  Mobile electrocardiogram (ECG) recording technologies represent a promising
tool to fight the ongoing epidemic of cardiovascular diseases, which are
responsible for more deaths globally than any other cause. While the ability to
monitor one's heart activity at any time in any place is a crucial advantage of
such technologies, it is also the cause of a drawback: signal noise due to
environmental factors can render the ECGs illegible. In this work, we develop
convolutional neural networks (CNNs) to automatically label ECGs for noise,
training them on a novel noise-annotated dataset. By reducing distraction from
noisy intervals of signals, such networks have the potential to increase the
accuracy of models for the detection of atrial fibrillation, long QT syndrome,
and other cardiovascular conditions. Comparing several architectures, we find
that a 16-layer CNN adapted from the VGG16 network which generates one
prediction per second on a 10-second input performs exceptionally well on this
task, with an AUC of 0.977.
