基于卷积神经网络的心电图心律失常分类方法  

Classification of ECG arrhythmia based on convolutional neural network

作　　者：艾文书 赵兴群[1] Ai Wenshu;Zhao Xinqun(College of Biological Science and Medical Engineering,Southeast University,Nanjing 210000,China)

机构地区：[1]东南大学生物科学与医学工程学院,南京210000

出　　处：《国际生物医学工程杂志》2021年第2期119-123,138,共6页International Journal of Biomedical Engineering

摘　　要：目的提升心电图心律失常分类算法的性能,为临床心电诊断提供辅助依据。方法将一维心电图数据按照R点进行切分,将切分后的数据生成2D图像。利用数据增强技术将样本进行扩增,再利用二维卷积神经网络(2D-CNN)中的2D卷积层、2D最大池化层、Flatten层和全连接层,对图像特征进行提取,并通过Softmax分类器进行分类。利用带有权重系数的损失函数来增强模型对于S类和V类的学习。采用MIT-BIH数据集进行模型训练并评估算法性能。结果样本扩增和使用带有权重系数的损失函数能够提升模型的召回率和特异性指标,同时保持模型对室性异位搏动(VEB)和室上性异位搏动(SVEB)分类的精确率的指标。结论所提出模型的准确率为99.02%,SVEB的召回率为96.4%,表明该分类方法可以辅助医护人员诊断心脏疾病。Objective To improve the performance of ECG arrhythmia classification algorithm and provide auxiliary basis for clinical ECG diagnosis.Methods The one-dimensional ECG data was segmented according to the R point,and the segmented data was generated into a 2D image.The samples were expanded by data augmentation technology,and the image features were extracted by the 2D convolutional layer,2D maximum pooling layer,Flatten layer and fully connected layer in 2D-CNN.Then,the samples were classified with Softmax classifier.The loss function with weight coefficients was used to enhance the model's learning of class S and class V.The MIT-BIH data set was used for model training and algorithm performance evaluation.Results Sample expansion and the use of loss functions with weight coefficients can improve the recall rate and specificity index of the model,while maintaining the model's accuracy index of the classificatio on VEB and SVEB.Conclusions The accuracy of the proposed model is 99.02%,and the recall rate of SVEB is 96.4%,indicating that this classification method can assist medical staff in diagnosing heart diseases.

关 键 词：心律失常 数据增强 卷积神经网络 分类性能 

分 类 号：R540.41[医药卫生—心血管疾病] R541.7[医药卫生—内科学]