Research on single-lead ECG beat classification based on semantic segmentation
In order to accurately identify the beats from ECG signals,we proposed an improved 1D U-Net semantic segmentation model fusing residual connection and attention mechanism.148 340 single-lead ECG datas intercepted from remote dynamic ECG re-cords of tens of thousands of patients were used to classify five common beat types:normal sinus beats(Normal),premature ventricu-lar contractions(PVC),atrial premature beat(APB),left bundle branch block(LBBB)and right bundle branch block(RBBB).The model took a certain length of ECG segments as input,and completed semantic segmentation of all sampling points by adding back-ground labels,meanwhile completed type recognition while positioned each beat.The experimental results on the test set showed that the model could accurately detect the position of each beat,and only 0.04%of beats were missed,and the F1 scores of Normal,PVC,APB,LBBB and RBBB were 99.44%,99.03%,97.63%,95.25%and 94.77%,respectively.Compared with the traditional U-Net model,the proposed model achieves better results of beat classification.
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