ABSTRACT
An electrocardiography system records electrical activities of the heart, and it is used to assist doctors in the diagnosis of cardiac arrhythmia such as atrial fibrillation. This study presents a fast, automated deep-learning algorithm that predicts atrial fibrillation with excellent performance (F-1 score 88.2% and accuracy 97.3%). Our approach involves the pre-processing of ECG signals, followed by an alternative representation of the signals using a spectrogram, which is then fed to a fine-tuned EfficientNet B0, a pre-trained convolution neural network model, for the classification task. Using the transfer learning approach and with fine-tuning of the EfficientNet, we optimize the model to achieve highly efficient and effective classification of the atrial fibrillation.
