Multi-label ECG Abnormality Classification Using A Combined ResNet-DenseNet Architecture with ResU Blocks

Electrocardiogram (ECG) abnormality classification is to detect various types of clinical abnormalities from ECG. This paper proposes a deep neural network (DNN)-based ECG abnormality classification architecture where ResNet and DenseNet are cascaded. ResNet in the proposed architecture comprises residual U-shaped (ResU) blocks that effectively capture multi-scale feature maps without significantly increasing neural parameters. In addition, we use a multi-head self-attention (MHSA) to ensure that the model focuses on essential features in the given ECG. Experimental results show that our proposed model has superior ECG abnormality classification performance compared to other recently proposed DNN-based models.Clinical relevance: This study can offer accurate ECG analysis results using DNN, compared to recent DNN models.