典型文献
Research on ECG classification based on transfer learning
文献摘要：
Concerning current deep learning-based electrocardiograph(ECG)classification methods,there exists domain discrepancy between the data distributions of the training set and the test set in the inter-patient paradigm.To reduce the negative effect of domain discrepancy on the classification accuracy of ECG signals,this paper incorporates transfer learning into the ECG classification,which aims at applying the knowledge learned from the training set to the test set.Specifically,this paper first develops a deep domain adaptation network(DAN)for ECG classification based on the convolutional neural network(CNN).Then,the network is pre-trained with training set data obtained from the famous Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)ECG arrhythmia database.On this basis,by minimizing the multi-kernel maximum mean discrepancy(MK-MMD)between the data distributions of the training set and the test set,the pre-trained network is adjusted to learn transferable feature representations.Finally,with the low-density separation of unlabeled target data,the feature representations are more transferable.The extensive experimental results show that the proposed domain adaptation method has reached a 7.58％improvement in overall classification accuracy on the test set,and achieves competitive performance with other state-of-the-arts.
文献关键词：
中图分类号：
作者姓名：
Jiang Fan;Chen Jiajun;Gao Youjun;Sun Changyin
作者机构：
Shaanxi Key Laboratory of Information Communication Network and Security,Xi'an University of Posts and Telecommunications,Xi'an 710121,China;China Mobile System Integration Co.,Ltd.,Beijing 100052,China;China Mobile Xiong'an Information Communication Technology Co.,Ltd.,Beijing 100052,China
文献出处：
引用格式：
[1]Jiang Fan;Chen Jiajun;Gao Youjun;Sun Changyin-.Research on ECG classification based on transfer learning)[J].中国邮电高校学报（英文版）,2022(06):83-96
A类：
electrocardiograph,Beth
B类：
Research,ECG,classification,learning,Concerning,current,deep,methods,there,exists,domain,discrepancy,between,distributions,training,test,inter,patient,paradigm,To,reduce,negative,effect,accuracy,signals,this,paper,incorporates,into,which,aims,applying,knowledge,learned,from,Specifically,first,develops,adaptation,network,DAN,convolutional,neural,Then,trained,obtained,famous,Massachusetts,Institute,Technology,Israel,Hospital,MIT,BIH,arrhythmia,database,On,basis,by,minimizing,multi,kernel,maximum,mean,MK,MMD,adjusted,transferable,feature,representations,Finally,low,density,separation,unlabeled,target,are,more,extensive,experimental,results,show,that,proposed,has,reached,improvement,overall,achieves,competitive,performance,other,state,arts
AB值：
0.532288
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