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  • Poincaré plot  (1)
  • beta-adrenoceptor agonist antagonist  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Clinical autonomic research 8 (1998), S. 145-153 
    ISSN: 1619-1560
    Keywords: heart rate variability ; Poincaré plot ; scatterplot ; nonlinear ; β-adrenoceptor ; agonist ; antagonist
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract There is evidence that the processes regulating heart rate variations reflect non-linear complexity and show ‘chaotic’ determinism. Data analyses using non-linear methods may therefore reveal patterns not apparent with conventional statistical approaches. We have consequently investigated two non-linear methods, the Poincaré plot (scatterplot) and cardiac sequence (quadrant) analysis, and compared these with standard time-domain summary statistics, during a normal volunteer investigation of an agonist and antagonists of the cardiac β-adrenoceptor. Under double-blind and randomized conditions (Latin square design), 12 normal volunteers received placebo, celiprolol (β2- and β2-adrenoceptor partial agonist), propranolol (β2- and β2-adrenoceptor antagonist), atenolol (β1- adrenoceptor antagonist) and combinations of these agents. Single oral doses of medication (at weekly intervals) were administered at 22∶30 h with sleeping heart rates recorded overnight. The long (SDNN, SDANN) and short-term (rmsSD) time-domain summary statistics were reduced by celiprolol — effects different from the unchanged or small increases after atenolol and propranolol alone. The Poincaré plot was constructed by plotting each RR interval against the preceding RR interval, but unlike previous descriptions of the method, an automated computer method, with a high level of reproducibility, was employed. Scatterplot length and area were reduced following celiprolol and different from the small increases after propranolol and atenolol. The geometric analysis of the scatterplots allowed width assessment (i.e. dispersion) at fixed RR intervals. Differences between the drugs were confined to the higher percentiles (i.e. 75% and 90% of scatterplot length: low heart rate). The long-term time-domain statistics (SDNN, SDANN) correlated best with scatterplot length and area whereas the short-term heart rate variability (HRV) indices (rmsSD, pNN50) correlated strongly with scatterplot width. Cardiac sequence analysis (differences between three adjacent beats; ΔRR vs ΔRR n+1) assessed the short-term patterns of cardiac acceleration and deceleration, four patterns are identified: +/+ (a lengthening sequencing), +/− or −/+ (balanced sequences), and finally −/− (a shortening sequence). A running count of events by quadrant, together with the average magnitude of the differences was computed. The β-adrenoceptor partial agonist celiprolol increased acceleration sequences. The duration of beat-to-beat difference shortened after celiprolol; this contrasted with increased duration of beat-to-beat difference after propranolol and atenolol. These results demonstrated a shift towards sympathetic dominance after the β-adrenoceptor partial agonist celiprolol contrasting with parasympathetic dominance after the β-adrenoceptor antagonists propranolol and atenolol. These non-linear methods appear to be valuable tools to investigate HRV in health and in cardiovascular disease and to study the implications of alterations in autonomic control during therapeutic intervention. Clin Auton Res 8:145–153
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Cardiovascular drugs and therapy 14 (2000), S. 433-440 
    ISSN: 1573-7241
    Keywords: heart rate variability ; scatterplot ; compactness ; beta-adrenoceptor agonist antagonist
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract This article compares different methods of scatterplot analysis to assess the optimal methodology. The scatterplot (Poincaré plot) is a nonlinear heart rate variability method where a “return map” is constructed by plotting each current cycle against the previous beat (RR vs. RRn−1). Geometric analysis of the scatterplot allows short-term and long-term heart rate variability (HRV) to be assessed. A three-dimensional construct is also possible, where the third axis represents the density of values, at any given RR vs. RRn−1 intersection. Topological methods of analysis can compute the density distribution function or compactness of a dataset. Scatterplots that otherwise appear very similar in the two-dimensional plot may be clearly differentiated using this approach. Correct characterization may improve the ability of scatterplot analysis to predict outcomes in cardiovascular disease. We have assessed two computational approaches that take account of scatterplot density, namely, the heart rate variability fraction and the compactness measure. Scatterplots were constructed from three double-blind and randomized placebo controlled studies conducted in a total of 49 healthy subjects. Single oral doses of antagonists (atenolol 50 mg [β-1, propranolol 160 mg [β-1 and β-2], and ICI 118,551 25 mg [β-2]) or agonists (xamoterol 200 mg [β-1], salbutamol 8 mg [β-2], prenalterol 50 mg [β-1 and β-2], and pindolol 10 mg [mainly β-2] of the cardiac β-adrenoceptor were studied. Salbutamol, pindolol, and xamoterol increased compactness and reduced HRV fraction significantly compared with placebo. However, when compared with the more conventional scatterplot parameters, these newer density methods were found to be less discriminating. An alternative approach to improve scatterplot discrimination, using the combination of several scatterplot features, is under investigation.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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