  The classification of electrocardiogram (ECG) signals, which takes much time
and suffers from a high rate of misjudgment, is recognized as an extremely
challenging task for cardiologists. The major difficulty of the ECG signals
classification is caused by the long-term sequence dependencies. Most existing
approaches for ECG signal classification use Recurrent Neural Network models,
e.g., LSTM and GRU, which are unable to extract accurate features for such long
sequences. Other approaches utilize 1-Dimensional Convolutional Neural Network
(CNN), such as ResNet or its variant, and they can not make good use of the
multi-lead information from ECG signals.Based on the above observations, we
develop a multi-scale deep residual network for the ECG signal classification
task. We are the first to propose to treat the multi-lead signal as a
2-dimensional matrix and combines multi-scale 2-D convolution blocks with 1-D
convolution blocks for feature extraction. Our proposed model achieves 99.2%
F1-score in the MIT-BIH dataset and 89.4% F1-score in Alibaba dataset and
outperforms the state-of-the-art performance by 2% and 3%, respectively, view
related code and data at https://github.com/Amadeuszhao/SE-ECGNet
