Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter.

ABSTRACT: Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia. In this study, an automatic system combining denoising and segmentation modules was developed to detect the deviation of the ST-segment and J point. We proposed a ECG Bidirectional Transformer network that applied in both denoising and segmentation tasks. The denoising model achieved RMSE_de, SNR_imp, and PRD values of 0.074, 10.006,

SUBMITTER: Wu S 

PROVIDER: S-EPMC9277481 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature