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

ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention