  The complexity of the patterns associated with Atrial Fibrillation (AF) and
the high level of noise affecting these patterns have significantly limited the
current signal processing and shallow machine learning approaches to get
accurate AF detection results. Deep neural networks have shown to be very
powerful to learn the non-linear patterns in the data. While a deep learning
approach attempts to learn complex pattern related to the presence of AF in the
ECG, they can benefit from knowing which parts of the signal is more important
to focus during learning. In this paper, we introduce a two-channel deep neural
network to more accurately detect AF presented in the ECG signal. The first
channel takes in a preprocessed ECG signal and automatically learns where to
attend for detection of AF. The second channel simultaneously takes in the
preprocessed ECG signal to consider all features of entire signals. The model
shows via visualization that what parts of the given ECG signal are important
to attend while trying to detect atrial fibrillation. In addition, this
combination significantly improves the performance of the atrial fibrillation
detection (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy
of 99.40% on the MIT-BIH atrial fibrillation database with 5-s ECG segments.)

Quantitative Methods;Signal Processing;Medical Physics