Improved atrial fibrillation recognition algorithm based on residual network

Abstract

An improved residual network model is proposed to deal with the complex and changeable characteristics of one-dimensional electrocardiogram. In this model, firstly, in order to avoid the network degradation problem of the model along with the deepening of the number of layers, when extracting various deep-level features of ECG signals using multiple convolution layers in CNN, the residual module is integrated into the network, and an appropriate shortcut connection is selected to connect the input with the superposition output of the corresponding convolution layer to construct a deep residual network to extract more abstract signal features. Secondly, the output of the last residual module is sent to the GAP layer, and the parameters of this layer are greatly reduced compared with those of the full connection layer, which is equivalent to the compression of the model, and thus the over-fitting of the model is avoided to a certain extent. Finally, the original ECG signals were automatically classified based on the PCinCC2017 database to complete the recognition of atrial fibrillation. Experimental results show that the proposed algorithm has a classification accuracy of 86% and a F1 measure of 83%, which prove the feasibility of the model and the effectiveness of the algorithm.