Abstract:
In the directions of modern medicine, a new area of processing and analysis
of visual data is actively developing - a radio municipality - a computer
technology that allows you to deeply analyze medical images, such as
computed tomography (CT), magnetic resonance imaging (MRI), chest
radiography (CXR), electrocardiography and electrocardiography. This
approach allows us to extract quantitative texture signs from signals and
distinguish informative features to describe the heart's pathology, providing
a personified approach to diagnosis and treatment. Cardiovascular diseases
(SVD) are one of the main causes of death in the world, and early detection
is crucial for timely intervention and improvement of results. This
experiment aims to increase the accuracy of deep learning algorithms to
determine cardiovascular diseases. To achieve the goal, the methods of deep
learning were considered used to analyze cardiograms. To solve the tasks set
in the work, 50 patients were used who are classified by three indicators,
13 anomalous, 24 nonbeat, and 1 healthy parameter, which is taken from the
MIT-BIH Arrhythmia database.
