Isolation of ECG Sections Associated with Signs of Cardiovascular Diseases Using the Transformer Architecture

The problem of explainability of artificial intelligence is extremely relevant at the present time. The most acute need for this tool is felt in the areas of practical application of intelligent systems. One of these areas is medicine. Extracting knowledge and understanding the reason for the model’s prediction will allow us to provide in a human-readable form the main factors used by the model to predict. The article will consider a method for highlighting areas of the electrocardiogram associated with signs of cardiovascular diseases using the transformer machine learning architecture. Visualization of the attention of the transformer to the ECG signal allows you to highlight the segments of the electrocardiogram, which may show signs of various cardiovascular diseases. For the diagnosis of inferior myocardial infarction, the algorithm for labeling the data extracted from the trained model identified Q and R waves, for anterior septal myocardial infarction, Q and T waves, as well as the ST segment, and for left ventricular hypertrophy, the ST segment. Areas of attraction of “attention” of the neural network are shown graphically and displayed in the article.