Medical & Biological Engineering & Computing

, Volume 37, Issue 5, pp 560–565 | Cite as

Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system



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-bassed system is 81.3%, and the positive predictivity is 80.6%.


Beat classification ECG analysis Fuzzy logic Filter bank Time-frequency analysis 


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Copyright information

© IFMBE 1999

Authors and Affiliations

  1. 1.Department of Electrical & Computer EngineeringUniversity of WisconsinMadisonUSA
  2. 2.Endocardial Solutions, Inc.Saint PaulUSA

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