Multi-class Arrhythmia Detection based on Neural Network with Multi-stage Features Fusion

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases. In this paper, we proposed a novel end-to-end deep learning method for multi-class arrhythmia detection with multiple stage features fusion. The network is composed of multiple convolution and attention module. Specifically, we use skip connection operation to fuse different levels of features extracted at different stages for target task processing. And the channel-wise attention modules are adopted for effectively extracting the features learned at the different stages. By combining the attention module and convolutional neural network, the discrimination power of the network for ECG classification is improved. We demonstrate the proposed method for ECG classification on an open ECG dataset and compare it with some state-of-the-art methods, which achieves an average F1-score of 81.3% in classification of 8 types of arrhythmias and sinus rhythm. The experimental results convince the efficiency of the proposed method.