Journal
2020 COMPUTING IN CARDIOLOGY
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.22489/CinC.2020.107
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Cardiac abnormalities are a leading cause of death and their early diagnosis are of importance for providing timely interventions. The goal of 2020 PhysioNet/CinC challenge was to develop algorithms to diagnose multiple cardiac abnormalities using 12-lead ECG data. In this work, we develop a wide and deep transformer neural network to classify each 12-lead ECG sequence into 27 cardiac abnormality classes. Our approach combines hand-crafted ECG features, which were determined to be important by a random forest model, and discriminative feature representations that are automatically learned from a transformer neural network. Our entry to the 2020 PhysioNet/CinC challenge placed 1st out of 41 official ranking teams (team name = prna). Using the official generalized weighted accuracy metric for evaluation, we achieved a validation score of 0.587 and top score of 0.533 on the full held-out test set.
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