title: Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system.
abstract: The classification of heart beats is important for automated arrhythmia monitoring devices. The study describes two different classifiers for the identification of premature ventricular complexes (PVCs) in surface ECGs. A decision-tree algorithm based on inductive learning from a training set and a fuzzy rule-based classifier are explained in detail. Traditional features for the classification task are extracted by analysing the heart rate and morphology of the heart beats from a single lead. In addition, a novel set of features based on the use of a filter bank is presented. Filter banks allow for time-frequency-dependent signal processing with low computational effort. The performance of the classifiers is evaluated on the MIT-BIH database following the AAMI recommendations. The decision-tree algorithm has a gross sensitivity of 85.3% and a positive predictivity of 85.2%, whereas the gross sensitivity of the fuzzy rule-based system is 81.3%, and the positive predictivity is 80.6%.
authors.affiliation: Department of Electrical & Computer Engineering, University of Wisconsin, Madison, USA.
authors.forename: O
authors.initials: O
authors.lastname: Wieben
authors.forename: V X
authors.initials: VX
authors.lastname: Afonso
authors.forename: W J
authors.initials: WJ
authors.lastname: Tompkins
mesh_terms: D003663:Decision Trees; D004562:Electrocardiography / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D018879:Ventricular Premature Complexes / Q000175:diagnosis*