Zeng Yuni, Lv Hang, Jiang Mingfeng, Zhang Jucheng, Xia Ling, Wang Yaming, Wang Zhikang
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310019, China.
Math Biosci Eng. 2023 Jan;20(1):1-17. doi: 10.3934/mbe.2023001. Epub 2022 Sep 29.
Arrhythmia is one of the common cardiovascular diseases. Nowadays, many methods identify arrhythmias from electrocardiograms (ECGs) by computer-aided systems. However, computer-aided systems could not identify arrhythmias effectively due to various the morphological change of abnormal ECG data. This paper proposes a deep method to classify ECG samples. Firstly, ECG features are extracted through continuous wavelet transform. Then, our method realizes the arrhythmia classification based on the new lightweight context transform blocks. The block is proposed by improving the linear content transform block by squeeze-and-excitation network and linear transformation. Finally, the proposed method is validated on the MIT-BIH arrhythmia database. The experimental results show that the proposed method can achieve a high accuracy on arrhythmia classification.
心律失常是常见的心血管疾病之一。如今，许多方法通过计算机辅助系统从心电图（ECG）中识别心律失常。然而，由于异常ECG数据的各种形态变化，计算机辅助系统无法有效地识别心律失常。本文提出了一种用于对ECG样本进行分类的深度方法。首先，通过连续小波变换提取ECG特征。然后，我们的方法基于新的轻量级上下文变换块实现心律失常分类。该块是通过挤压激励网络和线性变换改进线性内容变换块而提出的。最后，在MIT-BIH心律失常数据库上对所提出的方法进行了验证。实验结果表明，所提出的方法在心律失常分类上能够达到较高的准确率。
