Imbalanced ECG data classification using a novel model based on active training subset selection and modified broad learning system.

• A novel hybrid-based model is proposed for imbalanced ECG data classification. • The imbalanced classification task is solved in an iterative manner. • Samples in each class with high uncertainty are selected to form the training subset. • The label of the test sample is obtained by voting on predictions of all iterations. • The proposed model has excellent and stable performance on imbalanced datasets. This paper classifies non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats in the MIT-BIH arrhythmia database. The classification encounters serious class imbalance since the number of beats in N (majority class) with sample number above the average per class is heavily outnumbered than that in S, V, and F (minority classes) with sample number below the average per class. To address the class imbalance, a novel model based on active training subset selection and modified broad learning system (MBLS) is proposed. In each iteration, the MBLS trained with the current training subset is used to predict the class label of the test sample and actively select a new training subset for the next iteration. Finally, the class of the test sample is determined by voting on the predictions of all iterations. The experimental results show that our method has excellent performance and outperforms the existing methods. [ABSTRACT FROM AUTHOR]