具有自动抗噪功能的心电信号分类算法  

ECG classification algorithm with automatic anti noise function

作　　者：雷宇 刘少儒 徐寅林[1] Lei Yu;Liu Shaoru;Xu Yinlin(School of Computer and Electronic Information/School of Artificial Intelligence,Nanjing Normal University,Nanjing210023,China)

机构地区：[1]南京师范大学计算机与电子信息学院/人工智能学院,南京210023

出　　处：《电子测量技术》2021年第21期49-55,共7页Electronic Measurement Technology

摘　　要：心电图(ECG)检测是心脏疾病最常用的诊断方法。但是在心电信号采集过程中往往会受到噪声干扰,从而使心电信号分类诊断的正确率受到很大影响。为提高分类诊断的准确率和抗噪能力,改进设计了一种用深度残差收缩网络(DRSN)实现自动抗噪、全局平均池化(GAP)整合空间信息的ECG分类诊断模型。在MIT-BIH心律失常数据集上验证了模型的分类性能,并将其与普通的卷积神经网络(CNN)模型进行了抗噪性能分析比较。实验结果表明,设计的DRSN+GAP诊断模型基于AAMI标准的分类正确率高达99.3%,对不同强度的工频及高斯两种噪声其抗噪性能均优于普通的CNN模型。Electrocardiogram(ECG) detection is the most commonly used diagnostic method of heart disease.However,in the process of ECG signal acquisition,it is often disturbed by noise,which greatly affects the accuracy of ECG signal classification and diagnosis.In order to improve the accuracy and anti noise ability of classification diagnosis,this paper improves and designs an ECG classification and diagnosis model which use deep residual shrinkage network(DRSN) to resist noise automatically and integrate spatial information by global average pooling(GAP).The classification performance of the model is verified on MIT-BIH arrhythmia data set,and its anti noise performance is analyzed and compared with the ordinary convolutional neural network(CNN) model.The experimental results show that the classification accuracy of the designed DRSN+GAP diagnostic model based on AAMI standard is up to 99.3%,and its anti noise performance is better than ordinary CNN model for power frequency and Gaussian noise with different intensity.

关 键 词：ECG 深度学习 自动抗噪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]