## 5 Discussion  
The article presents a system for neural network determination of atrial fibrillation on ECG signals with wavelet-based preprocessing. Simulation of the proposed system made it possible to achieve a recognition accuracy of 87.5%, which is significantly higher than the 79.0-82.0% level that can be achieved using known systems [33; 34; 35]. Work [33] is devoted to the development of a smartphone application for the detection of atrial fibrillation. The method proposed in [33] uses preliminary preprocessing of the ECG signal, which includes the analysis of the R-R and P-peak intervals. Despite the identity of the methods used, the authors of the work indicate the accuracy of the system at 79.0%, which is significantly lower than the result obtained by modeling the system proposed in this work. The authors of [34] used the training of a recurrent neural network without preliminary signal processing and achieved an accuracy of 82.0%. The recurrent network is the basis of the LSTM network used in our method, which makes it possible to make comparisons. In [35], the authors used a part of the MIT-BH 2017 database consisting of normal ECG signals, signals with fibrillation, and \"others.\" A group of noisy signals was not used. For the experiment, the authors used signals with a length of 4 heart counts (4 R-R intervals). The method proposed in [35] did not give a positive result for signals of the \"other\" category. The results shown for determining the presence of fibrillation from groups with normal signals and signals with fibrillations were 82.0% accuracy, which is also lower than the result of the accuracy of the proposed system for neural network determination of atrial fibrillation on ECG signals. There are other ways to measure atrial fibrillation. In [36], a more complex methodology is used that requires more computing power. Using multiple convolutional neural networks and LSTM networks is a resource-intensive method. At the same time, the use of pre-processing of the signal before training the neural network can reduce resource costs. The proposed stages of preliminary processing of ECG signals made it possible to prepare data for further analysis to conduct an automated determination of atrial fibrillation. The average accuracy of a cardiologist's diagnosis by visual analysis of ECG signals is 65.0-70.0% [37]. The use of the proposed neural network system for determining atrial fibrillation from ECG signals by specialists will make it possible to increase the efficiency of diagnostics in comparison with methods of visual diagnosis. The proposed system for neural network determination of atrial fibrillation on ECG signals can only be used as an additional diagnostic tool by specialists. This system is not a medical device and cannot independently diagnose patients. A promising direction for further research is the construction of more complex systems for the neural network classification of ECG signals, using, together with the analysis of signals, various metadata about patients, such as age, gender, race, genetic predisposition, and other descriptors. One of the next steps for further research is the creation of a mobile application for real-time fibrillation detection. Additionally, further research plans include the complication of the proposed system by using convolutional neural networks to improve the accuracy of determining atrial fibrillation. Conceptualization, P.L. ; methodology, P.L. ; software, M.K. ; validation, M.K. ; formal analysis, P.L. ; investigation, M.K. ; resources, U.L. ; data curation, P.L. ; writing--original draft preparation, M.K. ; writing--review and editing, U.L. ; visualization, M.K. ; supervision, U.L. ; project administration, P.L. ; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Russian Foundation for Basic Research (project no. 19-07-00130 A) and by the Presidential Council for grants (project no. MK-3918.2021.1.6). Not applicable.