A Dual-scale Network with Spatial-temporal Attention for 12-lead ECG Classification

The electrocardiogram (ECG) signal is commonly used to screen and diagnose cardiovascular diseases.In recent years, deep neural networks have been regarded as an effective way for automatic ECG disease diagnosis.The convolutional neural network is widely used for ECG signal extraction because it can obtain different levels of information.However, most previous studies adopt single scale convolution filters to extract ECG signal features, ignoring the complementarity between ECG signal features of different scales.In the paper, we propose a dual-scale network with convolution filters of different sizes for 12-lead ECG classification.Our model can extract and fuse ECG signal features of different scales.In addition, different spatial and time periods of the feature map obtained from the 12-lead ECG may have different contributions to ECG classification.Therefore, we add a spatial-temporal attention to each scale sub-network to emphasize the representative local spatial and temporal features.Our approach is evaluated on PTB-XL dataset and achieves 0.9307, 0.8152, and 89.11 on macroaveraged ROC-AUC score, a maximum F1 score, and mean accuracy, respectively.The experiment results have proven that our approach outperforms the baselines.