  We develop a multi-task convolutional neural network (CNN) to classify
multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset
comprised of over 40,000 ECGs, with labels derived from cardiologist clinical
interpretations. Since many clinically important classes can occur in low
frequencies, approaches are needed to improve performance on rare classes. We
compare the performance of several single-class classifiers on rare classes to
a multi-headed classifier across all available classes. We demonstrate that the
addition of common classes can significantly improve CNN performance on rarer
classes when compared to a model trained on the rarer class in isolation. Using
this method, we develop a model with high performance as measured by F1 score
on multiple clinically relevant classes compared against the gold-standard
cardiologist interpretation.
