College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China.
Comput Intell Neurosci. 2022 Aug 27;2022:8683855. doi: 10.1155/2022/8683855. eCollection 2022.
The classification and identification of arrhythmias using electrocardiogram (ECG) signals are of great practical significance in the early prevention and diagnosis of cardiovascular diseases. In this study, we propose an arrhythmia classification algorithm based on two-dimensional (2D) images and modified EfficientNet. First, we developed a method for converting original one-dimensional (1D) ECG signals into 2D image signals. In contrast with the existing classification method that uses only the time-domain features of a 1D ECG signal, the classification of 2D images can consider the spatiotemporal characteristics of the signal. Then, to better assign feature weights, we introduced an attention feature fusion module (AFF) into the EfficientNet network to replace the addition operation in the mobile inverted bottleneck convolution (MBConv) structure of the network. We selected EfficientNet for modification because, compared with most convolutional neural networks (CNNs), EfficientNet does not require manual adjustment of parameters, which improves the accuracy and speed of the network. Finally, we combined the 2D images and the improved EfficientNet network and tested its performance as an arrhythmia classification method. Our experimental results show that the network training of the proposed method requires less equipment and training time, and this method can effectively distinguish eight types of heartbeats in the MIT-BIH arrhythmia database, with a classification accuracy of 99.54%. Thus, the model has a good classification effect.
使用心电图（ECG）信号对心律失常进行分类和识别，对于心血管疾病的早期预防和诊断具有重要的实际意义。在这项研究中，我们提出了一种基于二维（2D）图像和改进的 EfficientNet 的心律失常分类算法。首先，我们开发了一种将原始一维（1D）ECG 信号转换为 2D 图像信号的方法。与仅使用 1D ECG 信号的时域特征的现有分类方法相比，2D 图像的分类可以考虑信号的时空特征。然后，为了更好地分配特征权重，我们在 EfficientNet 网络中引入了注意力特征融合模块（AFF），以替换网络中移动反卷积瓶颈卷积（MBConv）结构中的加法操作。我们选择修改 EfficientNet，是因为与大多数卷积神经网络（CNN）相比，EfficientNet 不需要手动调整参数，从而提高了网络的准确性和速度。最后，我们结合 2D 图像和改进的 EfficientNet 网络，测试了其作为心律失常分类方法的性能。我们的实验结果表明，所提出方法的网络训练所需的设备和训练时间更少，并且该方法可以有效地区分 MIT-BIH 心律失常数据库中的八种心跳类型，分类准确率为 99.54%。因此，该模型具有良好的分类效果。
