Background and Purpose-Traditional spectral and nonspectral methods have sh own that heart rate (WR) variability is reduced after stroke. Some patients with poor outcome, however, show randomlike, complex patterns of HR behavi or that traditional analysis techniques are unable to quantify. Therefore, we designed the present study to evaluate the complexity and correlation pr operties of HR dynamics after stroke by using new analysis methods based on nonlinear dynamics and fractals ("chaos theory"). Methods-In addition to the traditional spectral components of HR variabilit y, we measured instantaneous beat-to-beat variability and long-term continu ous variability analyzed from Poincare plots, fractal correlation propertie s, and approximate entropy of R-R interval dynamics from 24-hour ambulatory ECG recordings in 30 healthy control subjects, 31 hemispheric stroke patie nts, and 15 brain stem stroke patients (8 medullary, 7 pontine) in the acut e phase of stroke and 6 months after stroke. Results-In the acute phase, the traditional spectral components of HR varia bility and the long-term continuous variability from Poincare plots were im paired (P<0.01) in patients with hemispheric and medullary brain stem strok e, but not in patients with pontine brain stem stroke, in comparison with c ontrol subjects. At 6 months after stroke, measures of HR variability in he mispheric stroke patients were still lower (P<0.05) than those of the contr ol subjects. Various complexity and fractal measures of HR variability were similar in patients and control subjects. The conventional frequency domai n measures of HR variability as well as the Poincare measures showed strong correlations (Pearson correlation coefficient, r=0.68 to r=0.90) with each other but only weak correlations (r=0.09 to r=0.56) with the complexity an d fractal measures of HR variability. Conclusions-Hemispheric and medullary brain stem infarctions seem to damage the cardiovascular autonomic regulatory system and appear as abnormalities in the magnitude of HR variability. These abnormalities can be more easily detected with the use of analysis methods of HR variability, which are bas ed on moment statistics, than by methods based on nonlinear dynamics. Abnor mal HR variability may be involved in prognostically unfavorable cardiac co mplications and other known manifestations of autonomic failure associated with stroke.