Abstract: The automatic classification of different types of arrhythmias can provide reliable diagnosis information for doctors and effectively improve the diagnosis efficiency of this kind of diseases. Therefore, this paper proposes an improved residual dense network for automatic classification of arrhythmias. In the model, the improved residual dense block is used as the basic module of residual dense network, and the depthwise separable convolution is used to replace the traditional convolution to effectively extract the features between channels, which reduces the amount of calculation. At the same time, the channel attention mechanism is introduced to realize the feature selection and improve the weight distribution of important features. Based on the public data set provided by 2018 China physiological signal challenge, nine types of arrhythmias are classified, and the Macro F1_score reaches 81.2%, which is better than the mainstream deep learning network model. The experimental results verify the feasibility and advantages of the model, and provide a new method for arrhythmia classification.

Key words: arrhythmia, residual dense network, depthwise separable convolution, channel attention mechanism, multi-label classification