Automated Classification of Atrial Fibrillation and Atrial Flutter in ECG Signals based on Deep Learning

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.