Imbalance Reduction Techniques Applied to ECG Classification Problem

Jedrzej Kozal, Pawel Ksieniewicz

Abstract: In this work we explored capabilities of improving deep learning models performance by reducing the dataset imbalance. For our experiments a highly imbalanced ECG dataset MIT-BIH was used. Multiple approaches were considered. First we introduced mutliclass UMCE, the ensemble designed to deal with imbalanced datasets. Secondly, we studied the impact of applying oversampling techniques to a training set. smote without prior majority class undersampling was used as one of the methods. Another method we used was smote with noise introduced to synthetic learning examples. The baseline for our study was a single ResNet network with undersampling of the training set. Mutliclass UMCE proved to be superior compared to the baseline model, but failed to beat the results obtained by a single model with smote applied to training set. Introducing perturbations to signals generated by smote did not bring significant improvement. Future work may consider combining multiclass UMCE with smote.