Disclosed is a method for predicting paroxysmal atrial fibrillation in a normal sinus rhythm electrocardiogram state by using deep learning. A method for predicting paroxysmal atrial fibrillation according to an embodiment may include: a step for performing pre-processing in which electrical biosignals are converted into input data based on a diagnostic model; a step for training a diagnostic model, configured for the prediction of paroxysmal atrial fibrillation, with the pre-processed input data; and a step for evaluating the potential probability of paroxysmal atrial fibrillation in cases that include a group potentially classified as PAF (PAF normal cases) and a real normal group (Real normal cases), by using the training results learned through the diagnostic model.
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