Detecting Cardiovascular Diseases by ECG Signal Using Machine-learning methods
Abstract
The problem of automatic diagnostic of diseases, in particular cardiovascular diseases by ECG signal (Electrocardiography). Goal. Improve diagnostic efficiency by using modern machine learning methods. Results. The results of the article review are presented in a systematic form, the most effective methods and approaches are highlighted, as well as their applicability to solving specific problems of diagnosing cardiovascular diseases. Based on the analysis of existing works, conclusions are made about the prospects of using machine learning methods in the field of diagnosis of cardiovascular diseases based on ECG data and suggest possible directions for future research in this field. Practical significance. The article highlights modern methods and approaches used in modern research aimed at automatic detection of cardiovascular diseases using ECG data.
About the Authors
A. D. Avramenko
Financial University under the Government of the Russian Federation
Russian Federation
Russian Federation
Alexander D. Avramenko - Postgraduate Student, Assistant, Department of Information Technology, Faculty of Information Technology and Big Data Analysis
Moscow
V. A. Sudakov
Financial University under the Government of the Russian Federation; The M.V. Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences
Russian Federation
Russian Federation
Vladimir A. Sudakov - Dr. Sci. (Tech.), Assoc. Prof., Leading Researcher
Moscow
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Review
For citations:
Avramenko A.D., Sudakov V.A. Detecting Cardiovascular Diseases by ECG Signal Using Machine-learning methods. Digital Solutions and Artificial Intelligence Technologies. 2025;1(2):26-31. (In Russ.)
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