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Updated: Jun 25, 2026

Optocardiography and Electrophysiology Studies of Ex Vivo Langendorff-perfused Hearts
Published on: November 7, 2019
Altered complexity and correlation properties of R-R interval dynamics before the spontaneous onset of paroxysmal
S Vikman1, T H Mäkikallio, S Yli-Mäyry
1Division of Cardiology, Department of Medicine, the Tampere School of Public Health, University of Tampere, Finland. saila.vikman@koti.tpo.fi
Background:
Trigger mechanisms for the onset of paroxysmal atrial fibrillation (AF) in patients without structural heart disease are not well established. New analysis methods of heart rate (HR) variability based on nonlinear system theory may reveal features and abnormalities in R-R interval behavior that are not detectable by traditional analysis methods. The purpose of this study was to reveal possible alterations in the dynamics of R-R intervals before the spontaneous onset of paroxysmal AF.
Methods And Results:
Traditional time and frequency domain HR variability indices, along with the short-term scaling exponent alpha(1) and approximate entropy (ApEn), were analyzed in 20-minute intervals before 92 episodes of spontaneous, paroxysmal AF in 22 patients without structural heart disease. Traditional HR variability measures showed no significant changes before the onset of AF. A progressive decrease occurred both in ApEn (1.09+/-0.26 120 to 100 minutes before AF; 0.88+/-0.24 20 to 0 minutes before AF; P<0.001) and in alpha(1) (1.01+/-0.28 120 to 100 minutes before AF, 0.89+/-0.28 20 to 0 minutes before AF; P<0.05) before the AF episodes. Both ApEn (0. 89+/-0.27 versus 1.02+/-0.30; P<0.05) and alpha(1) (0.91+/-0.28 versus 1.27+/-0.21; P<0.001) were also lower before the onset of AF compared with values obtained from matched healthy control subjects.
Conclusions:
A decrease in the complexity of R-R intervals and altered fractal properties in short-term R-R interval dynamics precede the spontaneous onset of AF in patients with no structural heart disease. Further studies are needed to determine the physiological correlates of these new, nonlinear HR variability measures.
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