## 2 Related Research  
Today, medicine is considered one of the promising areas for the introduction of artificial intelligence, because the analysis of medical signals is the most common research method in this area. An example is an algorithm for identifying patients with atrial fibrillation in sinus rhythm based on convolutional neural networks (CNN) in [10]. The work [11] presents a hybrid approach with the use of Empirical Mode Decomposition and CNN to classify ECG signals. In [12], the author has implemented the extraction of ECG signal features based on wavelet transform with further classification using Long Short-Term Memory (LSTM). The work [13] presents a method for detecting atrial fibrillation  
Figure 1: Example of an electrocardiogram signal. using LSTM. The method achieved an accuracy of 98.51%. The essence of the approach proposed in the work is the separation of ECG signals with a sliding window and the loading of the obtained blocks into the decision-making system. ECG signals were taken from the MIT-BIH database Atrial Fibrillation Database. Dataset was 10 h long and each signal contained 100 heartbeats (R-R peak). The authors point out the slow learning speed and high requirements for computing resources. For the simulation, the authors used the Quadro M5000 GPU (Nvidia, Santa Clara, CA, USA). This work is of scientific interest, however, due to the different methodology and resources used, it cannot be compared with the proposed system for neural network determination of atrial fibrillation on ECG signals with wavelet-based preprocessing. Finding peaks in signals is an important step in many signal processing applications. Automatic peak detection using neural network classification systems is difficult due to the physiological variability of P waves and QRS complexes, as well as the presence of various types of noise, including muscle noise, artifacts due to electrode movement, power-line noise, and baseline deviations. Software QRS complex recognition is an integral part of modern computerized ECG monitoring systems. An algorithm for their recognition is presented in [14] and is based on optimized filtering and simple threshold setting since optimized filtering is considered a factor in achieving good timing accuracy. The most widely known method for detecting a single R-peak is the Pan-Tomkins method, which uses three types of processing steps: linear digital filtering, nonlinear transformation, and decision rule algorithms [15]. The work [16] presents an algorithm for detecting QRS using multi-stage morphological filtering to suppress impulse noise. In work [17], to determine the QRS complex, it is proposed to create an estimated QRS signal using the parameters extracted from the original ECG signal. For automatic disease diagnosis systems based on ECG signals, accurate determination of the P wave is critical. In work [18], one of the first methods of processing ECG signals to isolate the P-peak in its flow using wavelet transform and subsequent training of the neural network is described. In [3], a system for determining the P and T waves based on the wavelet transform is presented. The main methods of processing ECG signals for noise reduction are digital filtering, adaptive filtering, wavelet filtering. The paper [19] describes a method for processing ECG signals using an adaptive wavelet transform based on the Poincare section and the Shannon method. There are also methods for processing signals online in real-time for use in pacemakers. The paper [20] describes a processing method using biorthogonal wavelet transform based on a linear phase structure for noise removal, feature extraction, and compression of the ECG signal. There are also methods for computerized detection of fibrillation. Work [21] describes a method for detecting fibrillation using an eight-layer neural convolutional network, which requires only basic data normalization without preliminary processing and extraction of features from raw ECG samples.