融合多尺度时序特征的心电信号质量评估新方法  

A novel approach for assessing quality of electrocardiogram signal by integrating multiscale temporal features

作　　者：陈诚 张爱华[1,2,3] 马玉润 漆宇晟 李佳琪 CHEN Cheng;ZHANG Aihua;MA Yurun;QI Yusheng;LI Jiaqi(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,P.R.China;Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou University of Technology,Lanzhou 730050,P.R.China;National Demonstration Center for Experimental Electrical and Control Engineering Education,Lanzhou University of Technology,Lanzhou 730050,P.R.China)

机构地区：[1]兰州理工大学电气工程与信息工程学院,兰州730050 [2]兰州理工大学甘肃省工业过程先进控制重点实验室,兰州730050 [3]兰州理工大学电气与控制工程国家级实验教学示范中心,兰州730050

出　　处：《生物医学工程学杂志》2024年第6期1169-1176,共8页Journal of Biomedical Engineering

基　　金：国家自然科学基金(81360229);甘肃省青年科技基金(24JRRA969);甘肃省自然科学基金(20JR5RA459);甘肃省工业过程先进控制重点实验室开放基金项目(2022KX11)。

摘　　要：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。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 multiscale 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.

关 键 词：12导联心电图 导联间信息 多尺度特征 特征融合 质量评估 

分 类 号：TN911.7[电子电信—通信与信息系统] R318[电子电信—信息与通信工程]