  Machine learning (ML) applied to routine patient monitoring within intensive
care units (ICUs) has the potential to improve care by providing clinicians
with novel insights into each patient's health and expected response to
interventions. This paper applies deep learning to a large volume of unlabeled
electrocardiogram (ECG) telemetry signals, which are commonly used for
continuous patient monitoring in hospitals but have important differences from
the standard, single time-point 12-lead ECG used in many prior machine learning
studies. We applied self-supervised learning to pretrain a spectrum of deep
networks on approximately 147,000 hours of ECG telemetry data. Our approach
leverages this dataset to train models that significantly improve performance
on four distinct downstream tasks compared with direct supervised learning
using labeled data. These pretrained models enable medically useful predictions
and estimates in smaller patient cohorts that are typically limited by the
scarcity of labels. Notably, we demonstrate that our pretrained networks can
continuously annotate ECG telemetry signals, thereby providing monitoring
capabilities that are often unavailable due to the requirement for specialized
expertise and time-consuming professional annotations.
