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J G Riddell - One of the best experts on this subject based on the ideXlab platform.
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beta adrenoceptor modulation and heart rate variability the value of Scatterplot measures of compactness
Cardiovascular Drugs and Therapy, 2000Co-Authors: B Silke, C G Hanratty, Sandor M Veres, J G RiddellAbstract:This article compares different methods of Scatterplot analysis to assess the optimal methodology. The Scatterplot (Poincare 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. RR(n-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. RR(n-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 [beta-1], propranolol 160 mg [beta-1 and beta-2], and ICI 118,551 25 mg [beta-2]) or agonists (xamoterol 200 mg [beta-1], salbutamol 8 mg [beta-2], prenalterol 50 mg [beta-1 and beta-2], and pindolol 10 mg [mainly beta-2] of the cardiac beta-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.
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heart rate variability effects of β adrenoceptor agonists xamoterol prenalterol and salbutamol assessed nonlinearly with Scatterplots and sequence methods
Journal of Cardiovascular Pharmacology, 1999Co-Authors: B Silke, C G Hanratty, J G RiddellAbstract:Full antagonists of the cardiac beta-adrenoceptor improve heart-rate variability (HRV) in humans; however, partial agonism at the beta2-adrenoceptor has been suggested to decrease HRV. We therefore studied the HRV effects of some partial agonists of the beta1- and beta2-adrenoceptors in normal volunteers. Under double-blind and randomised conditions (Latin square design), eight healthy volunteers received placebo; xamoterol, 200 mg (beta1-adrenoceptor partial agonist); prenalterol, 50 mg (beta1- and beta2-adrenoceptor partial agonist); salbutamol, 8 mg (beta2-adrenoceptor partial agonist); ICI 118,551, 25 mg (selective beta2-adrenoceptor antagonist); and combinations of each partial agonist with ICI 118,551. Single oral doses of medication (at weekly intervals) were administered at 22:30 h with HRV assessed from the overnight sleeping heart rates. HRV was determined by using standard time-domain summary statistics and two nonlinear methods, the Poincare plot (Scatterplot) and cardiac sequence analysis. On placebo, the sleeping heart rate decreased significantly, between 2 and 8 h after dosing. The heart rate with ICI 118,551 was unaltered. Xamoterol, prenalterol, and salbutamol increased the sleeping heart rate. ICI 118,551 blocked the heart-rate effects of salbutamol, attenuated those of prenalterol, but did not influence the xamoterol heart rate. The Scatterplot (Poincare) area was reduced by beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and combined beta1- and beta2-adrenoceptor (prenalterol) agonism. A reduction in Scatterplot length followed salbutamol, prenalterol alone, and prenalterol in combination with ICI 118,551. The geometric analysis of the Scatterplots allowed width assessment (i.e., dispersion) at fixed RR intervals. At higher heart rates (i.e., 25 and 50% of RR Scatterplot length), dispersion was decreased after xamoterol, prenalterol, and prenalterol/ICI 118,551. Cardiac sequence analysis (differences between three adjacent beats; deltaRR vs. deltaRRn+1) assessed the short-term patterns of cardiac acceleration and deceleration; four patterns were identified: +/+ (a lengthening sequencing), +/- or -/+ (balanced sequences), and finally -/- (a shortening sequence). Cardiac acceleration or deceleration episodes (i.e., number of times deltaRR and deltaRRn+1 were altered in the same direction) were increased after salbutamol and prenalterol. In conclusion, partial agonism at either the cardiac beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and beta1- plus beta2-adrenoceptors (prenalterol) altered the autonomic balance toward sympathetic dominance in healthy volunteers; blockade of the beta2-adrenoceptor with the highly selective beta2-antagonist ICI 118,551 prevented the effects of salbutamol on HRV, attenuated the HRV effects of prenalterol, but had no effect on the actions of xamoterol. Agonism at both the beta1- and beta2-adrenoceptor reduced HRV in healthy subjects; the implications for the preventive use of the beta-adrenoceptor compounds in cardiovascular disease warrant further investigation.
