During long-term electrocardiogram (ECG) monitoring, various types of noise inevitably become mixed with the signal, potentially hindering doctors' ability to accurately assess and interpret patient data. Therefore, evaluating the quality of ECG signals before conducting analysis and diagnosis is crucial. This paper addresses the limitations of existing ECG signal quality assessment methods, particularly their insufficient focus on the 12-lead multi-scale correlation. We propose a novel ECG signal quality assessment method that integrates a convolutional neural network (CNN) with a squeeze and excitation residual network (SE-ResNet). This approach not only captures both local and global features of ECG time series but also emphasizes the spatial correlation among ECG signals. Testing on a public dataset demonstrated that our method achieved an accuracy of 99.5%, sensitivity of 98.5%, and specificity of 99.6%. Compared with other methods, our technique significantly enhances the accuracy of ECG signal quality assessment by leveraging inter-lead correlation information, which is expected to advance the development of intelligent ECG monitoring and diagnostic technology.
在长期监测心电图（ECG）的过程中不可避免地会混杂各类噪声，影响医生对患者数据的读取和判断，因此在分析和诊断前对ECG信号质量进行评估至关重要。针对目前已有的ECG信号质量评估方法对12导联多尺度相关性关注不足的问题，本文提出一种集成卷积神经网络（CNN）和压缩与激励残差网络（SE-ResNet）的ECG信号质量评估方法。该方法不仅能提取ECG信号时间序列的局部及全局特征，而且还关注了ECG信号的空间相关性，在公共数据集上测试得到的准确率、灵敏度和特异性分别为99.5%、98.5%和99.6%。与其他方法相比，本文所提方法利用导联间相关信息有效提高了ECG信号质量评估的准确率，有望促进ECG信号的智能监测与诊断技术的发展。.
Keywords: 12-lead electrocardiogram; Feature fusion; Inter-lead information; Multi-scale features; Quality assessment.