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Purpose: Atrial fibrillation (AF), the most common among cardiac arrhythmias, is associated with significant morbidity and mortality. For its diagnosis, documentation of the electrocardiographic tracing is required. The use of eletrocardiogram has been established as a valuable noninvasive diagnostic tool, and the interpretation of electrocardiographic records using deep learning models has attracted significant attention in recent years. Relying on signal-to-image and transfer learning approaches, this study is aimed at the development of a deep neural network for classifying binary electrocardiographic records according to their rhythm, i.e., normal or AF.Methods: Electrocardiographic records labeled as normal (n = 917) or AF (n = 1,097) from the China Physiological Signal Challenge 2018 were collected and used to generate images, which were split into training and test sets and used as inputs to a dense convolutional neural network (DCNN). For the training, transfer learning with a fine tuning of all layers was applied. For a performance evaluation of the test set, the accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC) were used as metrics.Results: For the test set, the proposed model achieved an accuracy of 99.34%, sensitivity of 98.85%, specificity of 100.00%, F1-score, of 99.42%, and AUC of 0.99.Conclusion: To validate the methodology, as well as apply it to the multilabel classification of arrhythmia, it is important that further studies adopting this approach be conducted for the detection of AF in larger volumes of data.

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Automated atrial fibrillation recognition in 12-lead electrocardiographic records: a signal to image and transfer learning approach Case-control accuracy study