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heart rate variability effects of beta adrenoceptor agonists xamoterol prenalterol and salbutamol assessed nonlinearly with Scatterplots and sequence methods
Journal of Cardiovascular Pharmacology, 1999Co-Authors: B Silke, C G Hanratty, J G RiddellAbstract:Full antagonists of the cardiac beta-adrenoceptor improve heart-rate variability (HRV) in humans; however, partial agonism at the beta2-adrenoceptor has been suggested to decrease HRV. We therefore studied the HRV effects of some partial agonists of the beta1- and beta2-adrenoceptors in normal volunteers. Under double-blind and randomised conditions (Latin square design), eight healthy volunteers received placebo; xamoterol, 200 mg (beta1-adrenoceptor partial agonist); prenalterol, 50 mg (beta1- and beta2-adrenoceptor partial agonist); salbutamol, 8 mg (beta2-adrenoceptor partial agonist); ICI 118,551, 25 mg (selective beta2-adrenoceptor antagonist); and combinations of each partial agonist with ICI 118,551. Single oral doses of medication (at weekly intervals) were administered at 22:30 h with HRV assessed from the overnight sleeping heart rates. HRV was determined by using standard time-domain summary statistics and two nonlinear methods, the Poincare plot (Scatterplot) and cardiac sequence analysis. On placebo, the sleeping heart rate decreased significantly, between 2 and 8 h after dosing. The heart rate with ICI 118,551 was unaltered. Xamoterol, prenalterol, and salbutamol increased the sleeping heart rate. ICI 118,551 blocked the heart-rate effects of salbutamol, attenuated those of prenalterol, but did not influence the xamoterol heart rate. The Scatterplot (Poincare) area was reduced by beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and combined beta1- and beta2-adrenoceptor (prenalterol) agonism. A reduction in Scatterplot length followed salbutamol, prenalterol alone, and prenalterol in combination with ICI 118,551. The geometric analysis of the Scatterplots allowed width assessment (i.e., dispersion) at fixed RR intervals. At higher heart rates (i.e., 25 and 50% of RR Scatterplot length), dispersion was decreased after xamoterol, prenalterol, and prenalterol/ICI 118,551. Cardiac sequence analysis (differences between three adjacent beats; deltaRR vs. deltaRRn+1) assessed the short-term patterns of cardiac acceleration and deceleration; four patterns were identified: +/+ (a lengthening sequencing), +/- or -/+ (balanced sequences), and finally -/- (a shortening sequence). Cardiac acceleration or deceleration episodes (i.e., number of times deltaRR and deltaRRn+1 were altered in the same direction) were increased after salbutamol and prenalterol. In conclusion, partial agonism at either the cardiac beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and beta1- plus beta2-adrenoceptors (prenalterol) altered the autonomic balance toward sympathetic dominance in healthy volunteers; blockade of the beta2-adrenoceptor with the highly selective beta2-antagonist ICI 118,551 prevented the effects of salbutamol on HRV, attenuated the HRV effects of prenalterol, but had no effect on the actions of xamoterol. Agonism at both the beta1- and beta2-adrenoceptor reduced HRV in healthy subjects; the implications for the preventive use of the beta-adrenoceptor compounds in cardiovascular disease warrant further investigation.
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evaluation of the effect on heart rate variability of some agents acting at the β adrenoceptor using nonlinear Scatterplot and sequence methods
Cardiovascular Drugs and Therapy, 1998Co-Authors: B Silke, J G RiddellAbstract:There is evidence that the processes regulating heart rate variability (HRV) reflect nonlinear complexity and show “chaotic” determinism. Data analyses using nonlinear methods may therefore reveal patterns not apparent with the standard methods for HRV analysis. We have consequently used two nonlinear methods, the Poincare plot (Scatterplot) and cardiac sequence (quadrant) analysis, in addition to the standard time-domain summary statistics, during a normal volunteer investigation of the effects on HRV of some agents acting at the cardiac beta-adrenoceptor. Under double-blind and randomized conditions (Latin square design), 25 normal volunteers received placebo, salbutamol 8 mg (β2-adrenoceptor partial agonist), pindolol 10 mg (β2-adrenoceptor partial agonist), or atenolol 50 mg (β1-adrenoceptor