  Atrial fibrillation (AF) is the most prevalent heart arrhythmia. AF manifests
on the electrocardiogram (ECG) though irregular beat-to-beat time interval
variation, the absence of P-wave and the presence of fibrillatory waves
(f-wave). We hypothesize that a deep learning (DL) approach trained on the raw
ECG will enable robust detection of AF events and the estimation of the AF
burden (AFB). We further hypothesize that the performance reached leveraging
the raw ECG will be superior to previously developed methods using the
beat-to-beat interval variation time series. Consequently, we develop a new DL
algorithm, denoted ArNet-ECG, to robustly detect AF events and estimate the AFB
from the raw ECG and benchmark this algorithms against previous work. Methods:
A dataset including 2,247 adult patients and totaling over 53,753 hours of
continuous ECG from the University of Virginia (UVAF) was used. Results:
ArNet-ECG obtained an F1 of 0.96 and ArNet2 obtained an F1 0.94. Discussion and
conclusion: ArNet-ECG outperformed ArNet2 thus demonstrating that using the raw
ECG provides added performance over the beat-to-beat interval time series. The
main reason found for explaining the higher performance of ArNet-ECG was its
high performance on atrial flutter examples versus poor performance on these
recordings for ArNet2.
