Nonlinear and chaos characteristics of heart period time series: healthy aging and postural change

Vesna Vuksanovic, Vera Gal

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


In this study we investigated nonlinear and linear characteristics of heart period variability with aging in supine and standing posture. Sixty healthy subjects (8-61 years) divided in three age groups participated in the study. Heart period variability was assessed by measurement of short-term scaling exponent, sample entropy, largest Lyapunov exponent and spectral low-frequency and high-frequency power. In standing, there was significant increase in short-term scaling exponent and largest Lyapunov exponent in all subjects, and significant decrease in sample entropy in children (<15 years) and young adults (15-39 years). Increasing age is associated with reduction in sample entropy in supine posture. Mutual effect of aging and postural change was reflected on heart rate and sample entropy. Correlation between low-frequency-to-high-frequency power ratio and short-term scaling exponent was found in supine posture. In standing both low-frequency and high-frequency powers are correlated with short-term scaling exponent and sample entropy. These results show that posture, standing compared to supine, has significant effect on nonlinear properties of heart period variability in healthy subjects while the influence of healthy aging is less pronounced. The findings indicate that intrinsic properties of heart period dynamics, reflected on nonlinear measures, are altered only by robust changes of autonomic modulation of heart rate.

Original languageEnglish
Pages (from-to)94-100
Number of pages7
JournalAutonomic Neuroscience: Basic & Clinical
Issue number1-2
Publication statusPublished - 31 Aug 2005


  • Adolescent
  • Adult
  • Age Factors
  • Aging
  • Analysis of Variance
  • Child
  • Electrocardiography
  • Entropy
  • Female
  • Fourier Analysis
  • Heart Rate
  • Humans
  • Male
  • Middle Aged
  • Nonlinear Dynamics
  • Posture
  • Statistics as Topic
  • Time Factors


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