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Due to the data shortage problem, which is one of the major problems in thefield of machine learning, the accuracy level of many applications remains wellbelow the expected. It prevents researchers from producing new artificialintelligence-based systems with the available data. This problem can be solvedby generating new synthetic data with augmentation methods. In this study, newECG signals are produced using MIT-BIH Arrhythmia Database by using GenerativeAdversarial Neural Networks (GAN), which is a modern data augmentation method.These generated data are used for training a machine learning system and realECG data for testing it. The obtained results show that this way theperformance of the machine learning system is increased.

Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset