Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics

  • Authors:

  • U. Parlitz;S. Berg;S. Luther;A. Schirdewan;J. Kurths;N. Wessel

  • Affiliations:

  • Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen, Germany and Institute for Nonlinear Dynamics, Georg-August-Universität Göttingen, Am Fassbe ...;Drittes Physikalisches Institut, Georg-August-Universität Göttingen, 37077 Göttingen, Germany;Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen, Germany and Institute for Nonlinear Dynamics, Georg-August-Universität Göttingen, Am Fassbe ...;Department of Cardiology and Pneumology, Charité-Universitätsmedizin Berlin,Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany;AG Nichtlineare Dynamik (S) / Kardiovaskuläre Physik, Institut für Physik, Humboldt-Universität zu Berlin, Robert-Koch-Platz 4, 10115 Berlin, Germany and Potsdam Institute for Clima ...;AG Nichtlineare Dynamik (S) / Kardiovaskuläre Physik, Institut für Physik, Humboldt-Universität zu Berlin, Robert-Koch-Platz 4, 10115 Berlin, Germany

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2012

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Abstract

The performance of (bio-)signal classification strongly depends on the choice of suitable features (also called parameters or biomarkers). In this article we evaluate the discriminative power of ordinal pattern statistics and symbolic dynamics in comparison with established heart rate variability parameters applied to beat-to-beat intervals. As an illustrative example we distinguish patients suffering from congestive heart failure from a (healthy) control group using beat-to-beat time series. We assess the discriminative power of individual features as well as pairs of features. These comparisons show that ordinal patterns sampled with an additional time lag are promising features for efficient classification.