Fusing Transformer Model with Temporal Features for ECG Heartbeat Classification

ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. Traditional heartbeat classification methods rely on handcrafted features and often fail to learn potentially abstract patterns, while current deep learning based methods usually consist of complex convolutional and recursive structures. In this paper, considering the time sequence property of ECG signals, we propose a novel heartbeat classification method based on Transformer, a sequence-to-sequence model with a relatively simple architecture and higher degree of parallelism. To adapt Transformer to our problem, we use only the encoder part of the model for that ECG signals do not have translation signals. We also replace the dropout layers with batch normalization layers, considering our small-size feature space and the natural differences among patients. Further, we fuse handcrafted temporal features with the features learnt by Transformer to better capture rhythmic patterns in ECG signals. We have conducted extensive experiments on the MIT-BIH arrhythmia database using both the original dataset and an augmented dataset with more balanced data. The results show that our model can achieve 99.62% accuracy, 95.09% precision and 94.12% sensitivity on the original dataset and 99.87% accuracy, 99.74% precision and 99.74% sensitivity on the augmented dataset. Besides, we have performed multiple experiments against state-of-the-art methods using their assessment strategies. Experimental results indicate that our model can achieve better performance under most circumstances.