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The classification of electrocardiogram (ECG) signals, which takes much timeand suffers from a high rate of misjudgment, is recognized as an extremelychallenging task for cardiologists. The major difficulty of the ECG signalsclassification is caused by the long-term sequence dependencies. Most existingapproaches for ECG signal classification use Recurrent Neural Network models,e.g., LSTM and GRU, which are unable to extract accurate features for such longsequences. Other approaches utilize 1-Dimensional Convolutional Neural Network(CNN), such as ResNet or its variant, and they can not make good use of themulti-lead information from ECG signals.Based on the above observations, wedevelop a multi-scale deep residual network for the ECG signal classificationtask. We are the first to propose to treat the multi-lead signal as a2-dimensional matrix and combines multi-scale 2-D convolution blocks with 1-Dconvolution 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 andoutperforms the state-of-the-art performance by 2% and 3%, respectively, viewrelated code and data at https://github.com/Amadeuszhao/SE-ECGNet

SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification