[Research on Clinical Electrocardiogram Classification Algorithm Based on Ensemble Learning].

With the increasing number of electrocardiogram(ECG)data,extensive application requirements of computer-aided ECG analysis have occurred.In the paper,we propose a variety of strategies to improve the performance of clinical ECG classification algorithm based on Lead Convolutional Neural Network(LCNN).Firstly,we obtained two classifiers by using different preprocessing methods and training methods in the study.Then,we applied the multiple output prediction method to both of them independently.Finally,the Bayesian approach was employed to fuse them.Tests conducted using more than 150 000 ECG records showed that the proposed method had an accuracy of 85.04% and the area under receiver operating characteristic curve(AUC)was 0.918 5,which significantly outperforms traditional methods based on feature extraction techniques.