Arrhythmia is a significant cardiovascular disease that poses a threat to human health, and its primary diagnosis relies on electrocardiogram (ECG). Implementing computer technology to achieve automatic classification of arrhythmia can effectively avoid human error, improve diagnostic efficiency, and reduce costs. However, most automatic arrhythmia classification algorithms focus on one-dimensional temporal signals, which lack robustness. Therefore, this study proposed an arrhythmia image classification method based on Gramian angular summation field (GASF) and an improved Inception-ResNet-v2 network. Firstly, the data was preprocessed using variational mode decomposition, and data augmentation was performed using a deep convolutional generative adversarial network. Then, GASF was used to transform one-dimensional ECG signals into two-dimensional images, and an improved Inception-ResNet-v2 network was utilized to implement the five arrhythmia classifications recommended by the AAMI (N, V, S, F, and Q). The experimental results on the MIT-BIH Arrhythmia Database showed that the proposed method achieved an overall classification accuracy of 99.52% and 95.48% under the intra-patient and inter-patient paradigms, respectively. The arrhythmia classification performance of the improved Inception-ResNet-v2 network in this study outperforms other methods, providing a new approach for deep learning-based automatic arrhythmia classification.
心律失常是一种常见的威胁人类健康的心血管疾病，其主要的确诊手段靠心电图（ECG）。采用计算机技术实现心律失常自动分类可有效避免人工误差，提高诊断效率并降低成本。心律失常自动分类算法大多集中于一维时序信号的处理，其鲁棒性不足。为此，本文提出一种基于格拉姆角和场（GASF）和改进的Inception-ResNet-v2的心律失常图像分类方法。首先使用变分模态分解进行去噪，用深度卷积生成对抗网络进行数据扩增，然后使用GASF将一维时序心电信号转换为二维图像，并使用改进的Inception-ResNet-v2网络实现AAMI推荐的五种（N、V、S、F和Q）心律失常分类。在MIT-BIH心律失常数据库测试实验表明：在患者内（intra-patient）和患者间（inter-patient）范式下分别获得了99.52%和95.48%的整体分类精度。本文改进的Inception-ResNet-v2网络的心律失常分类表现优于其他方法，为基于深度学习的心律失常自动分类提供了一种新途径。.
Keywords: Deep convolutional generative adversarial network; Gramian angular summation field; Image classification of arrhythmias; Inception-ResNet-v2.