  Electrocardiography analysis is widely used in various clinical applications
and Deep Learning models for classification tasks are currently in the focus of
research. Due to their data-driven character, they bear the potential to handle
signal noise efficiently, but its influence on the accuracy of these methods is
still unclear. Therefore, we benchmark the influence of four types of noise on
the accuracy of a Deep Learning-based method for atrial fibrillation detection
in 12-lead electrocardiograms. We use a subset of a publicly available dataset
(PTBXL) and use the metadata provided by human experts regarding noise for
assigning a signal quality to each electrocardiogram. Furthermore, we compute a
quantitative signal-to-noise ratio for each electrocardiogram. We analyze the
accuracy of the Deep Learning model with respect to both metrics and observe
that the method can robustly identify atrial fibrillation, even in cases
signals are labelled by human experts as being noisy on multiple leads. False
positive and false negative rates are slightly worse for data being labelled as
noisy. Interestingly, data annotated as showing baseline drift noise results in
an accuracy very similar to data without. We conclude that the issue of
processing noisy electrocardiography data can be addressed successfully by Deep
Learning methods that might not need preprocessing as many conventional methods
do.

Signal Processing;Machine Learning