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

• Published in: PLOS ONE Vol. 10; no. 10; p. e0140123
• Main Authors: Krasteva, Vessela, Jekova, Irena, Leber, Remo, Schmid, Ramun, Abächerli, Roger
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science (PLoS) 13.10.2015; Public Library of Science
• Subjects: Algorithms; Analysis; Arrhythmia; Biomedical engineering; Biophysics; Cardiac arrhythmia; Care and treatment; Classification; Classifiers; Cluster Analysis; Clustering; Clusters; Complications and side effects; Computer science; Correlation; Databases as Topic; Decision making; Decision trees; Decomposition; Development and progression; Discriminant Analysis; Electrocardiography; Fourier transforms; Fuzzy Logic; Fuzzy systems; Heart; Heart diseases; Heart Rate; Heart Rate - physiology; Humans; Internet; Iterative methods; Linear programming; Medical technology; Medicine; Models, Cardiovascular; Multivariate analysis; Neural networks; Optimization; Patient outcomes; Pruning; Q; Quantitative psychology; R; Reproducibility of Results; Research Article; Risk factors; Sample Size; Science; Signal processing; Template matching; Training; Ventricle; Wavelet transforms; Iran; Bulgaria; Switzerland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0140123

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.