Radial basis function neural networks for the characterization of heart rate variability dynamics

A. Bezerianos, S. Papadimitriou, D. Alexopoulos

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


This study introduces new neural network based methods for the assessment of the dynamics of the heart rate variability (HRV) signal. The heart rate regulation is assessed as a dynamical system operating in chaotic regimes. Radial-basis function (RBF) networks are applied as a tool for learning and predicting the HRV dynamics. HRV signals are analyzed from normal subjects before and after pharmacological autonomic nervous system (ANS) blockade and from diabetic patients with dysfunctional ANS. The heart rate of normal subjects presents notable predictability. The prediction error is minimized, in fewer degrees of freedom, in the case of diabetic patients. However, for the case of pharmacological ANS blockade, although correlation dimension approaches indicate significant reduction in complexity, the RBF networks fail to reconstruct adequately the underlying dynamics. The transient attributes of the HRV dynamics under the pharmacological disturbance is elucidated as the explanation for the prediction inability.

Original languageEnglish (US)
Pages (from-to)215-234
Number of pages20
JournalArtificial intelligence in medicine
Issue number3
StatePublished - Mar 1999
Externally publishedYes


  • Autonomic nervous system
  • Heart rate
  • Heart rate variability
  • Neural network learning
  • Nonlinear dynamics
  • Nonlinear prediction

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence


Dive into the research topics of 'Radial basis function neural networks for the characterization of heart rate variability dynamics'. Together they form a unique fingerprint.

Cite this