antagonist). Single oral doses of medication (at weekly intervals) were administered at 22:30 hours, with sleeping heart rates recorded overnight. The long-term (SDNN, SDANN) and short-term (rMSSD) time-domain summary statistics were reduced by salbutamol 8 mg and increased by atenolol 50 mg compared with placebo. The reductions in both SDNN and SDANN were greater after salbutamol 8 mg compared with pindolol 10 mg. The reduced HRV after pindolol 10 mg differed from the increased HRV following atenolol 50 mg. The Poincare plot, constructed by plotting each RR interval against the preceding RR interval, was measured using a reproducible computerized method. Scatterplot length and area were reduced by salbutamol 8 mg and increased by atenolol 50 mg compared with placebo; Scatterplot length and area were lower after pindolol 10 mg compared with atenolol 50 mg. Geometric analysis of the Scatterplots allowed width assessment (i.e., dispersion) at fixed RR intervals. At the higher percentiles (i.e., 90% of Scatterplot length: low HR), salbutamol 8 mg reduced and atenolol 50 mg increased dispersion; at lower percentiles (i.e., 10%, 25%, and 50% length), atenolol 50 mg and pindolol 10 mg increased dispersion compared with placebo and salbutamol 8 mg. Cardiac sequence analysis (differences between three adjacent beats; ΔRR vs. ΔRRn+1) was used to assess the short-term patterns of cardiac acceleration and deceleration. Four patterns were identified: +/+ (a lengthening sequencing), +/− or −/+ (balanced sequences), and finally −/− (a shortening sequence). Cardiac acceleration episodes (i.e., number of times ΔRR and ΔRRn+1 were both changed) were increased in quadrants −/− and +/+ following pindolol 10 mg and salbutamol 8 mg; the beat-to-beat difference (ΔRRn+1) was reduced after salbutamol 8 mg compared with the three other groups. These results demonstrated a shift towards sympathetic dominance (β-adrenoceptor partial agonist salbutamol 8 mg) or parasympathetic dominance (β1-adrenoceptor antagonist atenolol 50 mg); pindolol 10 mg exhibited HR-dependent effects, reducing HRV at low but increasing variability at high prevailing heart rates. These nonlinear methods appear to be valuable tools to investigate HRV in health and to study the implications of perturbation of HRV with drug therapy in disease states.
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heart rate variability effects of an agonist or antagonists of the beta adrenoceptor assessed with Scatterplot and sequence analysis
Clinical Autonomic Research, 1998Co-Authors: B Silke, J G RiddellAbstract: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 Poincare 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 beta-adrenoceptor. Under double-blind and randomized conditions (Latin square design), 12 normal volunteers received placebo, celiprolol (beta 1- and beta 2-adrenoceptor partial agonist), propranolol (beta 1- and beta 2-adrenoceptor antagonist), atenolol (beta 1-adrenoceptor antagonist) and combinations of these agents. Single oral doses of medication (at weekly intervals) were administered at 22:30 hours 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 Poincare 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; delta RR vs delta RRn+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 beta-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 beta-adrenoceptor partial agonist celiprolol contrasting in parasympathetic dominance after the beta-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.
B Silke - One of the best experts on this subject based on the ideXlab platform.
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beta adrenoceptor modulation and heart rate variability the value of Scatterplot measures of compactness
Cardiovascular Drugs and Therapy, 2000Co-Authors: B Silke, C G Hanratty, Sandor M Veres, J G RiddellAbstract:This article compares different methods of Scatterplot analysis to assess the optimal methodology. The Scatterplot (Poincare 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. RR(n-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. RR(n-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 [beta-1], propranolol 160 mg [beta-1 and beta-2], and ICI 118,551 25 mg [beta-2]) or agonists (xamoterol 200 mg [beta-1], salbutamol 8 mg [beta-2], prenalterol 50 mg [beta-1 and beta-2], and pindolol 10 mg [mainly beta-2] of the cardiac beta-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.
