The Adaptive Lead Weighted ResNet model achieved a 5-fold cross-validation weighted accuracy score of 0.684 and a hidden test score of 0.520 for classifying 24 cardiac abnormalities from 12-lead ECGs.
An adaptive lead-weighted ResNet model demonstrated strong performance in classifying 24 cardiac abnormalities from 12-lead ECGs, though generalizability to completely unseen datasets remains a challenge.
Introduction: We describe the creation of a deep neural network architecture to classify cardiac abnormality from 12 lead ECGs. The model was created by the team "between a ROC and a heart place" for the Phys-ioNet/Computing in Cardiology Challenge 2020.
Zhao et al. (Wed,) conducted a other in Cardiac abnormalities (n=37,749). Adaptive Lead Weighted ResNet was evaluated on snormalized (weighted accuracy metric) on 5-fold cross-validation. The Adaptive Lead Weighted ResNet model achieved a 5-fold cross-validation weighted accuracy score of 0.684 and a hidden test score of 0.520 for classifying 24 cardiac abnormalities from 12-lead ECGs.