Key result
SE-ECGNet achieved an F1-score of 99.2% on the MIT-BIH dataset and 89.4% on the Alibaba dataset, outperforming state-of-the-art approaches by 2% and 3%, respectively.
Why the study?
Existing ECG classification methods struggle with long-term sequence dependencies and fail to effectively utilize multi-lead information.
Does SE-ECGNet improve ECG signal classification compared to existing RNN and 1-D CNN models?
Does SE-ECGNet improve ECG signal classification compared to existing RNN and 1-D CNN models?
A novel multi-scale deep residual network (SE-ECGNet) treating multi-lead ECG signals as 2D matrices significantly improves classification performance over existing methods.
Answers are grounded in this paper and cite their sources.
May improve automated ECG classification; leaves open prospective clinical validation before practice adoption.
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.
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Zhang et al. (2020) studied ECG signal classification. SE-ECGNet vs. State-of-the-art approaches (RNNs, 1D CNNs) was evaluated on F1-score. SE-ECGNet achieved an F1-score of 99.2% on the MIT-BIH dataset and 89.4% on the Alibaba dataset, outperforming state-of-the-art approaches by 2% and 3%, respectively.
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