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heart rate variability effects of β adrenoceptor agonists xamoterol prenalterol and salbutamol assessed nonlinearly with Scatterplots and sequence methods
Journal of Cardiovascular Pharmacology, 1999Co-Authors: B Silke, C G Hanratty, J G RiddellAbstract:Full antagonists of the cardiac beta-adrenoceptor improve heart-rate variability (HRV) in humans; however, partial agonism at the beta2-adrenoceptor has been suggested to decrease HRV. We therefore studied the HRV effects of some partial agonists of the beta1- and beta2-adrenoceptors in normal volunteers. Under double-blind and randomised conditions (Latin square design), eight healthy volunteers received placebo; xamoterol, 200 mg (beta1-adrenoceptor partial agonist); prenalterol, 50 mg (beta1- and beta2-adrenoceptor partial agonist); salbutamol, 8 mg (beta2-adrenoceptor partial agonist); ICI 118,551, 25 mg (selective beta2-adrenoceptor antagonist); and combinations of each partial agonist with ICI 118,551. Single oral doses of medication (at weekly intervals) were administered at 22:30 h with HRV assessed from the overnight sleeping heart rates. HRV was determined by using standard time-domain summary statistics and two nonlinear methods, the Poincare plot (Scatterplot) and cardiac sequence analysis. On placebo, the sleeping heart rate decreased significantly, between 2 and 8 h after dosing. The heart rate with ICI 118,551 was unaltered. Xamoterol, prenalterol, and salbutamol increased the sleeping heart rate. ICI 118,551 blocked the heart-rate effects of salbutamol, attenuated those of prenalterol, but did not influence the xamoterol heart rate. The Scatterplot (Poincare) area was reduced by beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and combined beta1- and beta2-adrenoceptor (prenalterol) agonism. A reduction in Scatterplot length followed salbutamol, prenalterol alone, and prenalterol in combination with ICI 118,551. The geometric analysis of the Scatterplots allowed width assessment (i.e., dispersion) at fixed RR intervals. At higher heart rates (i.e., 25 and 50% of RR Scatterplot length), dispersion was decreased after xamoterol, prenalterol, and prenalterol/ICI 118,551. Cardiac sequence analysis (differences between three adjacent beats; deltaRR vs. deltaRRn+1) assessed the short-term patterns of cardiac acceleration and deceleration; four patterns were identified: +/+ (a lengthening sequencing), +/- or -/+ (balanced sequences), and finally -/- (a shortening sequence). Cardiac acceleration or deceleration episodes (i.e., number of times deltaRR and deltaRRn+1 were altered in the same direction) were increased after salbutamol and prenalterol. In conclusion, partial agonism at either the cardiac beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and beta1- plus beta2-adrenoceptors (prenalterol) altered the autonomic balance toward sympathetic dominance in healthy volunteers; blockade of the beta2-adrenoceptor with the highly selective beta2-antagonist ICI 118,551 prevented the effects of salbutamol on HRV, attenuated the HRV effects of prenalterol, but had no effect on the actions of xamoterol. Agonism at both the beta1- and beta2-adrenoceptor reduced HRV in healthy subjects; the implications for the preventive use of the beta-adrenoceptor compounds in cardiovascular disease warrant further investigation.
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heart rate variability effects of beta adrenoceptor agonists xamoterol prenalterol and salbutamol assessed nonlinearly with Scatterplots and sequence methods
Journal of Cardiovascular Pharmacology, 1999Co-Authors: B Silke, C G Hanratty, J G RiddellAbstract:Full antagonists of the cardiac beta-adrenoceptor improve heart-rate variability (HRV) in humans; however, partial agonism at the beta2-adrenoceptor has been suggested to decrease HRV. We therefore studied the HRV effects of some partial agonists of the beta1- and beta2-adrenoceptors in normal volunteers. Under double-blind and randomised conditions (Latin square design), eight healthy volunteers received placebo; xamoterol, 200 mg (beta1-adrenoceptor partial agonist); prenalterol, 50 mg (beta1- and beta2-adrenoceptor partial agonist); salbutamol, 8 mg (beta2-adrenoceptor partial agonist); ICI 118,551, 25 mg (selective beta2-adrenoceptor antagonist); and combinations of each partial agonist with ICI 118,551. Single oral doses of medication (at weekly intervals) were administered at 22:30 h with HRV assessed from the overnight sleeping heart rates. HRV was determined by using standard time-domain summary statistics and two nonlinear methods, the Poincare plot (Scatterplot) and cardiac sequence analysis. On placebo, the sleeping heart rate decreased significantly, between 2 and 8 h after dosing. The heart rate with ICI 118,551 was unaltered. Xamoterol, prenalterol, and salbutamol increased the sleeping heart rate. ICI 118,551 blocked the heart-rate effects of salbutamol, attenuated those of prenalterol, but did not influence the xamoterol heart rate. The Scatterplot (Poincare) area was reduced by beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and combined beta1- and beta2-adrenoceptor (prenalterol) agonism. A reduction in Scatterplot length followed salbutamol, prenalterol alone, and prenalterol in combination with ICI 118,551. The geometric analysis of the Scatterplots allowed width assessment (i.e., dispersion) at fixed RR intervals. At higher heart rates (i.e., 25 and 50% of RR Scatterplot length), dispersion was decreased after xamoterol, prenalterol, and prenalterol/ICI 118,551. Cardiac sequence analysis (differences between three adjacent beats; deltaRR vs. deltaRRn+1) assessed the short-term patterns of cardiac acceleration and deceleration; four patterns were identified: +/+ (a lengthening sequencing), +/- or -/+ (balanced sequences), and finally -/- (a shortening sequence). Cardiac acceleration or deceleration episodes (i.e., number of times deltaRR and deltaRRn+1 were altered in the same direction) were increased after salbutamol and prenalterol. In conclusion, partial agonism at either the cardiac beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and beta1- plus beta2-adrenoceptors (prenalterol) altered the autonomic balance toward sympathetic dominance in healthy volunteers; blockade of the beta2-adrenoceptor with the highly selective beta2-antagonist ICI 118,551 prevented the effects of salbutamol on HRV, attenuated the HRV effects of prenalterol, but had no effect on the actions of xamoterol. Agonism at both the beta1- and beta2-adrenoceptor reduced HRV in healthy subjects; the implications for the preventive use of the beta-adrenoceptor compounds in cardiovascular disease warrant further investigation.
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evaluation of the effect on heart rate variability of some agents acting at the β adrenoceptor using nonlinear Scatterplot and sequence methods
Cardiovascular Drugs and Therapy, 1998Co-Authors: B Silke, J G RiddellAbstract:There is evidence that the processes regulating heart rate variability (HRV) reflect nonlinear complexity and show “chaotic” determinism. Data analyses using nonlinear methods may therefore reveal patterns not apparent with the standard methods for HRV analysis. We have consequently used two nonlinear methods, the Poincare plot (Scatterplot) and cardiac sequence (quadrant) analysis, in addition to the standard time-domain summary statistics, during a normal volunteer investigation of the effects on HRV of some agents acting at the cardiac beta-adrenoceptor. Under double-blind and randomized conditions (Latin square design), 25 normal volunteers received placebo, salbutamol 8 mg (β2-adrenoceptor partial agonist), pindolol 10 mg (β2-adrenoceptor partial agonist), or atenolol 50 mg (β1-adrenoceptor antagonist). Single oral doses of medication (at weekly intervals) were administered at 22:30 hours, with sleeping heart rates recorded overnight. The long-term (SDNN, SDANN) and short-term (rMSSD) time-domain summary statistics were reduced by salbutamol 8 mg and increased by atenolol 50 mg compared with placebo. The reductions in both SDNN and SDANN were greater after salbutamol 8 mg compared with pindolol 10 mg. The reduced HRV after pindolol 10 mg differed from the increased HRV following atenolol 50 mg. The Poincare plot, constructed by plotting each RR interval against the preceding RR interval, was measured using a reproducible computerized method. Scatterplot length and area were reduced by salbutamol 8 mg and increased by atenolol 50 mg compared with placebo; Scatterplot length and area were lower after pindolol 10 mg compared with atenolol 50 mg. Geometric analysis of the Scatterplots allowed width assessment (i.e., dispersion) at fixed RR intervals. At the higher percentiles (i.e., 90% of Scatterplot length: low HR), salbutamol 8 mg reduced and atenolol 50 mg increased dispersion; at lower percentiles (i.e., 10%, 25%, and 50% length), atenolol 50 mg and pindolol 10 mg increased dispersion compared with placebo and salbutamol 8 mg. Cardiac sequence analysis (differences between three adjacent beats; ΔRR vs. ΔRRn+1) was used to assess the short-term patterns of cardiac acceleration and deceleration. Four patterns were identified: +/+ (a lengthening sequencing), +/− or −/+ (balanced sequences), and finally −/− (a shortening sequence). Cardiac acceleration episodes (i.e., number of times ΔRR and ΔRRn+1 were both changed) were increased in quadrants −/− and +/+ following pindolol 10 mg and salbutamol 8 mg; the beat-to-beat difference (ΔRRn+1) was reduced after salbutamol 8 mg compared with the three other groups. These results demonstrated a shift towards sympathetic dominance (β-adrenoceptor partial agonist salbutamol 8 mg) or parasympathetic dominance (β1-adrenoceptor antagonist atenolol 50 mg); pindolol 10 mg exhibited HR-dependent effects, reducing HRV at low but increasing variability at high prevailing heart rates. These nonlinear methods appear to be valuable tools to investigate HRV in health and to study the implications of perturbation of HRV with drug therapy in disease states.
