Algorithms for improving the detection of selected cardiac arrhythmias

The work deals with the generation of ECG arrhythmias that are underrepresented in databases. The theoretical part of the thesis is devoted to a literature search of academic publications that deal with the classification of arrhythmia by using deep learning and data augmentation metod for ECG. The practical part of the thesis deals with noise generator, because adding noise to signals could make the dataset richer. Functions for augmentation of atrial flutter and 3rd and 2nd atrioventricular block were created. It has been tried generation of 2nd atrioventricular block using generative adversarial networks (GAN). Deep learning-based ECG classifiers were used for evaluating the efficiency of the proposed technique in generating synthetic ECG data