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.
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