-
heart rate variability effects of an agonist or antagonists of the beta adrenoceptor assessed with Scatterplot and sequence analysis
Clinical Autonomic Research, 1998Co-Authors: B Silke, J G RiddellAbstract: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 Poincare 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 beta-adrenoceptor. Under double-blind and randomized conditions (Latin square design), 12 normal volunteers received placebo, celiprolol (beta 1- and beta 2-adrenoceptor partial agonist), propranolol (beta 1- and beta 2-adrenoceptor antagonist), atenolol (beta 1-adrenoceptor antagonist) and combinations of these agents. Single oral doses of medication (at weekly intervals) were administered at 22:30 hours 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 Poincare 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; delta RR vs delta RRn+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 beta-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 beta-adrenoceptor partial agonist celiprolol contrasting in parasympathetic dominance after the beta-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.
C G Hanratty - One of the best experts on this subject based on the ideXlab platform.
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beta adrenoceptor modulation and heart rate variability the value of Scatterplot measures of compactness
Cardiovascular Drugs and Therapy, 2000Co-Authors: B Silke, C G Hanratty, Sandor M Veres, J G RiddellAbstract:This article compares different methods of Scatterplot analysis to assess the optimal methodology. The Scatterplot (Poincare 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. RR(n-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. RR(n-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 [beta-1], propranolol 160 mg [beta-1 and beta-2], and ICI 118,551 25 mg [beta-2]) or agonists (xamoterol 200 mg [beta-1], salbutamol 8 mg [beta-2], prenalterol 50 mg [beta-1 and beta-2], and pindolol 10 mg [mainly beta-2] of the cardiac beta-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.
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heart rate variability effects of β adrenoceptor agonists xamoterol prenalterol and salbutamol assessed nonlinearly with Scatterplots and sequence methods
Journal of Cardiovascular Pharmacology, 1999Co-Authors: B Silke, C G Hanratty, J G RiddellAbstract:Full antagonists of the cardiac beta-adrenoceptor improve heart-rate variability (HRV) in humans; however, partial agonism at the beta2-adrenoceptor has been suggested to decrease HRV. We therefore studied the HRV effects of some partial agonists of the beta1- and beta2-adrenoceptors in normal volunteers. Under double-blind and randomised conditions (Latin square design), eight healthy volunteers received placebo; xamoterol, 200 mg (beta1-adrenoceptor partial agonist); prenalterol, 50 mg (beta1- and beta2-adrenoceptor partial agonist); salbutamol, 8 mg (beta2-adrenoceptor partial agonist); ICI 118,551, 25 mg (selective beta2-adrenoceptor antagonist); and combinations of each partial agonist with ICI 118,551. Single oral doses of medication (at weekly intervals) were administered at 22:30 h with HRV assessed from the overnight sleeping heart rates. HRV was determined by using standard time-domain summary statistics and two nonlinear methods, the Poincare plot (Scatterplot) and cardiac sequence analysis. On placebo, the sleeping heart rate decreased significantly, between 2 and 8 h after dosing. The heart rate with ICI 118,551 was unaltered. Xamoterol, prenalterol, and salbutamol increased the sleeping heart rate. ICI 118,551 blocked the heart-rate effects of salbutamol, attenuated those of prenalterol, but did not influence the xamoterol heart rate. The Scatterplot (Poincare) area was reduced by beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and combined beta1- and beta2-adrenoceptor (prenalterol) agonism. A reduction in Scatterplot length followed salbutamol, prenalterol alone, and prenalterol in combination with ICI 118,551. The geometric analysis of the Scatterplots allowed width assessment (i.e., dispersion) at fixed RR intervals. At higher heart rates (i.e., 25 and 50% of RR Scatterplot length), dispersion was decreased after xamoterol, prenalterol, and prenalterol/ICI 118,551. Cardiac sequence analysis (differences between three adjacent beats; deltaRR vs. deltaRRn+1) assessed the short-term patterns of cardiac acceleration and deceleration; four patterns were identified: +/+ (a lengthening sequencing), +/- or -/+ (balanced sequences), and finally -/- (a shortening sequence). Cardiac acceleration or deceleration episodes (i.e., number of times deltaRR and deltaRRn+1 were altered in the same direction) were increased after salbutamol and prenalterol. In conclusion, partial agonism at either the cardiac beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and beta1- plus beta2-adrenoceptors (prenalterol) altered the autonomic balance toward sympathetic dominance in healthy volunteers; blockade of the beta2-adrenoceptor with the highly selective beta2-antagonist ICI 118,551 prevented the effects of salbutamol on HRV, attenuated the HRV effects of prenalterol, but had no effect on the actions of xamoterol. Agonism at both the beta1- and beta2-adrenoceptor reduced HRV in healthy subjects; the implications for the preventive use of the beta-adrenoceptor compounds in cardiovascular disease warrant further investigation.
