Convolution-Free Waveform Transformers for Multi-Lead ECG Classification

Abstract

We present our entry to the 2021 PhysioNet/CinC challenge - a waveform transformer model to detect cardiac abnormalities from ECG recordings. We compare the performance of the waveform transformer model on different ECG-lead subsets using approximately 88,000 ECG recordings from six datasets. In the official rankings, team prna ranked between 9 and 15 on 12,6,4,3 and 2-lead sets respectively. Our waveform transformer model achieved scores of 0.49, 0.49, 0.46, 0.47 and 0.44 on different ECG-lead subsets, with an average score of 0.47 on the held-out test set. Our combined performance across all leads placed us at rank 11 out of 39 officially ranking teams.