title: Ventricular ectopic beats classification using Sparse Representation and Gini Index.
abstract: In this study, we consider using sparse representation and the Gini Index (GI) for Arrhythmia classification. Our approach involves, first, designing a separate dictionary for each Arrhythmia class using a set of labeled training QRS complexes. Sparse representations, based on the designed dictionaries, of each new test QRS complex are then calculated. Its class is finally predicted using the winner-takes-all principle; that is, the class associated with the highest GI is chosen. Our experiments showed promising results for the classification of premature ventricular contractions using a patient-specific approach. For many of the subjects considered, our classifier attained accuracies close to 100 % on the test set.
authors.forename: Hamza
authors.initials: H
authors.lastname: Baali
authors.forename: Mostefa
authors.initials: M
authors.lastname: Mesbah
mesh_terms: D000465:Algorithms; D006801:Humans; D018879:Ventricular Premature Complexes