Convolutional squeeze-and-excitation network for ECG arrhythmia detection

• Published in: Artificial intelligence in medicine 2021-11, Vol.121, p.102181-102181, Article 102181
• Main Authors: Ge, Rongjun, Shen, Tengfei, Zhou, Ying, Liu, Chengyu, Zhang, Libo, Yang, Benqiang, Yan, Ying, Coatrieux, Jean-Louis, Chen, Yang
• Format: Article
• Language: English
• Subjects: 12‑lead ECG; Arrhythmia detection; Bioengineering; Life Sciences; Se-residual convolutional module; Squeeze-and-excitation block
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2021.102181

Automatic detection of arrhythmia through an electrocardiogram (ECG) is of great significance for the prevention and treatment of cardiovascular diseases. In Convolutional neural network, the ECG signal is converted into multiple feature channels with equal weights through the convolution operation. Multiple feature channels can provide richer and more comprehensive information, but also contain redundant information, which will affect the diagnosis of arrhythmia, so feature channels that contain arrhythmia information should be paid attention to and given larger weight. In this paper, we introduced the Squeeze-and-Excitation (SE) block for the first time for the automatic detection of multiple types of arrhythmias with ECG. Our algorithm combines the residual convolutional module and the SE block to extract features from the original ECG signal. The SE block adaptively enhances the discriminative features and suppresses noise by explicitly modeling the interdependence between the channels, which can adaptively integrate information from different feature channels of ECG. The one-dimensional convolution operation over the time dimension is used to extract temporal information and the shortcut connection of the Se-Residual convolutional module in the proposed model makes the network easier to optimize. Thanks to the powerful feature extraction capabilities of the network, which can effectively extract discriminative arrhythmia features in multiple feature channels, so that no extra data preprocessing including denoising in other methods are need for our framework. It thus improves the working efficiency and keeps the collected biological information without loss. Experiments conducted with the 12-lead ECG dataset of the China Physiological Signal Challenge (CPSC) 2018 and the dataset of PhysioNet/Computing in Cardiology (CinC) Challenge 2017. The experiment results show that our model gains great performance and has great potential in clinical. •Arrhythmia detection•12-lead ECG•Se-Residual convolutional module•Squeeze-and-Excitation block