Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

ABSTRACT: Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model-based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the

SUBMITTER: Liu Y 

PROVIDER: S-EPMC8615597 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature