论文标题
SE-ECGNET：具有ECG信号分类的多尺度深残留网络，具有挤压和激发模块
SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification
论文作者
论文摘要
心电图（ECG）信号的分类需要花费很多时间，并且遭受了高判断率，这对心脏病学家来说是一项极具挑战性的任务。 ECG信号分类的主要困难是由长期序列依赖性引起的。 ECG信号分类的大多数现有方法都使用复发性神经网络模型，例如LSTM和GRU，这些模型无法为如此长的序列提取准确的特征。其他方法利用了1维卷积神经网络（CNN），例如Resnet或其变体，并且无法充分利用来自ECG信号的多潜在信息信息。基于上述观测值，我们为ECG信号分类任务开发了多规模的深残留网络。我们是第一个提议将多铅信号视为二维矩阵的人，并将多尺度的2-D卷积块与1-D卷积块相结合，以进行特征提取。 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
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
