Abstract:

Cardiac arrhythmia basically refers to abnormal activity of heart. Correct classification of cardiac arrhythmia is therefore crucial for appropriate treatment of heart diseases. In this paper, a novel approach is proposed for cardiac arrhythmia classification. Initially, the feature vectors extracted from raw electrocardiogram (ECG) signals are projected into a particular subspace obtained via the Common Vector Approach, which is an effective subspace method. The projected vectors are then fed into two distinct decision-tree-based classifiers—namely, C4.5 and random forest. The results obtained from the proposed approach are compared with those obtained with the original feature vectors using the same classifiers. For this purpose, the well-known MIT-BIH arrhythmia database was utilized. Six different sets of features based on QRS, time-domain, wavelet transform and power spectral density are derived from ECG signals in this database. The feature sets are then used in the classification of five main beat types including non-ectopic, ventricular ectopic, supraventricular ectopic, fusion and unknown. The experimental results reveal that the recognition performances achieved by most of the projected features are explicitly higher than those obtained with the original ones. In addition, the classification accuracy of the proposed approach climbs to 100% for the test set.

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