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 sequencetosequence 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 smallsize 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 MITBIH 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 stateoftheart methods using their assessment strategies. Experimental results indicate that our model can achieve better performance under most circumstances.

A highprecision arrhythmia classification method based on dual fully connected neural network Abstract   As an important arrhythmia detection method, the electrocardiogram (ECG) can directly reflect abnormalities in cardiac physiological activity. In view of the difficulty in the diagnosis of arrhythmia in different people, automatic arrhythmia detection methods have been studied in previous works. In this paper, we present a dual fullyconnected neural network model for accurate classification of heartbeats. Our method is following the AAMI interpatient standard, which includes normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). Firstly, a total of 105 features are extracted from the preprocessed signals. Then, a twolayer classifier is introduced in the classification stage. Each layer contains two independent fullyconnected neural networks, and the threshold criterion is also added in the second layer. For verification, both the MIT arrhythmia database (MITDB) and the MIT supraventricular arrhythmia database (SVDB) were adopted. The experiments demonstrate that the proposed method has high performance for arrhythmia detection. It also achieves high sensitivity for class S and V, which can easily detect potentially abnormal heartbeats. Furthermore, the proposed method can interfere with the classification effect for a certain disease and have more advantages in dataset size when comparing a convolutional neural network (CNN). Once properly trained, the proposed method can be employed as a tool to automatically detect arrhythmia from ECG.