An electrocardiogram (ECG) is one of the most common medical examinations. High-quality interpretation of a 12-channel electrocardiogram is important for subsequent diagnosis and treatment. One of the important steps in deciphering an ECG is to determine the boundaries of the elements of the PQRST complex. The article discusses mathematical methods for determining the boundaries of the P, T waves and the QRS complex, as well as the R, P and T peaks, presents the shortcomings of mathematical methods for determining the elements of the PQRST complex. And also the values ​​of the metrics obtained as a result of training the neural network segmentation model of the PQRST-complex are given. The experiments performed show the relevance of using neural network and combined approaches to the analysis of the PQRST complex.

Recording and analyzing 12-lead electrocardiograms is the most common procedure for detecting heart disease. Recently, various deep learning methods have been proposed for the automatic diagnosis by an electrocardiogram. The proposed methods can provide a second opinion for the doctor and help detect pathologies at an early stage. Various methods are proposed in the paper to improve the quality of prediction of ECG recording pathologies. Techniques include adding patient metadata, ECG noise reduction, and self-adaptive learning. The significance of data parameters in training a classification model is also explored. Among the considered parameters, the influence of various ECG leads, the length of the electrocardiogram and the volume of the training sample is studied. The experiments carried out show the relevance of the described approaches and offer an optimal estimate of the input data parameters.