  Electrocardiogram(ECG) is commonly used to detect cardiac irregularities such
as atrial fibrillation, bradycardia, and other irregular complexes. While
previous studies have achieved great accomplishment classifying these
irregularities with standard 12-lead ECGs, there existed limited evidence
demonstrating the utility of reduced-lead ECGs in capturing a wide-range of
diagnostic information. In addition, classification model's generalizability
across multiple recording sources also remained uncovered. As part of the
PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC,
proposed Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac
abnormalities from reduced-lead ECG. Our classifiers received scores of 0.602,
0.593, 0.597, 0.591, and 0.589 (ranked 54th, 37th, 38th, 38th, and 39th) for
the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden
validation set with the evaluation metric defined by the challenge.
