Heartbeat classification method combining multi-branch convolutional neural networks and transformer

The detection and classification of arrhythmias are crucial steps in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often fail to consider both the morpho-logical and temporal features of the electrocardiogram (ECG) simultaneously. Therefore, we propose a hybrid heartbeat classification method that combines Transformer and multi branch convolutional neural networks (CNNs). Then, use the fusion module to stitch the features obtained from different classifiers. We performed three different heartbeat classification protocols on the MIT-BIH arrhythmia (MIT-BIH-AR) database and analyzed performance on SVEB and VEB classes to validate our method. The first was an intra-patient protocol with an