title: Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System.
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.
authors.affiliation: Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
authors.forename: Vessela
authors.initials: V
authors.lastname: Krasteva
authors.affiliation: Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
authors.forename: Irena
authors.initials: I
authors.lastname: Jekova
authors.affiliation: Biomed Research and Signal Processing, Schiller AG, Baar, Switzerland.
authors.forename: Remo
authors.initials: R
authors.lastname: Leber
authors.affiliation: Biomed Research and Signal Processing, Schiller AG, Baar, Switzerland; Bern University of Applied Sciences, Medical Technology Center, Bern, Switzerland.
authors.forename: Ramun
authors.initials: R
authors.lastname: Schmid
authors.affiliation: Biomed Research and Signal Processing, Schiller AG, Baar, Switzerland; Bern University of Applied Sciences, Medical Technology Center, Bern, Switzerland; University Hospital Basel, Cardiovascular Research Institute Basel, Bazel, Switzerland.
authors.forename: Roger
authors.initials: R
authors.lastname: Abächerli
mesh_terms: D016000:Cluster Analysis; D019992:Databases as Topic; D016002:Discriminant Analysis; D004562:Electrocardiography; D017143:Fuzzy Logic; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D015203:Reproducibility of Results; D018401:Sample Size