Variation in the dominant period during ventricular fibrillation

Michael Small, Dejin Yu, Robert G. Harrison

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Time-varying periodicities are commonly observed in biological time series. In this paper, we discuss three different algorithms to detect and quantify change in periodicity. Each technique uses a sliding window to estimate periodic components in short subseries of a longer recording. The three techniques we utilize are based on: 1) standard Fourier spectral estimation; 2) an information theoretic adaption of linear (autoregressive) modeling; and 3) geometric properties of the embedded time series. We compare the results obtained from each of these methods using artificial data and experimental data from swine ventricular fibrillation (VF). Spectral estimates have previously been applied to VF time series to show a time-dependent trend in the dominant frequency. We confirm this result by showing that the dominant period of VF, following onset, first decreases to a minimum and then rises to a plateau. Furthermore, our algorithms detect longer period correlations which may indicate the presence of additional periodic oscillations or more complex nonlinear structure. We show that in general this possibly nonlinear structure is most apparent immediately after the onset of VF.

Original languageEnglish
Pages (from-to)1056-1061
Number of pages6
JournalIEEE Transactions on Biomedical Engineering
Volume48
Issue number9
DOIs
Publication statusPublished - 2001

Keywords

  • Description length
  • Linear modeling
  • Periodicity detection
  • Unstable periodic orbits
  • Ventricular fibrillation

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