The present invention relates to a method for predicting ventricular fibrillation by machine learning based on the QRS complex shape, and more specifically, to a method for predicting ventricular fibrillation by machine learning based on the QRS complex shape that features extracted from the QRS complex are used for early prediction of ventricular fibrillation by comparing the features extracted from the QRS complex with features of conventional heart rate variability and the prediction accuracy of the occurrence of ventricular fibrillation is remarkably improved using four features of the QRS complex and eleven features of the heart rate variability. The method for predicting ventricular fibrillation by using artificial neural network (ANN) learning based on the QRS complex shape according to the present invention comprises the following steps: selecting a database for a subject in order to perform learning by the ANN; determining an electrocardiogram (ECG) signal within the database; extracting data related to ventricular fibrillation prediction parameters from the ECG signal; and letting the ANN learn the extracted data, wherein the ventricular fibrillation prediction parameters are a plurality of features of the QRS complex.
QRS Prediction of Ventricular Fibrillation by Machine Learning Based on QRS Complex Shape Features