An End-to-End 12-Leading Electrocardiogram Diagnosis System Based on Deformable Convolutional Neural Network With Good Antinoise Ability

Abstract

Electrocardiogram (ECG) is a tool to help judge heart activity. In recent years, the convolutional neural network (CNN) and various deep learning algorithms have been widely used in ECG diagnosis. CNN only considers the local feature. However, the ECG signal is susceptible to noise, and the waveform is complex, making it difficult for existing methods to get a good result. This article presents a novel neural network architecture for ECG diagnosis based on deformable CNN (Deform-CNN). The architecture makes good use of the feature-learning capability of deformable convolution to learn the time-domain and lead characteristics of multilead ECG signals. The proposed end-to-end method can achieve an overall diagnostic accuracy of 86.3% in the 12-lead ECG data of CPSC-2018, with good antinoise ability, which makes the method have a more competitive performance than other deep learning algorithms. The source code is publicly available at https://github.com/HeartbeatAI/Deform-CNN .