title: Short and long term non-linear analysis of RR variability series.
abstract: The complexity of RR variability is approached in the short and in the long term by means of black-box data analysis. Short term series of a few hundred beats are explored by means of informational entropy and predictability indexes. A correction to biases toward false determinism is performed assuming maximum uncertainty, whenever data do not furnish sufficient recurrences. Non-randomness and non-linearity are tested by means of surrogate data provided by random shuffling and phase randomization respectively. In the long term of the 24-h or of several hours, similar tests based on mutual information are applied and validated by means of surrogate series. In addition the state space reconstruction is carried out by means of state space non-linear filtering addressing directly the reconstructed trajectories. In this condition, parameters characterizing the hypothetical attractor, mainly the maximum Lyapunov exponent, can be reliably identified.
authors.affiliation: Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy. baselli@biomed.polimi.it
authors.forename: G
authors.initials: G
authors.lastname: Baselli
authors.forename: S
authors.initials: S
authors.lastname: Cerutti
authors.forename: A
authors.initials: A
authors.lastname: Porta
authors.forename: M G
authors.initials: MG
authors.lastname: Signorini
mesh_terms: D055592:Biophysical Phenomena; D001703:Biophysics; D002319:Cardiovascular System / Q000473:pathology*; D016027:Heart Transplantation; D006801:Humans; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000378:metabolism; D013997:Time Factors