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heart rate variability effects of beta adrenoceptor agonists xamoterol prenalterol and salbutamol assessed nonlinearly with Scatterplots and sequence methods
Journal of Cardiovascular Pharmacology, 1999Co-Authors: B Silke, C G Hanratty, J G RiddellAbstract:Full antagonists of the cardiac beta-adrenoceptor improve heart-rate variability (HRV) in humans; however, partial agonism at the beta2-adrenoceptor has been suggested to decrease HRV. We therefore studied the HRV effects of some partial agonists of the beta1- and beta2-adrenoceptors in normal volunteers. Under double-blind and randomised conditions (Latin square design), eight healthy volunteers received placebo; xamoterol, 200 mg (beta1-adrenoceptor partial agonist); prenalterol, 50 mg (beta1- and beta2-adrenoceptor partial agonist); salbutamol, 8 mg (beta2-adrenoceptor partial agonist); ICI 118,551, 25 mg (selective beta2-adrenoceptor antagonist); and combinations of each partial agonist with ICI 118,551. Single oral doses of medication (at weekly intervals) were administered at 22:30 h with HRV assessed from the overnight sleeping heart rates. HRV was determined by using standard time-domain summary statistics and two nonlinear methods, the Poincare plot (Scatterplot) and cardiac sequence analysis. On placebo, the sleeping heart rate decreased significantly, between 2 and 8 h after dosing. The heart rate with ICI 118,551 was unaltered. Xamoterol, prenalterol, and salbutamol increased the sleeping heart rate. ICI 118,551 blocked the heart-rate effects of salbutamol, attenuated those of prenalterol, but did not influence the xamoterol heart rate. The Scatterplot (Poincare) area was reduced by beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and combined beta1- and beta2-adrenoceptor (prenalterol) agonism. A reduction in Scatterplot length followed salbutamol, prenalterol alone, and prenalterol in combination with ICI 118,551. The geometric analysis of the Scatterplots allowed width assessment (i.e., dispersion) at fixed RR intervals. At higher heart rates (i.e., 25 and 50% of RR Scatterplot length), dispersion was decreased after xamoterol, prenalterol, and prenalterol/ICI 118,551. Cardiac sequence analysis (differences between three adjacent beats; deltaRR vs. deltaRRn+1) assessed the short-term patterns of cardiac acceleration and deceleration; four patterns were identified: +/+ (a lengthening sequencing), +/- or -/+ (balanced sequences), and finally -/- (a shortening sequence). Cardiac acceleration or deceleration episodes (i.e., number of times deltaRR and deltaRRn+1 were altered in the same direction) were increased after salbutamol and prenalterol. In conclusion, partial agonism at either the cardiac beta1-adrenoceptor (xamoterol), beta2-adrenoceptor (salbutamol), and beta1- plus beta2-adrenoceptors (prenalterol) altered the autonomic balance toward sympathetic dominance in healthy volunteers; blockade of the beta2-adrenoceptor with the highly selective beta2-antagonist ICI 118,551 prevented the effects of salbutamol on HRV, attenuated the HRV effects of prenalterol, but had no effect on the actions of xamoterol. Agonism at both the beta1- and beta2-adrenoceptor reduced HRV in healthy subjects; the implications for the preventive use of the beta-adrenoceptor compounds in cardiovascular disease warrant further investigation.
Jean-daniel Fekete - One of the best experts on this subject based on the ideXlab platform.
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A multidimensional brush for Scatterplot data analytics
2014Co-Authors: Michaël Aupetit, Nicolas Heulot, Jean-daniel FeketeAbstract:Brushing is a fundamental interaction for visual analytics. A brush is usually defined as a closed region of the screen used to select data items and to highlight them in the current view and other linked views. Scatterplots are also standard ways to visualize values for two variables of a set of multidimensional data. We propose a technique to brush and interactively cluster multidimensional data navigating through a single of their Scatterplot projection.
