title: Non-linear dynamics of cardiovascular variability signals.
abstract: Long-term regulation of beat-to-beat variability involves several different kinds of controls. A linear approach performed by parametric models 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 deterministic systems, trajectories of the state vector describe a strange attractor characterized by a fractal of dimension D. Signals are supposed to be generated by a deterministic and finite dimensional but non-linear dynamic system with trajectories in a multi-dimensional space-state. We estimated the fractal dimension through the Grassberger and Procaccia algorithm and Self-Similarity approaches of the 24-h heart-rate variability (HRV) signal in different physiological and pathological conditions such as severe heat failure, or after heart transplantation. State-space representations through Return Maps are also obtained. Differences between physiological and pathological cases have been assessed and generally a decrease in the system complexity is correlated to pathological conditions.
authors.affiliation: Department of Biomedical Engineering, Polytechnic University, Milano, Italy.
authors.forename: M G
authors.initials: MG
authors.lastname: Signorini
authors.forename: S
authors.initials: S
authors.lastname: Cerutti
authors.forename: S
authors.initials: S
authors.lastname: Guzzetti
authors.forename: R
authors.initials: R
authors.lastname: Parola
mesh_terms: D000465:Algorithms; D004562:Electrocardiography; D015716:Electrocardiography, Ambulatory; D006333:Heart Failure / Q000503:physiopathology*; D016027:Heart Transplantation / Q000502:physiology*; D006801:Humans; D017711:Nonlinear Dynamics; D012016:Reference Values