  Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet
powerful way to diagnose heart disease. ECG studies using Machine Learning to
automatically detect abnormal heartbeats so far depend on large, manually
annotated datasets. While collecting vast amounts of unlabeled data can be
straightforward, the point-by-point annotation of abnormal heartbeats is
tedious and expensive. We explore the use of multiple weak supervision sources
to learn diagnostic models of abnormal heartbeats via human designed
heuristics, without using ground truth labels on individual data points. Our
work is among the first to define weak supervision sources directly on time
series data. Results show that with as few as six intuitive time series
heuristics, we are able to infer high quality probabilistic label estimates for
over 100,000 heartbeats with little human effort, and use the estimated labels
to train competitive classifiers evaluated on held out test data.
