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.