Cardiovascular disease is the most important cause of death in the world. In the early stage of cardiovascular disease, arrhythmia is often accompanied. Therefore, it is of great significance to carry out safe and effective arrhythmia detection for prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the algorithmic paradigm of deep learning, multi-class arrhythmia classification based on automatic feature extraction of ECG has become increasingly attractive. However, the majority of studies cannot accept varied-length ECG signals and have limited performance in detecting multi-class arrhythmias. In this study, we propose a multi-label classification network based on the multi-scale feature fusion module for arrhythmia in 12-lead varied-length ECG. Our model utilizes the complementary power between different structures, which include group convolution block, Spatial Pyramid Pooling (SPP) layer, and multi-scale feature fusion module. The proposed method can extract features from every lead separately for 12-lead varied-length records, and effectively achieve multi-scale feature extraction and cross-scale information complementarity of ECG by integrating multiple convolution kernels with different receptive fields. The experimental results show that our model achieved an overall classification F1 score of 83.3%. Combining all these excellent features, this model offers a solution for varied-length signal processing problems.

Atrial fibrillation and atrial flutter are two more common rapid atrial arrhythmias, they are independent of each other and can transform each other. Because the clinical characteristics of the two diseases are similar, it is easy to cause misdiagnosis. Therefore, effective diagnosis of distinguishing atrial fibrillation and atrial flutter can reduce the damage suffered by patients during treatment. In this paper, we propose a kind of based on residual network and multi-scale feature fusion structure of neural network to automatically classify atrial fibrillation, atrial flutter and other ECG signals. The ECG signals used are derived from clinical data labeled by expert diagnosis. In addition, we used several popular classification methods proposed in the past for comparative experiments to verify the classification performance of our models. The experimental results show that the total F1, recall and precision of the proposed algorithm are 0.923, 0.916 and 0.93. The algorithm we propose has the potential to provide reference for subsequent research.