PVC Recognition Algorithm Based on Ensemble Learning
ZHOU Fei-yan1,2, JIN Lin-peng1,2, DONG Jun1
1. Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences, Suzhou, Jiangsu 215123, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China
In order to improve the recognition performance of premature ventricular contraction (PVC),this paper reports an algorithm based on ensemble learning.First,the tow-lead ECG signals from the MIT-BIH Arrhythmia database are classified into PVC and non PVC beats using lead convolutional neural network (LCNN) classifier.Then the results are fused with some rules.The accuracy,sensitivity and specificity of the proposed algorithm are 99.91%,98.76% and 99.97%,respectively,which are better than that of other existing algorithms for PVC beats classification.In addition,this paper realizes an inter-patient PVC recognition experiment by combining LCNN and diagnostic rules for clinical application.The effectiveness of the proposed algorithm has been confirmed by the accuracy (97.87%),sensitivity (87.94%) and specificity (98.02%) with the data set over 140000 ECG records.
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2017, Vol. 45 
