Objective: A novel structure based on channel-wise attention mechanism is presented in this paper. Embedding with the proposed structure, an efficient classification model that accepts multi-lead electrocardiogram (ECG) as input is constructed. Methods: One-dimensional convolutional neural networks (CNN) have proven to be effective in pervasive classification tasks, enabling the automatic extraction of features while classifying targets. We implement the Residual connection and design a structure which can learn the weights from the information contained in different channels in the input feature map during the training process. An indicator named mean square deviation is introduced to monitor the performance of a particular model segment in the classification task on the two out of the five ECG classes. The data in the MIT-BIH arrhythmia database is used and a series of control experiments is conducted. Results: Utilizing both leads of the ECG signals as input to the neural network classifier can achieve better classification results than those from using single channel inputs in different application scenarios. Models embedded with the channel-wise attention structure always achieve better scores on sensitivity and precision than the plain Resnet models. The proposed model exceeds the performance of most of the state-of-the-art models in ventricular ectopic beats (VEB) classification, and achieves competitive scores for supraventricular ectopic beats (SVEB). Conclusion: Adopting more lead ECG signals as input can increase the dimensions of the input feature maps, helping to improve both the performance and generalization of the network model. Significance: Due to its end-to-end characteristics, and the extensible intrinsic for multi-lead heart diseases diagnosing, the proposed model can be used for the real-time ECG tracking of ECG waveforms for Holter or wearable devices.

中文翻译：

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通过基于信息的注意力卷积神经网络进行多导联心电图分类

目标：本文提出了一种基于通道注意力机制的新结构。嵌入所提出的结构，构建了一个接受多导联心电图 (ECG) 作为输入的有效分类模型。方法：一维卷积神经网络 (CNN) 已被证明在普遍分类任务中是有效的，能够在对目标进行分类的同时自动提取特征。我们实现了残差连接并设计了一种结构，该结构可以在训练过程中从输入特征图中不同通道中包含的信息中学习权重。引入了一个名为均方偏差的指标来监控特定模型段在分类任务中对五个 ECG 类别中的两个类别的性能。使用MIT-BIH心律失常数据库中的数据并进行一系列对照实验。结果：在不同的应用场景中，利用心电信号的双导联作为神经网络分类器的输入，比使用单通道输入的分类结果更好。与普通的 Resnet 模型相比，嵌入了 channel-wise attention 结构的模型总是在灵敏度和精度上获得更好的分数。所提出的模型在室性异位搏动 (VEB) 分类中超过了大多数最先进模型的性能，并在室上性异位搏动 (SVEB) 方面取得了有竞争力的分数。结论：采用更多的导联心电信号作为输入可以增加输入特征图的维度，有助于提高网络模型的性能和泛化能力。