Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
This work aims to improve the early and accurate diagnosis of cardiovascular diseases for patients, potentially preventing severe complications.
The paper addresses the challenge of automatically identifying 27 distinct ECG abnormalities from 12-lead ECG recordings. The authors developed an ensembled Se-ResNet framework incorporating a Sign Loss function to achieve this.
Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG recordings.