基于并行架构和时空注意力机制的心电分类方法

为了有效提取心电信号 (ECG) 的时空特征和提高分类准确性，提出基于深度学习的并行架构心电分类模型. 该模型采用基于GCA Block和GTSA Block模块实现多路特征融合的时空注意力机制. 使用双向长短时记忆网络和卷积神经网络作为基特征提取器，分别捕捉心电信号序列数据的前后依赖关系和不同尺度上的局部相关特征，实现对5种不同类型的心电信号的自动分类. 在MIT-BIH数据集上验证的结果表明，该方法对5种不同心电信号的总体分类准确率、特异性、敏感度、精确度和Macro-F1分别为99.50%、99.61%、96.20%、98.02%和97.08%. 相较于其他心电分类模型，该模型不仅能够有效地缩短网络模型深度，防止模型过拟合，而且能够更准确地提取心电信号的时空特征，获得更好的分类性能.

Classification method for electrocardiograph signals based on parallel architecture model and spatial-temporal attention mechanism

A parallel architecture electrocardiograph (ECG) classification model based on deep learning was proposed in order to effectively extract the spatiotemporal characteristics of ECG signals and improve the classification accuracy. A spatiotemporal attention mechanism based on gate channel attention block (GCA block) and gate time step attention (GTSA block) module was adopted in order to achieve multi-channel feature fusion. The bidirectional long-short time memory network and the convolutional neural network were used as the base feature extractor. The before-after dependence of the ECG signal sequence data and the local correlation features at different scales were captured respectively, and the automatic classification of five different types of ECG signals was realized. Results verified on the MIT-BIH dataset showed that the accuracy, specificity, sensitivity, accuracy and Macro-F1 of the total classification of five different ECG signals by the method were 99.50%, 99.61%, 96.20%, 98.02% and 97.08%, respectively. The model can not only effectively shorten the depth of the network model and prevent the model from overfitting, but also more accurately extract the spatiotemporal characteristics of the ECG signal and obtain better classification performance compared with other ECG classification models.