基于互信息的多导联心电图排序方法

Sorting Method of Multi Leads ECG Based on Mutual Information

基于卷积神经网络的心电图(Electrocardiograph,ECG)自动分类研究从默认12导联顺序的心电图中提取特征,未考虑导联顺序对卷积网络特征提取的影响。为解决该问题,文中提出了一种基于互信息的两端递增排序方法,使用互信息衡量导联之间的相关性,并根据导联之间的相关性以及二维卷积的特点将关系密切的导联相邻排序。实验结果表明,多导联心电图排序方法在3个数据库和3个卷积网络分类模型上取得了显著效果,F1、正确率、召回率、精确率以及杰卡德系数数值分别提升了0.011、0.009、0.007、0.014和0.013,汉明损失值减低了0.002。

The studies of automatic Electrocardiograph(ECG)classification based on convolutional neural networks all extract features from the ECG with the default 12-lead sequence,ignore the influence of lead sequence on feature extraction of convolutional network.To solve the problem,this study proposes a 2-end increasing sorting method based on mutual information,which uses mutual information to measure the correlation between leads.According to the correlation between leads and the characteristics of two-dimensional convolution,the adjacent connections of closely related leads are sorted.The experimental results show that the multi-lead ECG sorting method has achieved remarkable results on three databases and three convolutional network classification models.F1,accuracy,recall,accuracy,and Jacquard′s coefficient of the proposed method increases by 0.011,0.009,0.007,0.014,and 0.013,while Hamming′s loss decreases by 0.002.