Quantifying electrocardiogram RT-RR variability interactions

A. Porta, G. Baselli, E. Caiani, A. Malliani, F. Lombardi, S. Cerutti

Research output: Contribution to journalArticlepeer-review

Abstract

A dynamic linear parametric model is designed to quantify the dependence of ventricular repolarisation duration variability on heart period changes and other immeasurable factors. The model analyses the beat-to-beat series of the RR duration and of the interval between R- and T-wave apexes (RT period). Directly from these two signals, a parametric identification procedure and spectral decomposition techniques allow RT variability to be divided into RR-related and RR-unrelated parts and allow the RT-RR transfer function to be calculated. RT variability is driven by RR changes at low frequency (LF, around 0.1 Hz) and high frequency (HF, at the respiratory rate), whereas, at very low frequencies, the RR-unrelated contribution to the total RT variability is remarkable. During tilt at LF the RR-related RT percentage power increases (p <0.02), the RR-unrelated RT percentage power remains unchanged, the gain of the RT-RR relationship largely increases (p <0.001), and the phase is not significantly modified. Both the RR-related and the RR-unrelated RT percentage powers at LF are not affected by controlled respiration, and an increase in the RT-RR gain at HF is observed (p <0.02). The proposed analysis may help to describe the regulation of the ventricular repolarisation process and to extract indexes quantifying the coupling between heart period and ventricular repolarisation interval changes.

Original languageEnglish
Pages (from-to)27-34
Number of pages8
JournalMedical and Biological Engineering and Computing
Volume36
Issue number1
DOIs
Publication statusPublished - Jan 1998

Keywords

  • Parametric identification
  • RR variability
  • RT variability
  • RT-RR interaction model
  • RT-RR transfer function
  • Spectral decomposition

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management
  • Computer Science Applications
  • Computational Theory and Mathematics

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