论文标题
Sevggnet-LSTM：一个用于ECG分类的融合深度学习模型
SEVGGNet-LSTM: a fused deep learning model for ECG classification
论文作者
论文摘要
本文提出了一种用于ECG分类的融合深度学习算法。它利用了ECG分类的组合卷积和复发性神经网络的优势，以及注意机制的重量分配能力。首先将输入ECG信号分割并标准化，然后将其馈入组合的VGG和LSTM网络进行特征提取和分类。注意机制（SE块）嵌入到核心网络中，以增加重要特征的重量。来自不同来源和设备的两个数据库用于性能验证，结果很好地证明了所提出算法的有效性和鲁棒性。
This paper presents a fused deep learning algorithm for ECG classification. It takes advantages of the combined convolutional and recurrent neural network for ECG classification, and the weight allocation capability of attention mechanism. The input ECG signals are firstly segmented and normalized, and then fed into the combined VGG and LSTM network for feature extraction and classification. An attention mechanism (SE block) is embedded into the core network for increasing the weight of important features. Two databases from different sources and devices are employed for performance validation, and the results well demonstrate the effectiveness and robustness of the proposed algorithm.
