A proposed multi-view and multi-scale deep neural network for 12-lead ECG classification outperformed existing state-of-the-art networks across multiple multi-label datasets.
A novel multi-view multi-scale neural network improves the accuracy of multi-label 12-lead ECG classification compared to existing models.
The 12-lead electrocardiogram (ECG) is a common method used to diagnose cardiovascular diseases. Recently, ECG classification using deep neural networks has been more accurate and efficient than traditional methods. Most ECG classification methods usually connect the 12-lead ECG into a matrix and then input this matrix into a deep neural network. We propose a multi-view and multi-scale deep neural network for ECG classification tasks considering different leads as different views, taking full advantage of the diversity of different lead features in a 12-lead ECG. The proposed network utilizes a multi-view approach to effectively fuse different lead features, and uses a multi-scale convolutional neural network structure to obtain the temporal features of an ECG at different scales. In addition, the spatial information and channel relationships of ECG features are captured by coordinate attention to enhance the feature representation of the network. Since our network contains six view networks, to reduce the size of the network, we also explore the distillation of dark knowledge from the multi-view network into a single-view network. Experimental results on multiple multi-label datasets show that our multi-view network outperforms existing state-of-the-art networks in multiple tasks.
Yang et al. (Thu,) conducted a other in Cardiovascular diseases (ECG classification). Multi-view multi-scale deep neural network vs. Existing state-of-the-art networks was evaluated on ECG classification performance on multiple multi-label datasets. A proposed multi-view and multi-scale deep neural network for 12-lead ECG classification outperformed existing state-of-the-art networks across multiple multi-label datasets.