title: Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics.
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.
authors.affiliation: Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen, Germany. ulrich.parlitz@ds.mpg.de
authors.forename: U
authors.initials: U
authors.lastname: Parlitz
authors.forename: S
authors.initials: S
authors.lastname: Berg
authors.forename: S
authors.initials: S
authors.lastname: Luther
authors.forename: A
authors.initials: A
authors.lastname: Schirdewan
authors.forename: J
authors.initials: J
authors.lastname: Kurths
authors.forename: N
authors.initials: N
authors.lastname: Wessel
mesh_terms: D000368:Aged; D016022:Case-Control Studies; D004562:Electrocardiography / Q000145:classification* / Q000379:methods*; D005260:Female; D006321:Heart / Q000502:physiology*; D006333:Heart Failure / Q000503:physiopathology; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted