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.
METHOD FOR PREDICTING PAROXYSMAL ATRIAL FIBRILLATION IN NORMAL SINUS RHYTHM ELECTROCARDIOGRAM STATE BY USING DEEP LEARNING