NONLINEAR DYNAMICS OF CARDIOVASCULAR VARIABILITY SIGNALS

Long-term regulation of beat-to-beat variability involves several diff erent kinds of controls. A linear approach performed by parametric mod els enhances the short-term regulation of the autonomic nervous system . Some non-linear long-term regulation can be assessed by the chaotic deterministic approach applied to the beat-to-beat variability of the discrete RR-interval series, extracted from the ECG. For chaotic deter ministic systems, trajectories of the state vector describe a strange attractor characterized by a fractal of dimension D. Signals are suppo sed to be generated by a deterministic and finite dimensional but non- linear dynamic system with trajectories in a multi-dimensional space-s tate. We estimated the fractal dimension through the Grassberger and P rocaccia algorithm and Self-Similarity approaches of the 24-h heart-ra te variability (HRV) signal in different physiological and pathologica l conditions such as severe heart failure, or after heart transplantat ion. State-space representations through Return Maps are also obtained . Differences between physiological and pathological cases have been a ssessed and generally a decrease in the system complexity is correlate d to pathological conditions.