Arrhythmia Classification with Heartbeat-Aware Transformer

Electrocardiography (ECG) is a conventional method in arrhythmia diagnosis. In this paper, we proposed a novel neural network model which treats typical heartbeat classification task as ‘Translation’ problem. By introducing Transformer structure into model, and adding heartbeat-aware attention mechanism to enhance the alignment between encoded sequence and decoded sequence, after trained with ECG database, (which are collected from 200k patients in over 2000 hospitals for more than 10 years), the validation result of independent test dataset shows that this new heartbeat-aware Transformer model can outperform classic Transformer and other sequence to sequence methods. Finally, we show that the visualization of encoder-decoder attention weights provides more interpretable information about how a Transformer make a diagnosis based on raw ECG signals, which has guiding significance in clinical diagnosis.