Multilayer neural network architecture for ECG signal analysis

Currently, given the increasing number of cardiovascular diseases, the relevance of fast and accurate analysis of electrocardiograms (ECG) is growing significantly. Traditional methods of ECG analysis require significant time costs and high qualifications of specialists, which stimulates the development and implementation of automated systems capable of effectively interpreting ECG data. The article investigates the use of neural networks for ECG analysis, which allows significantly increasing the speed and accuracy of diagnosis. The development and analysis of the effectiveness of a neural network model capable of recognizing and classifying the main arrhythmias from ECG data is proposed. The article presents the results of comparing several neural network architectures, including convolutional and recurrent neural networks, on a set of ECG data with various pathologies. The analysis showed that the use of convolutional neural networks (CNN) demonstrates high accuracy in classifying ECG signals, which provides significant potential for clinical application. Fig.: 1. Bibliography: 9 titles.