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Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System

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  • Vessela Krasteva
  • Irena Jekova
  • Remo Leber
  • Ramun Schmid
  • Roger Abächerli

Abstract

This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3–6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ‘if-then’ rules.

Suggested Citation

  • Vessela Krasteva & Irena Jekova & Remo Leber & Ramun Schmid & Roger Abächerli, 2015. "Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-29, October.
    • Handle: RePEc:plo:pone00:0140123
    DOI: 10.1371/journal.pone.0140123
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    Cited by:

    1. Belenky Vadim & Klicenko Olga & Gelman Victor & Golovkin Vladimir, 2019. "The New Evidence of Equality of Performance of Classification Tree Method to Discriminant Analysis," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(4), pages 97-101, June.

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