A token selection-based multi-scale dual-branch CNN-transformer network for 12-lead ECG signal classification

The timely identification of cardiovascular diseases is critical for effective intervention, with the electrocardiogram (ECG) serving as a pivotal diagnostic tool. Recent advancements in deep learning-based methods have significantly enhanced the accuracy of ECG signal classification. In clinical settings, cardiologists rely on diagnoses derived from standardized 12-lead ECG recordings. It must be acknowledged that there is considerable redundancy in the 12-lead ECG recordings used for ECG signal classification, thereby hindering their generalization capabilities. Meanwhile, considering multi-scale features in 12-lead ECG recordings is a crucial aspect that is often overlooked by existing methods. Based on the above observations, we develop a multi-scale Convolutional Transformer network for ECG signal classification. By utilizing learnable Convolutional neural network (CNN) blocks and novel dual-branch Transformer encoders, the proposed network automatically extracts features at different scales, resulting in superior feature representation. Additionally, by discarding low-importance patches and focusing on high-importance patches, we effectively alleviate information redundancy in the 12-lead ECG recordings. We conduct comprehensive experiments on three commonly used ECG datasets. The Research results show that our proposed network outperforms existing state-of-the-art networks in multiple tasks.