Study on ECG Signal Recognition and Classification Based on U-Net++

Abstract

It is difficult to explore efficient,fast and accurate ECG signal recognition and classification algorithm.The classification of ECG fragments is more suitable for clinical application.Based on this,the improved generation countermeasure network (DCGAN) is used for data expansion,and the optimized one-dimensional U-Net++ is used for fragment signal recognition of arrhythmia.ECG fragments from 1200 sampling points in MIT-BIH database are continuously intercepted as the experimental data set,and the type that appears the most times of beats in each fragment recording center is used as the label of the whole record.Then the DCGAN,which uses optimized one-dimensional U-Net++ as generator,is used to realize partial data expansion to solve the problem of data imbalance.Under the condition that the original ECG signals are not preprocessed and the generated extended data are used to complete the wavelet threshold denoising,the accuracy of the optimized one-dimensional U-Net++ model for normal,ventricular premature beat,left bundle branch block,right bundle branch block four kind of different type can reach 98.10% for the training sets.The precision ratio,recall ratio and F1 score of the test set have good results.Under the same experimental data set,the accuracy of U-Net++ model is 1.05% higher than that of U-Net model.Under the same experimental parameters,compared with under sampling data,the accuracy of the experimental model of the data set expanded by DCGAN is improved by 0.85%.

Keywords

• ecg|recognition and classification|dcgan|u-net++|mit-bih