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Handling Imbalanced ECG Heartbeat Classification with Adaptive Sample Masking Update

Datasets with unequal class distribution are known as imbalanced. When using a deep learning model for imbalanced data reasoning, the performance of the model will be significantly decreased. This is a serious problem, especially in the field of cardiovascular, where the data distribution between various medical institutions is quite different. To address this issue, a novel multi-task electrocardiogram (ECG) heartbeat classification method is proposed based on adaptive update. First, adaptive masking is used to assign the importance of training data samples. Second, the weight for each task is adaptive adjusted to make the model further adapted to local tasks. Comprehensive experimental evaluations on our collected data set prove the effectiveness of the proposed method. With adaptive updates, the F1 indicator of ECG classification increases from 49.54% to 75.5%.