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rolling the dice multidimensional visual exploration using Scatterplot matrix navigation
IEEE Transactions on Visualization and Computer Graphics, 2008Co-Authors: Niklas Elmqvist, Pierre Dragicevic, Jean-daniel FeketeAbstract:Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using Scatterplots. This exploration is performed using a matrix of Scatterplots that gives an overview of the possible configurations, thumbnails of the Scatterplots, and support for interactive navigation in the multidimensional space. Transitions between Scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets.
Adam Dolezal - One of the best experts on this subject based on the ideXlab platform.
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Additional file 9 of Transcriptomic responses to diet quality and viral infection in Apis mellifera
2019Co-Authors: Lindsay Rutter, Dianne Cook, Jimena Carrillo-tripp, Bryony Bonning, Amy Toth, Adam DolezalAbstract:The 224 DEGs from the third cluster of the Galbraith dataset (originally shown in Fig. 3) superimposed as turquoise dots onto all genes as black dots in the form of a Scatterplot matrix. The data has been standardized. “N” represents non-inoculated control samples and “V” represents virus-treated samples. We confirm that the DEGs mostly follow the expected structure, with their placement deviating from the x=y line in the treatment Scatterplots, but adhering to the x=y line in the replicate Scatterplots. (PNG 618 kb
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Additional file 8 of Transcriptomic responses to diet quality and viral infection in Apis mellifera
2019Co-Authors: Lindsay Rutter, Dianne Cook, Jimena Carrillo-tripp, Bryony Bonning, Amy Toth, Adam DolezalAbstract:The 327 DEGs from the second cluster of the Galbraith dataset (originally shown in Fig. 3) superimposed as dark orange dots onto all genes as black dots in the form of a Scatterplot matrix. The data has been standardized. “N” represents non-inoculated control samples and “V” represents virus-treated samples. We confirm that the DEGs mostly follow the expected structure, with their placement deviating from the x=y line in the treatment Scatterplots, but adhering to the x=y line in the replicate Scatterplots. (PNG 589 kb
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Additional file 14 of Transcriptomic responses to diet quality and viral infection in Apis mellifera
2019Co-Authors: Lindsay Rutter, Dianne Cook, Jimena Carrillo-tripp, Bryony Bonning, Amy Toth, Adam DolezalAbstract:The 43 virus-related DEGs from our dataset superimposed onto all genes in the form of a Scatterplot matrix. Only replicates 10, 11, and 12 are shown from both treatment groups. The data has been standardized. “N” represents non-inoculated control samples and “V” represents virus-treated samples. We see that, compared to the Scatterplot matrices from certain clusters of the Galbraith data, the 43 DEGs from this subset of six samples from our data do not paint as clear of a picture, and most of them unexpectedly deviate from the x=y line in the virus-related replicate plots. (PNG 587 kb
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Additional file 12 of Transcriptomic responses to diet quality and viral infection in Apis mellifera
2019Co-Authors: Lindsay Rutter, Dianne Cook, Jimena Carrillo-tripp, Bryony Bonning, Amy Toth, Adam DolezalAbstract:The 43 virus-related DEGs from our dataset superimposed as magenta dots onto all genes in the form of a Scatterplot matrix. Only replicates 4, 5, and 6 are shown from both treatment groups. The data has been standardized. “N” represents non-inoculated control samples and “V” represents virus-treated samples. We see that, compared to the Scatterplot matrices from certain clusters of the Galbraith data, the 43 DEGs from this subset of six samples from our data do not paint as clear of a picture, and most of them unexpectedly adhere to the x=y line in the treatment plots. (PNG 579 kb
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Additional file 11 of Transcriptomic responses to diet quality and viral infection in Apis mellifera
2019Co-Authors: Lindsay Rutter, Dianne Cook, Jimena Carrillo-tripp, Bryony Bonning, Amy Toth, Adam DolezalAbstract:The 43 virus-related DEGs from our dataset superimposed as magenta dots onto all genes in the form of a Scatterplot matrix. Only replicates 1, 2, and 3 are shown from both treatment groups. The data has been standardized. “N” represents non-inoculated control samples and “V” represents virus-treated samples. We see that, compared to the Scatterplot matrices from certain clusters of the Galbraith data, the 43 DEGs from this subset of six samples from our data do not paint as clear of a picture, sometimes unexpectedly deviating from the x=y line in the replicate plots and sometimes unexpectedly adhering to the x=y line in the treatment plots. (PNG 584 kb
